pacemaker-fit-tutorial.ipynb 608 KB
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron import Project"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
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   "source": [
    "data_pr = Project(\"../../datasets\")\n",
    "if len(data_pr.job_table()) == 0:\n",
    "    data_pr.unpack(\"Cu_training_archive\")"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>286</td>\n",
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       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>df1_A1_A2_A3_EV_elast_phon</td>\n",
       "      <td>/df1_A1_A2_A3_EV_elast_phon</td>\n",
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       "      <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
       "      <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
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       "      <td>2021-02-08 10:33:52.341472</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>zora@cmti001#1</td>\n",
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       "      <td>TrainingContainer</td>\n",
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       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>287</td>\n",
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       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>df3_10k</td>\n",
       "      <td>/df3_10k</td>\n",
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       "      <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
       "      <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
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       "      <td>2021-02-08 10:33:53.993230</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>zora@cmti001#1</td>\n",
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       "      <td>TrainingContainer</td>\n",
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       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>288</td>\n",
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       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>df2_1k</td>\n",
       "      <td>/df2_1k</td>\n",
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       "      <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
       "      <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
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       "      <td>2021-02-08 10:33:54.435308</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>zora@cmti001#1</td>\n",
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       "      <td>TrainingContainer</td>\n",
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       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
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       "    id    status chemicalformula                         job  \\\n",
       "0  286  finished            None  df1_A1_A2_A3_EV_elast_phon   \n",
       "1  287  finished            None                     df3_10k   \n",
       "2  288  finished            None                      df2_1k   \n",
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       "\n",
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       "                        subjob                              projectpath  \\\n",
       "0  /df1_A1_A2_A3_EV_elast_phon  /home/yury/PycharmProjects/pyiron-2021/   \n",
       "1                     /df3_10k  /home/yury/PycharmProjects/pyiron-2021/   \n",
       "2                      /df2_1k  /home/yury/PycharmProjects/pyiron-2021/   \n",
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       "\n",
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       "                                                       project  \\\n",
       "0  pyiron_potentialfit/datasets/imported_datasets/Cu_database/   \n",
       "1  pyiron_potentialfit/datasets/imported_datasets/Cu_database/   \n",
       "2  pyiron_potentialfit/datasets/imported_datasets/Cu_database/   \n",
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       "\n",
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       "                   timestart timestop totalcputime        computer  \\\n",
       "0 2021-02-08 10:33:52.341472     None         None  zora@cmti001#1   \n",
       "1 2021-02-08 10:33:53.993230     None         None  zora@cmti001#1   \n",
       "2 2021-02-08 10:33:54.435308     None         None  zora@cmti001#1   \n",
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       "\n",
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       "            hamilton hamversion parentid masterid  \n",
       "0  TrainingContainer        0.4     None     None  \n",
       "1  TrainingContainer        0.4     None     None  \n",
       "2  TrainingContainer        0.4     None     None  "
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      ]
     },
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     "execution_count": 5,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pr.job_table()"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use smalles dataset for fitting, as real dataset took much more time, and go outside of the scope of the workshop"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {},
   "outputs": [],
   "source": [
    "data_job = data_pr.load('df1_A1_A2_A3_EV_elast_phon')"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>atoms</th>\n",
       "      <th>energy</th>\n",
       "      <th>forces</th>\n",
       "      <th>number_of_atoms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1:FHI-aims/PBE/tight:elastic:s_e_0</td>\n",
       "      <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>-3.699843</td>\n",
       "      <td>[[0.0, 0.0, 0.0]]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1:FHI-aims/PBE/tight:murnaghan:strain_1_0</td>\n",
       "      <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>-3.699841</td>\n",
       "      <td>[[0.0, 0.0, 0.0]]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1:FHI-aims/PBE/tight:phonon:supercell_phonon_0</td>\n",
       "      <td>(Atom('Cu', [4.3368086899420173e-19, 0.007070999999999999, 0.007070999999999999], index=0), Atom('Cu', [3.3306690738754696e-16, 1.81563, 1.8156300000000005], index=1), Atom('Cu', [7.262518, 10.893...</td>\n",
       "      <td>-236.789603</td>\n",
       "      <td>[[-1.13852957740976e-06, -0.0464638907314277, -0.0464636807741622], [-3.86335457040412e-06, 0.0124851330231607, 0.0124792943417333], [-1.94300535086066e-06, 6.63943441884098e-05, 6.92790474109119e...</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1:FHI-aims/PBE/tight:murnaghan:strain_1_02</td>\n",
       "      <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>-3.697932</td>\n",
       "      <td>[[0.0, 0.0, 0.0]]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1:FHI-aims/PBE/tight:murnaghan:strain_0_98</td>\n",
       "      <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>-3.697559</td>\n",
       "      <td>[[0.0, 0.0, 0.0]]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>A2:FHI-aims/PBE/tight:elastic:s_01_e_0_05000</td>\n",
       "      <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>-3.573436</td>\n",
       "      <td>[[0.0, 0.0, 0.0]]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>A1:FHI-aims/PBE/tight:elastic:s_01_e_m0_05000</td>\n",
       "      <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>-3.546222</td>\n",
       "      <td>[[0.0, 0.0, 0.0]]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>A3:FHI-aims/PBE/tight:elastic:s_01_e_m0_05000</td>\n",
       "      <td>(Atom('Cu', [1.2150849, 0.70152958, 0.9998186400000001], index=0), Atom('Cu', [0.0, 1.40305917, 2.99945593], index=1))</td>\n",
       "      <td>-7.079448</td>\n",
       "      <td>[[-3.5446112180968e-23, -1.13427558979097e-22, 0.0], [3.5446112180968e-23, 1.13427558979097e-22, 0.0]]</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>A2:FHI-aims/PBE/tight:elastic:s_01_e_m0_05000</td>\n",
       "      <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>-3.513068</td>\n",
       "      <td>[[0.0, 0.0, 0.0]]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>A2:FHI-aims/PBE/tight:elastic:s_23_e_m0_05000</td>\n",
       "      <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>-3.494427</td>\n",
       "      <td>[[0.0, 0.0, 0.0]]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  \\\n",
       "0                A1:FHI-aims/PBE/tight:elastic:s_e_0   \n",
       "1         A1:FHI-aims/PBE/tight:murnaghan:strain_1_0   \n",
       "2    A1:FHI-aims/PBE/tight:phonon:supercell_phonon_0   \n",
       "3        A1:FHI-aims/PBE/tight:murnaghan:strain_1_02   \n",
       "4        A1:FHI-aims/PBE/tight:murnaghan:strain_0_98   \n",
       "..                                               ...   \n",
       "100     A2:FHI-aims/PBE/tight:elastic:s_01_e_0_05000   \n",
       "101    A1:FHI-aims/PBE/tight:elastic:s_01_e_m0_05000   \n",
       "102    A3:FHI-aims/PBE/tight:elastic:s_01_e_m0_05000   \n",
       "103    A2:FHI-aims/PBE/tight:elastic:s_01_e_m0_05000   \n",
       "104    A2:FHI-aims/PBE/tight:elastic:s_23_e_m0_05000   \n",
       "\n",
       "                                                                                                                                                                                                       atoms  \\\n",
       "0                                                                                                                                                                     (Atom('Cu', [0.0, 0.0, 0.0], index=0))   \n",
       "1                                                                                                                                                                     (Atom('Cu', [0.0, 0.0, 0.0], index=0))   \n",
       "2    (Atom('Cu', [4.3368086899420173e-19, 0.007070999999999999, 0.007070999999999999], index=0), Atom('Cu', [3.3306690738754696e-16, 1.81563, 1.8156300000000005], index=1), Atom('Cu', [7.262518, 10.893...   \n",
       "3                                                                                                                                                                     (Atom('Cu', [0.0, 0.0, 0.0], index=0))   \n",
       "4                                                                                                                                                                     (Atom('Cu', [0.0, 0.0, 0.0], index=0))   \n",
       "..                                                                                                                                                                                                       ...   \n",
       "100                                                                                                                                                                   (Atom('Cu', [0.0, 0.0, 0.0], index=0))   \n",
       "101                                                                                                                                                                   (Atom('Cu', [0.0, 0.0, 0.0], index=0))   \n",
       "102                                                                                   (Atom('Cu', [1.2150849, 0.70152958, 0.9998186400000001], index=0), Atom('Cu', [0.0, 1.40305917, 2.99945593], index=1))   \n",
       "103                                                                                                                                                                   (Atom('Cu', [0.0, 0.0, 0.0], index=0))   \n",
       "104                                                                                                                                                                   (Atom('Cu', [0.0, 0.0, 0.0], index=0))   \n",
       "\n",
       "         energy  \\\n",
       "0     -3.699843   \n",
       "1     -3.699841   \n",
       "2   -236.789603   \n",
       "3     -3.697932   \n",
       "4     -3.697559   \n",
       "..          ...   \n",
       "100   -3.573436   \n",
       "101   -3.546222   \n",
       "102   -7.079448   \n",
       "103   -3.513068   \n",
       "104   -3.494427   \n",
       "\n",
       "                                                                                                                                                                                                      forces  \\\n",
       "0                                                                                                                                                                                          [[0.0, 0.0, 0.0]]   \n",
       "1                                                                                                                                                                                          [[0.0, 0.0, 0.0]]   \n",
       "2    [[-1.13852957740976e-06, -0.0464638907314277, -0.0464636807741622], [-3.86335457040412e-06, 0.0124851330231607, 0.0124792943417333], [-1.94300535086066e-06, 6.63943441884098e-05, 6.92790474109119e...   \n",
       "3                                                                                                                                                                                          [[0.0, 0.0, 0.0]]   \n",
       "4                                                                                                                                                                                          [[0.0, 0.0, 0.0]]   \n",
       "..                                                                                                                                                                                                       ...   \n",
       "100                                                                                                                                                                                        [[0.0, 0.0, 0.0]]   \n",
       "101                                                                                                                                                                                        [[0.0, 0.0, 0.0]]   \n",
       "102                                                                                                   [[-3.5446112180968e-23, -1.13427558979097e-22, 0.0], [3.5446112180968e-23, 1.13427558979097e-22, 0.0]]   \n",
       "103                                                                                                                                                                                        [[0.0, 0.0, 0.0]]   \n",
       "104                                                                                                                                                                                        [[0.0, 0.0, 0.0]]   \n",
       "\n",
       "     number_of_atoms  \n",
       "0                1.0  \n",
       "1                1.0  \n",
       "2               64.0  \n",
       "3                1.0  \n",
       "4                1.0  \n",
       "..               ...  \n",
       "100              1.0  \n",
       "101              1.0  \n",
       "102              2.0  \n",
       "103              1.0  \n",
       "104              1.0  \n",
       "\n",
       "[105 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_job.to_pandas()"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting project"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 9,
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   "metadata": {},
   "outputs": [],
   "source": [
    "fit_pr = Project(\"pacemaker_fit\")"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 10,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "job = fit_pr.create_job(job_type=fit_pr.job_type.PaceMakerJob, job_name=\"df1_cut5_pyace\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "cutoff = 5.0 # potential cutoff"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "Potential specification"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 12,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "job.input[\"cutoff\"] = cutoff # global potential cutoff"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 13,
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   "metadata": {},
   "outputs": [],
   "source": [
    "job.input[\"potential\"]= {\n",
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    "    \"deltaSplineBins\": 0.001, # spline bins, used for fast radial functions evaluations\n",
    "    \n",
    "    \"element\": \"Cu\", # element - copper    \n",
    "    \n",
    "    \"npot\": \"FinnisSinclairShiftedScaled\",    # embedding functions\n",
    "    \"ndensity\": 2, # number of densities: rho_1 and rho_2\n",
    "    \"fs_parameters\": [1, 1, 1, 0.5], # parameters of embedding functions (for two densities)\n",
    "    #this embedding function corresponds to rho_1 + sqrt(abs(rho_2)) , with some modifications\n",
    "    \n",
    "    # radial base functions specification\n",
    "    \"radbase\": \"ChebExpCos\",\n",
    "    \"radparameters\": [5.25],\n",
    "    \"rcut\": cutoff,\n",
    "    \"dcut\": 0.01,\n",
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    "    \"NameOfCutoffFunction\": \"cos\",\n",
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    "    \n",
    "    # MOST IMPORTANT: potential \"shape\" i.e.:\n",
    "    # number of orders (rank), maximum index of radial functions (nradmax) and orbital moments (lmax) for each rank\n",
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    "    \"rankmax\": 3,\n",
    "    \"nradmax\": [7,2,1],\n",
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    "    \"lmax\": [0,2,1],    \n",
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    "}"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting settings:"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 14,
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   "metadata": {},
   "outputs": [],
   "source": [
    "job.input[\"fit\"]= {    \n",
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    "    # loss function specification\n",
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    "    'loss': {\n",
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    "        'kappa': 0.5, # relative weight of forces residuals\n",
    "        \n",
    "        # coefficients L1-L2 regularization\n",
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    "        'L1_coeffs': 5e-7, # L1-regularization\n",
    "        'L2_coeffs': 5e-7, # L2-regularization\n",
    "        'w1_coeffs': 1,\n",
    "        'w2_coeffs': 1,\n",
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    "        \n",
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    "        #radial smoothness regularization\n",
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    "        'w0_rad': 1e-4,     # for radial functions values\n",
    "        'w1_rad': 1e-4,     # for radial functions first derivatives\n",
    "        'w2_rad': 1e-4,     # for radial functions second derivatives\n",
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    "    },\n",
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    "    \n",
    "    # minimization setup:\n",
    "    'optimizer': 'BFGS',  # scipy BFGS algorithm\n",
    "    'maxiter': 150,       # max number of iterations    \n",
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    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
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    "Assign the dataset to fit on"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 15,
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   "metadata": {},
   "outputs": [],
   "source": [
    "job.structure_data=data_job"
   ]
  },
  {
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
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    "Run fitting job"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 16,
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   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "2021-03-08 11:13:01,028 - root - INFO - structure_data is TrainingContainer\n",
      "2021-03-08 11:13:01,036 - root - INFO - Saving training structures dataframe into /home/yury/PycharmProjects/pyiron-2021/pyiron_potentialfit/day_2/03-ace/pacemaker_fit/df1_cut5_pyace_hdf5/df1_cut5_pyace/df_fit.pckl.gzip with pickle protocol = 4, compression = gzip\n"
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "The job df1_cut5_pyace was saved and received the ID: 289\n"
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     ]
    }
   ],
   "source": [
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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   "execution_count": 17,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
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       "      <th>0</th>\n",
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       "      <td>289</td>\n",
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       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>df1_cut5_pyace</td>\n",
       "      <td>/df1_cut5_pyace</td>\n",
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       "      <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
       "      <td>pyiron_potentialfit/day_2/03-ace/pacemaker_fit/</td>\n",
       "      <td>2021-03-08 11:13:01.402031</td>\n",
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       "      <td>317.0</td>\n",
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       "      <td>pyiron@dell-inspiron#1</td>\n",
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      "text/plain": [
       "    id    status chemicalformula             job           subjob  \\\n",
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       "0  289  finished            None  df1_cut5_pyace  /df1_cut5_pyace   \n",
       "\n",
       "                               projectpath  \\\n",
       "0  /home/yury/PycharmProjects/pyiron-2021/   \n",
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       "\n",
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       "                                           project                  timestart  \\\n",
       "0  pyiron_potentialfit/day_2/03-ace/pacemaker_fit/ 2021-03-08 11:13:01.402031   \n",
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       "\n",
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       "                    timestop  totalcputime                computer  \\\n",
       "0 2021-03-08 11:18:19.154866         317.0  pyiron@dell-inspiron#1   \n",
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       "\n",
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       "       hamilton hamversion parentid masterid  \n",
       "0  PaceMakerJob        0.1     None     None  "
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      ]
     },
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     "execution_count": 17,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_pr.job_table(full_table=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse the fitting results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss function"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 18,
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   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(job[\"output/log/loss\"])\n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"# iter\")\n",
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    "plt.ylabel(\"Loss\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE of energies per atoms "
730
731
732
733
   ]
  },
  {
   "cell_type": "code",
734
   "execution_count": 19,
735
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743
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745
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748
749
750
751
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   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(job[\"output/log/rmse_energy\"])\n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"# iter\")\n",
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    "plt.ylabel(\"Energy RMSE, meV/atom\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE of forces norm"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 20,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(job[\"output/log/rmse_forces\"])\n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"# iter\")\n",
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    "plt.ylabel(\"Forces RMSE, meV/Ang/structure\");"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview of the fitted potential internals"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 40,
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   "metadata": {},
   "outputs": [],
   "source": [
    "from pyace import *"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 41,
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   "metadata": {},
   "outputs": [],
   "source": [
    "final_potential = job.get_final_potential()\n",
    "final_basis_set = ACEBBasisSet(final_potential)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For single-species potential there is only one **species block** for *Cu*:"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 42,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
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     "execution_count": 42,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(final_potential.funcspecs_blocks)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 43,
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   "metadata": {},
   "outputs": [],
   "source": [
    "Cu_block = final_potential.funcspecs_blocks[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic definitions and notations:\n",
    "\n",
    "* Radial functions: $R_{nl}(r) = \\sum_k c_{nlk} g_k(r)$\n",
    "\n",
    "\n",
    "* Spherical harmonics:  $  Y_{lm}(\\hat{\\pmb{r}}_{ji}) $\n",
    "\n",
    "\n",
    "* Basis function: $\\phi_{\\mu_j \\mu_i nlm}(\\pmb{r}_{ji}) = R_{nl}^{\\mu_j \\mu_i}(r_{ji}) Y_{lm}(\\hat{\\pmb{r}}_{ji}) $\n",
    "\n",
    "\n",
    "* Atomic base (A-functions): $ A_{i \\mu n l m} = \\sum_j \\delta_{\\mu \\mu_j} \\phi_{\\mu_j\\mu_i nlm}(\\pmb{r}_{ji})  $\n",
    "\n",
    "\n",
    "* Product of atomic base: $ \\pmb{A}_{i\\pmb{\\mu n l m}} =  \\prod_{t = 1}^{\\nu} A_{i \\mu_t n_t l_t m_t}  $\n",
    "\n",
    "\n",
    "* Equivariant basis (B-functions):  $  {B}_{i\\pmb{\\mu n l L}}  = \\sum_{\\pmb{m}}   \\left(\n",
    "\\begin{array}{c} \n",
    "\\pmb{l m} \\\\\n",
    "\\pmb{L M}\n",
    "\\end{array}\n",
    "L_R\n",
    "\\right)  \\pmb{A}_{i\\pmb{\\mu n l m}}  $ ,\n",
    "\n",
    "where $ \\left(\\begin{array}{c} \\pmb{l m} \\\\ \\pmb{L M}\\end{array} L_R\\right) $ is *generalized Clebsh-Gordan coefficients* \n",
    "\n",
    "\n",
    "* Atomic property (densities) $ \\rho_i^{(p)} = \\sum_{\\pmb{\\mu n l L}} {c}^{(p)}_{\\mu_i\\pmb{\\mu n l L}} {B}_{i\\pmb{\\mu n l L}}  $\n",
    "\n",
    "\n",
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    "* Atomic energy: $ E_i = F(\\rho_i^{(1)}, \\dots,\\rho_i^{(P)} )  $, where $F$ is embedding function"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "radial coefficients $c_{nlk}$:"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 44,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 7)"
      ]
     },
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     "execution_count": 44,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(Cu_block.radcoefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the radial basis functions ($g_k$) and radial functions ($R_{nl}$) and their derivatives:"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 45,
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   "metadata": {
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    "scrolled": true
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1440 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RadialFunctionsVisualization(final_basis_set).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total number of basis functions"
   ]
  },
  {
   "cell_type": "code",
970
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Cu_block.funcspecs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of B-basis functions $ {B}_{i\\pmb{\\mu n l L}}$: $\\pmb{\\mu} = $ `elements`, $\\pmb{n} = $ `ns`, $\\pmb{l} = $ `ls`, $\\pmb{L} = $ `LS`)  and corresponding coefficients $ {c}^{(p)}_{\\mu_i\\pmb{\\mu n l L}} =$ `coeffs` for two densities"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[BBasisFunctionSpecification(elements=[Cu,Cu],  ns=[1],  ls=[0],  coeffs=[-0.947664,-0.20673]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu],  ns=[2],  ls=[0],  coeffs=[0.127744,0.0378252]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu],  ns=[3],  ls=[0],  coeffs=[0.395046,0.128133]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu],  ns=[4],  ls=[0],  coeffs=[0.490821,0.17195]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu],  ns=[5],  ls=[0],  coeffs=[0.192457,0.100866]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu],  ns=[6],  ls=[0],  coeffs=[-0.381239,-0.0435031]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu],  ns=[7],  ls=[0],  coeffs=[-0.717072,-0.124286]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu],  ns=[1,1],  ls=[0,0],  coeffs=[0.176241,0.135483]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu],  ns=[1,1],  ls=[1,1],  coeffs=[0.0335965,0.00542081]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu],  ns=[1,1],  ls=[2,2],  coeffs=[0.361527,0.101027]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu],  ns=[2,1],  ls=[0,0],  coeffs=[0.953892,0.247374]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu],  ns=[2,1],  ls=[1,1],  coeffs=[-0.000366411,-0.000581538]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu],  ns=[2,1],  ls=[2,2],  coeffs=[0.0654394,0.0146316]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu],  ns=[2,2],  ls=[0,0],  coeffs=[0.142427,0.0350483]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu],  ns=[2,2],  ls=[1,1],  coeffs=[0.00472481,6.57905e-05]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu],  ns=[2,2],  ls=[2,2],  coeffs=[0.00853967,-0.000426002]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu,Cu],  ns=[1,1,1],  ls=[0,0,0],  LS=[0],  coeffs=[-0.0106058,-0.0227657]),\n",
       " BBasisFunctionSpecification(elements=[Cu,Cu,Cu,Cu],  ns=[1,1,1],  ls=[1,1,0],  LS=[0],  coeffs=[0.309698,0.0822887])]"
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cu_block.funcspecs"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare to potential \"shape\" i.e.:\n",
    "* \"rankmax\": 3\n",
    "* \"nradmax\": [7,2,1]\n",
    "* \"lmax\": [0,2,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable parameters number\n",
      "\n",
      "B-functions coefficients:  36\n",
      "Radial functions coefficients:  42\n",
      "----------------------------------------\n",
      "Total number of trainable parameters:  78\n"
     ]
    }
   ],
   "source": [
    "print(\"Trainable parameters number\")\n",
    "print()\n",
    "print(\"B-functions coefficients: \",Cu_block.ndensityi * len(Cu_block.funcspecs))\n",
    "print(\"Radial functions coefficients: \",len(np.array(Cu_block.radcoefficients).flatten()))\n",
    "print(\"-\"*40)\n",
    "print(\"Total number of trainable parameters: \",len(final_potential.get_all_coeffs()))"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test fitted potential"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 29,
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   "metadata": {},
   "outputs": [],
   "source": [
    "test_pr = Project(\"test_ace_potential\")"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 30,
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   "metadata": {},
   "outputs": [],
   "source": [
    "test_pr.remove_jobs_silently()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 93,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>290</td>\n",
       "      <td>aborted</td>\n",
       "      <td>Cu4</td>\n",
       "      <td>opt_lammps</td>\n",
       "      <td>/opt_lammps</td>\n",
       "      <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
       "      <td>pyiron_potentialfit/day_2/03-ace/test_ace_potential/</td>\n",
       "      <td>2021-03-08 11:19:06.338172</td>\n",
       "      <td>2021-03-08 11:19:06.828952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pyiron@dell-inspiron#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
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       "    id   status chemicalformula         job       subjob  \\\n",
       "0  290  aborted             Cu4  opt_lammps  /opt_lammps   \n",
       "\n",
       "                               projectpath  \\\n",
       "0  /home/yury/PycharmProjects/pyiron-2021/   \n",
       "\n",
       "                                                project  \\\n",
       "0  pyiron_potentialfit/day_2/03-ace/test_ace_potential/   \n",
       "\n",
       "                   timestart                   timestop  totalcputime  \\\n",
       "0 2021-03-08 11:19:06.338172 2021-03-08 11:19:06.828952           0.0   \n",
       "\n",
       "                 computer hamilton hamversion parentid masterid  \n",
       "0  pyiron@dell-inspiron#1   Lammps        0.1     None     None  "
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      ]
     },
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     "execution_count": 93,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pr.job_table()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 94,
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   "metadata": {},
   "outputs": [],
   "source": [
    "cu_ace_potential = job.get_lammps_potential()"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Config</th>\n",
       "      <th>Filename</th>\n",
       "      <th>Model</th>\n",
       "      <th>Name</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>[pair_style pace\\n, pair_coeff  * * /home/yury/PycharmProjects/pyiron-2021/pyiron_potentialfit/day_2/03-ace/pacemaker_fit/df1_cut5_pyace_hdf5/df1_cut5_pyace/df1_cut5_pyace.ace Cu\\n]</td>\n",
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       "      <td></td>\n",
       "      <td>ACE</td>\n",
       "      <td>df1_cut5_pyace</td>\n",
       "      <td>[Cu]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
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       "                                                                                                                                                                                  Config  \\\n",
       "0  [pair_style pace\\n, pair_coeff  * * /home/yury/PycharmProjects/pyiron-2021/pyiron_potentialfit/day_2/03-ace/pacemaker_fit/df1_cut5_pyace_hdf5/df1_cut5_pyace/df1_cut5_pyace.ace Cu\\n]   \n",
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       "\n",
       "  Filename Model            Name Species  \n",
       "0            ACE  df1_cut5_pyace    [Cu]  "
      ]
     },
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     "execution_count": 95,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cu_ace_potential\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 96,
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   "metadata": {},
   "outputs": [],
   "source": [
    "lammps_job = test_pr.create.job.Lammps(\"opt_lammps\", delete_existing_job=True)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 97,
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   "metadata": {},
   "outputs": [],
   "source": [
    "lammps_job.potential = cu_ace_potential"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 98,
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   "metadata": {},
   "outputs": [],
   "source": [
    "lammps_job.structure = test_pr.create.structure.ase_bulk(\"Cu\",\"fcc\",cubic=True)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 99,
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   "outputs": [],
   "source": [
    "lammps_job.calc_minimize(pressure=0.0)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 104,
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   "metadata": {},
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job opt_lammps was saved and received the ID: 290\n"
     ]
    }
   ],
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   "source": [
    "lammps_job.run()"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>290</td>\n",
       "      <td>finished</td>\n",
       "      <td>Cu4</td>\n",
       "      <td>opt_lammps</td>\n",
       "      <td>/opt_lammps</td>\n",
       "      <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
       "      <td>pyiron_potentialfit/day_2/03-ace/test_ace_potential/</td>\n",
       "      <td>2021-03-08 17:52:19.328134</td>\n",
       "      <td>2021-03-08 17:52:20.225226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pyiron@dell-inspiron#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id    status chemicalformula         job       subjob  \\\n",
       "0  290  finished             Cu4  opt_lammps  /opt_lammps   \n",
       "\n",
       "                               projectpath  \\\n",
       "0  /home/yury/PycharmProjects/pyiron-2021/   \n",
       "\n",
       "                                                project  \\\n",
       "0  pyiron_potentialfit/day_2/03-ace/test_ace_potential/   \n",
       "\n",
       "                   timestart                   timestop  totalcputime  \\\n",
       "0 2021-03-08 17:52:19.328134 2021-03-08 17:52:20.225226           0.0   \n",
       "\n",
       "                 computer hamilton hamversion parentid masterid  \n",
       "0  pyiron@dell-inspiron#1   Lammps        0.1     None     None  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
    "test_pr.job_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
1414
    "## E-V curve"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 113,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "ev_job = test_pr.create.job.Murnaghan(\"murn\", delete_existing_job=True)"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 114,
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   "metadata": {},
   "outputs": [],
   "source": [
    "ref_job = test_pr.create.job.Lammps(\"ref_job\", delete_existing_job=True)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 115,
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   "metadata": {},
   "outputs": [],
   "source": [
    "ref_job.potential = cu_ace_potential"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 116,
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   "metadata": {},
   "outputs": [],
   "source": [
    "ref_job.structure = lammps_job.get_structure()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 118,
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   "metadata": {},
   "outputs": [],
   "source": [
1459
    "ev_job.ref_job = ref_job"
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   ]
  },
  {
   "cell_type": "code",
1464
   "execution_count": 119,
1465
1466
1467
1468
1469
1470
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
1471
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1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
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1493
      "The job murn was saved and received the ID: 291\n",
      "The job strain_0_9 was saved and received the ID: 292\n",
      "The job strain_0_92 was saved and received the ID: 293\n",
      "The job strain_0_94 was saved and received the ID: 294\n",
      "The job strain_0_96 was saved and received the ID: 295\n",
      "The job strain_0_98 was saved and received the ID: 296\n",
      "The job strain_1_0 was saved and received the ID: 297\n",
      "The job strain_1_02 was saved and received the ID: 298\n",
      "The job strain_1_04 was saved and received the ID: 299\n",
      "The job strain_1_06 was saved and received the ID: 300\n",
      "The job strain_1_08 was saved and received the ID: 301\n",
      "The job strain_1_1 was saved and received the ID: 302\n",
      "job_id:  292 finished\n",
      "job_id:  293 finished\n",
      "job_id:  294 finished\n",
      "job_id:  295 finished\n",
      "job_id:  296 finished\n",
      "job_id:  297 finished\n",
      "job_id:  298 finished\n",
      "job_id:  299 finished\n",
      "job_id:  300 finished\n",
      "job_id:  301 finished\n",
      "job_id:  302 finished\n"
1494
1495
1496
1497
     ]
    }
   ],
   "source": [
1498
    "ev_job.run()"
1499
1500
1501
1502
   ]
  },
  {
   "cell_type": "code",
1503
   "execution_count": 120,
1504
1505
1506
1507
   "metadata": {},
   "outputs": [
    {
     "data": {
1508
      "image/png": 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2nnPkyJGqqjp+/Hht2rSprly5Urds2aJ77bWXrlmzRlVVa9Wqpaqq27dv1w0bNqiq6urVq3XffffVwsLCIvvEe6/D7zl5GuN71e5cSsHfKuyCC8LrDzooOX2RGWOyS/PmzenWrRsAZ555JtOmTaNVq1a0bt0agCFDhvDhhx8WOaZWrVr07t2bt956i2+++Ybt27fTrl07AFq1akWHDh0A6NSpE8uWLWPDhg2sX7+eHj16RD3nSSedBEC7du1o27YtTZs2pVq1auyzzz4sX768yGurKqNGjaJ9+/b06dOHFStW8MsvvyT1PbHkUkKRzY3POiu8bfZsePRRSzDGVDSlbQ49dOhQJk6cyBNPPMG55567c321atV2zleqVIkdO3YUe67QMTk5OUWOz8nJ2eX4Z599ltWrVzNnzhzmzZtHkyZNkt7LgSWXEoj2HMv++0OXLm5++3ZYubLsnV0aY0oulQVjxfnpp5/49NNPAXj++efp06cPy5Yt4/vvvwfg6aef3nnH4delSxeWL1/Oc889x+mnnx73NerWrUv9+vX56KOP4p4zERs2bKBx48ZUqVKFGTNm8OOPMXvOLzVLLgmK94Dk2WeH559+Ojm9KRtjskebNm148sknad++PevWreOKK67giSee4NRTT6Vdu3bk5OQwbNiwqMcOHDiQbt26Ub9+/WJf58knn+Saa66hffv2zJs3j5tvvrlU8Q4ePJi8vDxyc3N59tlnOeCAA0p1nrhiVcZUpKm4Cv1Q5X2syvo1a1SrVAn/zvHq8Io9zhhTNtEqmdNt6dKl2rZt21Iff/zxx+vUqVOTGFFqWIV+kiXSpUuDBnD88eHlJ590/9odjDEmlvXr19O6dWtq1KjBkUceGXQ4SWfJJY6S9BU2ZEh4/qmnXH9jYAnGmPKuZcuWLFiwoMTH1atXj2+//ZaXXnopBVEFz5JLHLNnJ96ly3HHQcOGbj4/v2giCSWY2bNTE6cxFZkmUuNuyqQ077EllzhGjky8S5eqVV3PyCGhorGQXr3c+YwxyVO9enXWrl1rCSaFVN14LtWrVy/RcWIfCuTm5moyOpKbOxcOPtjN16gBP/8MdeqU+bTGmBhsJMr0iDUSpYjMUdXcaMdYl/tJ1KEDtGsHX30FmzfDyy/DeecFHZUx5VeVKlVKNDqiSR8rFksiETjnnPDyxIlBRWKMMcGy5JJkgwdDpUpu/qOPYMmSYOMxxpggWHJJsiZN4Nhjw8t292KMqYgCSS4icqqILBSRQhHZpTJIRFqISIGIXB3j+NEiskJE5nnTcd76liKy2bf+oVRfSzT+epaJE8PPvBhjTEUR1J3LAqA/8GGM7eOAKcWcY5yqdvCmd3zrl/jWR+/MJ8WOPx4aNXLz+fkwdWoQURhjTHACSS6q+rWqLo62TUROBn4AFqY1qCSqWrVoV/wTJgQXizHGBCGj6lxEpBZwLTAmgd0vEZH5IjJBRPzdibYSkbki8oGIdI/zWheKSJ6I5K1evbqsoe/CNzQDr78Oa9cm/SWMMSZjpSy5iMhUEVkQZeob57AxuOKugmJO/x9gX6ADsAq421u/Cmihqh2BK4HnRCTqY4yq+oiq5qpqbqNQGVYSHXQQHHKIm9+2DZ57LukvYYwxGStlD1Gqap9SHNYFGCAiY4F6QKGIbFHV+yPOvXM8ThF5FHjLW78V2OrNzxGRJUBroOyP35fCeefBrFlufsIEuPTSIKIwxpj0y6hiMVXtrqotVbUlMB64PTKxAIhIU99iP1wDAUSkkYhU8ub3AfbH1d8EYtAgCHXHM28efPFFUJEYY0x6BdUUuZ+I5AOHAW+LyLsJHPOYr9nyWBH5SkTmA72AK7z1RwDzReRL4GVgmKquS8ElJKRuXTjllPDyY48FFYkxxqSXdVxJ8jqujOaDD6BnTzdfpw6sWgU1a6bkpYwxJq3idVyZUcVi5dERR8D++7v533+HcjoukDHGFGHJJcVEYOjQ8LIVjRljKgJLLmkwZAhU9trlffwxfP11sPEYY0yqWXJJgyZN4KSTwsuPPx5cLMYYkw6WXNLEXzT25JOwdWtwsRhjTKpZckmTv/0Nmjd382vWwOTJwcZjjDGpZMklTSpVgvPPDy8//HBwsRhjTKpZckmj88+HHO8dnz4dvv022HiMMSZVLLmkUbNmcMIJ4eVHHgkuFmOMSSVLLmn297+H5ydOhC1bAgvFGGNSxpJLmh19NLRo4ebXroVXXw02HmOMSQVLLmlWqRJccEF4+aGHgovFGGNSxZJLAM4/3yUZgI8+gkWLgo3HGGOSzZJLAJo2hb6+8TitWbIxpryx5BKQ4cPD8xMnwsaNgYVijDFJZ8klIL17Q+vWbv733+G554KNxxhjksmSS0BycorevTz4INi4bcaY8sKSS4CGDIEaNdz8vHnw2WeBhmOMMUljySVA9evDGWeElx98MLhYjDEmmSy5BOyii8LzkybB6tXBxWKMMcliySVgBx8MXbq4+W3bbCAxY0z5YMklA/jvXh58EHbsCC4WY4xJBksuGWDgQGjY0M0vXw5vvBFsPMYYU1aWXDJA9epw4YXh5fvvDy4WY4xJBksuGWL48HB/YzNmwIIFwcZjjMluY8e675JkmDHDna8kLLlkiGbNoF+/8LLdvRhjyqJzZ1fkXtYEM2OGO0/nziU7LpDkIiKnishCESkUkdwo21uISIGIXB3nHJeKyGLvPGN9668Xke+9bUen6hpS4dJLw/NPPw2//RZcLMaY7Narl3u8oSwJJpRYJk1y5yuJoO5cFgD9gQ9jbB8HTIl1sIj0AvoC7VW1LXCXt/5AYBDQFjgGeFBEKiUx7pTq3h3at3fzmzbBhAnBxmOMyW5lSTBlSSwQUHJR1a9VdXG0bSJyMvADsDDOKYYD/1LVrd75fvXW9wVeUNWtqroU+B44JGmBp5hI0buX++6zZsnGmLIpTYIpa2KBDKtzEZFawLXAmGJ2bQ10F5HPReQDEQmVBu4FLPftl++ti/ZaF4pInojkrc6gx+IHD4YGDdz8jz9as2RjTNmVJMEkI7FACpOLiEwVkQVRpr5xDhsDjFPVgmJOXxmoDxwKXANMEhEBJMq+UfsaVtVHVDVXVXMbNWqUwBWlR40aMGxYeHn8+MBCMcaUI4kkmGQlFkhhclHVPqp6UJRpcpzDugBjRWQZcDkwSkQuibJfPvCqOrOAQqCht765b79mwMpkXE86XXQRVK7s5j/6CObMCTYeY0z5EJlg/I2GkplYIMOKxVS1u6q2VNWWwHjgdlWN1ij3daA3gIi0BqoCa4A3gEEiUk1EWgH7A7PSEHpS7bmn+5BD7rknuFiMMeVLKMEMGAAtWrh63rfeSm5igeCaIvcTkXzgMOBtEXk3gWMe8zVbngDsIyILgBeAId5dzEJgErAI+C9wsar+mZqrSK0RI8LzL7wAq1YFF4sxpnzp1QvatYOCAvdMXf/+yU0sAKI2/CG5ubmal5cXdBi76NYN/vc/N3/jjXDbbcHGY4wpH95+G044Ibx81lnw1FMlP4+IzFHVXZ5VhAwrFjNFXX55eP4//3HPvhhjTFls3AgXXxxebt8epkxJXlcxIZZcMli/frD33m5+7drS/bIwxhi/0aPdYw4AderAtGllf5I/GksuGSjU4VzlykXvXv79bygsLNm5StPhnDGmfJo3z32PhNxzjxvuIxldxUSy5JKB/B3OnX8+1K3r1n/3Hbz5ZuLnKW2Hc8aY8ufPP2HQoPAP1J49YciQ8PZkJxhLLhnI/yHn5cHf/x7edvfdiZ0j2W3WjTHZbcQIWOx1ulW1Kjz0kOtyyi+ZCcaSS4byf8idOhV9qHJWMU/uWGIxxvi98AI88EB4+cYb4S9/ib5vshKMJZcMFvqQL77Y3cKG3HVX7GMssRhj/KZPL1r8deCBcO218Y9JRoKx5JLhQh+y/zGcV16BJUt23dcSizHGb8YMOPlk2LYtvO7RR12xWHHKmmAsuWSBXr3g1VehShW3XFi4a92LJRZjjN+MGXDqqeHh08ENp961a+LnKEuCseSSJXr1gjvuCC8/8QT86o1iY4nFGBNp9mzIzYX1693ynnsW/Q5JVCjBzJ5dsuPiJhcROUxEHhCR+SKyWkR+EpF3RORiEalb8jBNWVx5JbRu7ea3bHGDiVliMcZE07EjvOvrtfE//wk/1lBSvXrByJElOyZmchGRKcBQ4F3ckMFNgQOBG4HqwGQROal0oZrSEIF//CO8fNdd7rbXEosxxm/jxqKPMAwcCCel+du6cpxtZ6nqmoh1BcAX3nS3iDRMWWQmqv79Yd99XYX+li3QvbslFmNMUTffDEuXuvn69eHee9MfQ7xisdEiErfqJ0ryMSlWqRKceGJ4edo0eO+94OIxxmSWzz8vOoLtv/8NTZqkP454yeU73N3JMhG5U0Q6pCkmE8eMGfDMM+7XCLiWY6eckvweTY0x2WfrVjjvvHAXL0cdVfQZl3SKmVxU9R5VPQzoAawDnhCRr0XkZm/0R5Nm/sr7664Lr69TJ/k9mhpjss8//wmLFrn5WrXgkUd27eIlXYptiqyqP6rqnaraETgD6Ad8nfLITBGRrcKGD4d69dy2lSvdsiUYYyquL78s2tT4X/+Cli0DC6f45CIiVUTkRBF5FpgCfAuckvLIzE7RmhvXrg2XXRbeZ/JkePFFSzDGVETbt7visB073PLhh8NFFwUbU7ymyEeJyAQgH7gQeAfYV1VPU9XX0xRfhRfvOZbLLnO3vgDz57uRKlMx6I8xJrONHQtffOHmq1WDxx6DnIAfkY/38qOAT4E2qnqiqj6rqhvTFJeh+AckGzQo2pb9n/90HVxagjGm4liwAMaMCS/fdlvsHo/TKV6Ffi9VfVRV14nI4SJyLoCINBKRVukLsWJK9Mn7q64Kd0L32WeuaXIqRpUzxmSeHTvg3HNdsRhAly6uJ49MkEidyy3AtcD13qoqwDOpDKqiK0mXLnvu6UarDBkzBlQtwRhTEdx9d7jH9KpVYcKEoh1VBimRUrl+wEnARgBVXQnUTmVQFd3s2SXr0uW668I9Jn/8MXzwgZsvbYdzxpjMt2iRexI/ZMwYN1ZLpkgkuWxTVQUUQERqpTYkM3Jkybp0adECzjknvHzrreH50nQ4Z4zJbDt2uIcjQ+O05ObC1VcHG1OkRJLLJBF5GKgnIhcAU4FHUxuWKanrrw/fDs+Y4YZDNsaUT3feWbQ47Mknw0OhZ4pEHqK8C3gZeAX4C3Czqt6X6sBMybRqBWefHV72370YY8qPL7/ctXVYJhWHhcR7zmVnpwGq+r6qXqOqV6vq+9H2KQkROVVEFopIoYjkRtneQkQKRCTmjZ6IXCoii73zjPXWtRSRzSIyz5seKk182WrUqHDb9qlTXf2LMab82LbNFYGHWocdeqhrMZqJ4t25zPC+wFv4V4pIVRHpLSJPAqXtEm0B0B/4MMb2cbjeAKISkV5AX6C9qrYF7vJtXqKqHbxpWCnjy0r77QdnnRVevuWW4GIxxiTfrbfCvHluvnp1mDgxc1qHRYqXXI4B/gSeF5GVIrJIRJbieks+HRinqhNL86Kq+rWqLo62TUROBn4AFsY5xXDgX6q61Tvfr6WJozy66abwH9v06TBzZqDhGGOS5PPPi/YddvvtmfGwZCzxHqLcoqoPqmo3YG/gSKCjqu6tqheo6rxkB+O1RLsWGFPMrq2B7iLyuYh8ICKdfdtaichcb333OK91oYjkiUje6tWrkxB9Zth336Itx265xT33YozJXps2uTrVUFf6PXrAiBHBxlSchHqfUdXtqrpKVdcnemIRmSoiC6JMfeMcNgZ3R1RQzOkrA/WBQ4FrcC3aBFgFtPB6cL4SeE5E6sS4pkdUNVdVcxs1apToZWWFG28Mtxz58EN3B2OMyV7XXw/ffuvmd9vNFYcF3XdYcVLWeE1V+5TisC7AAK+Cvh5QKCJbVPX+iP3ygVe9529miUgh0FBVVwOhorI5IrIEd5eTV9rryEYtW7qn9h9+2C3fdBP07h3cuA7GmNKbNq3oMMXjxwfblX6iMir3qWp3VW2pqi2B8cDtURILwOtAbwBv4LKqwBqv37NK3vp9gP1x9TcVzqhR4af2P/0U3nkn2HiMMSX3229Fi7mPP951rZ8NEulb7BIRqZ/MFxWRfiKSDxwGvC0i7yZwzGO+ZssTgH1EZAHwAjDEu4s5ApgvIl/ins0Zpqrrkhl7tmjRomiPyTfcEC6vNcZkh0sugfx8N9+woetKP1tKIESLqe0VkX8Ag4AvcF/q72pxB2WZ3NxczcsrfyVnP//sKvg3bXLLL7wAp50WbEzGVCRjx0LnziXrzinkhRfg9NPDy2PGuObHmdSdk4jMUdVdnlWExJ7QvxFXvPQ4cA7wnYjcLiL7JjVKk3R77FG0RclNN4VHqjPGpF7nzqXrmXz5cjd0ecgxx8B997nzZYtEW4sp8LM37cC11Ho59GS8yVzXXAN167r5775zfRAZY9KjNENfFBa6TinXr3fLTZqUvKf0TJBInctlIjIHGAt8ArRT1eFAJ+CUFMdnyqh+/aK30aNHw+bNgYVjTIVT0gRz993h/URgyxZ46aXsSiyQ2J1LQ6C/qh6tqi+p6nYAVS0ETkhpdCYpLrsMGjd28/n58MADwcZjTEWTaIKZO9c1vgmpUQNeey37EgskllzGA3+IyO6+qQq4blxSGp1Jit12Kzqo0O23uyaOxpj0KS7BbNoEgweHO6WsXBlefz07Ewsklly+AFYD3+L6FVsNLBWRL0SkUyqDM8lzwQWu5Ri4xPKvfwUbjzEVUbwEc9VV8LXv5/rjj8NRR6U3vmRKJLn8FzhOVRuqagPgWGAScBHwYCqDM8lTtaq7Ywm55x7XIsUYk17REszrr8NDvgFCrryy6PhM2SiR5JKrqjsfclTV94AjVPUzoFrKIjNJN2CAGw4VYOtW65LfmKD4E8ykSa67ppDu3eGuu2Ifmy0SSS7rRORaEdnbm0YCv3ndrNgz31kkJ8c91BUycSLMnx9YOMZUaL16wfPPw5lnwjqvH5FGjWDy5Ox5Cj+eRJLLGUAzXH9erwPNvXWVgIGpCsykRq9ecOyxbl7VPQdjjAnGrFnhCnyAl192jw+UB3GTi3d3Ml5VL1XVjt50qaquVtVtqvp9muI0SfR//xfurvu99+C//w02HmMqok8+cb1mhNSsCX/+GVw8yRY3uajqn0AjEamapnhMGrRtC0OHhpevusq6hTEmndatg/79w53JduvmisNK01VMpkqkWGwZ8ImI3CQiV4amFMdlUuzWW93zLwCLFrlmj8aY1FOFvn3hV29w9vr14bnnoE+fkncVk8kSSS4rgbe8fWv7JpPFmjSB664LL998M/z+e3DxGFNRXH45fPxxeHnCBDdEBpSuL7JMVWyX+zt3FKmlqhtTHE8gymuX+8XZvBn+8pfw8y4jR8KddwYbkzHl2cMPw7Bh4eWLL4b7owyHOGNGuJlyJj+hX6Yu90XkMBFZBHztLf9VROzhyXKgRo2iT+qPG+d6TjbGJN+bb8JFF4WXO3aM/TxLebiDSbRvsaOBtQCq+iVuxEdTDpx+OnTt6ua3b3eV+8aY5Jo+3T3EHKrAr13bJY/q1WMfk+0JJtHxXCI7CilHDeYqNhG4997wQ1tvvgnvFjvotDEmUTNmuAr8bdvC6x59FPbbr/hjsznBJJJclotIV0BFpKqIXI1XRGbKh06d4Nxzw8uXX170wS5jTOnMmOGaHPvHUBo2rGTDjWdrgkkkuQwDLgb2AvKBDt6yKUf++U93qw7wzTduSFVjTNl88AFUqxZ+OLJjR1e3WVKhBDN7dnLjS6WEW4uVZxW1tViku+4Kdwez226weDHsuWewMRmTrQoLXXHYW2+55bp1Yc6c8NAX5UFZW4s1EpFRIvKIiEwITckP0wRtxAho08bNFxTA1VcHG48x2Wzs2HBiAXjiifKVWIqTSLHYZKAuMBV42zeZcqZKlaJt7p9/HmbODCwcY7LWtGlFhyu+6iro1y+4eIKQSHKpqarXquokVX0lNKU8MpN2Y8e6VmP+ysaLLy5d5f6MGUW79zemoli+HAYNCjc7PvxwuOOOYGMKQiLJ5S0ROS7lkZjAde7sWqT07w+1arl1ixbB+PElO0/o6eLOnZMeojEZbetW9zzLmjVueY89XEV8lSrBxhWERJLLCFyC2SIiv4vIHyJivVCVQ6EWKRdf7AYwChk9Gn78MbFzZEu3FcakwogRbowWgEqV3P+Dpk2DjSkoxSYXVa2tqjmqWl1V63jLdcryoiJyqogsFJFCEdmlpYGItBCRAu+ZmmjHvygi87xpmYjM8227XkS+F5HFInJ0WeKsiEIJ5pVXoFUrt27TJrjkEtebazyWWExF9uijru+wkLvuckMWV1SJtBYTETlTRG7ylpuLyCFlfN0FQH/gwxjbxwFTYh2sqqepagdV7QC8ArzqxXYgMAhoCxwDPOgNeGZKIJRgfvstvO6tt+C112IfY4nFVGSffeZ+gIWcfrq7i6nIEikWexA4DDe0MUAB8EBZXlRVv1bVxdG2icjJwA/AwuLOIyKCG2r5eW9VX+AFVd2qqkuB74GyJsIKqVcvePXVon0fXXZZ9G75LbGYiuznn+GUU8Ldu/z1r/DYY+EulSqqRJJLF1W9GNgCoKq/ASkZmVJEagHXAmMSPKQ78Iuqhvry3Qvw94OW762L9loXikieiOStXr26tCGXa716wYsvhv+TrFgBo0YV3ccSi6nIQhX4K1e65fr13Y+ymjWDjSsTJJJctntFSwruoUqgsLiDRGSqiCyIMvWNc9gYYJyqFiQUPZxO+K4FINpvhag1Bar6iKrmqmpuo0aNEny5iuekk4omlAcfDA90ZInFVGSqrijsk0/cck4OvPAC7LNPsHFlisoJ7HMv8BrQWET+CQwAbizuIFXtU4p4ugADRGQsUA8oFJEtqrrLcDoiUhlXb9PJtzofaO5bboYbSdOUwW23wdSp8Pnn7j/U0KFwzz2uRZklFlNRPfigK/4KufNO+Nvfgosn0xSbXFT1WRGZAxyJuzM4WVVT0iuyqu5sWyEio4GCaInF0wf4RlXzfeveAJ4TkX8DewL7A7NSEWtFIgIvveRGrdy82fU51r+/q+S3xGIqopkzi1bYn3mmjYUUKdHxXL5R1QdU9f5kJBYR6Sci+biGAm+LSLEjiIjIYxHNlgdRtEgMVV0ITAIWAf8FLlZVG3smCZo3Lzpq3ubNrnzZmIpmyRJXgR/q6Tg3Fx55xCrwI1mvyFivyImaNg2OOQZ27HDL++7rnuCvmpLmHcZkng0b4LDD4GvvJ3aTJpCXB82aBRtXUMrUK7Ix4CrvBw1yZcyh5slLlsAFFwQblzHpsmOH63cvlFiqVYPJkytuYimOJRdTLH+rsCFD3MBiIU895YoEjCnvrrqq6BDgTzwBXboEF0+ms+Ri4orW3HjECOjWLbzPRRfBe+8FE58xiRg7tmxDBD/wANx7b3i5d2/3FL6JzZKLiSnWcyyVKrlfbTVquOU//4STT86u8b1NxRLq8bs0f6PvvON6pwipWnXXh4nNriy5mKiKe0By//3hX/8KL2/Z4ponW4IxmSjUX15JE8z8+a6eJTQ2S+XKrp7lyCNTE2d5YsnF7CLRJ+8vucQVD4B7uLJGDTj1VEswJjOVNMGsWAHHH++G/IbwE/jHHJPaOMsLSy6miJJ06ZKTAxMnQt26bnnVKjjkkNIXPxiTaokmmN9/h+OOg3zvEW0R16X+KaekJ87ywJKL2ak0fYU1b+66wQiZMgUuvdQSjMlcxSWY7dtdZ5Tz54fX3XknnHde+mIsDyy5mJ1mzy5dX2FnnOGegQm55x7Xumb27OTGZ0yyxEowqnDhhfD+++F1I0fCNdekP8ZsZ0/oY0/oJ8Nvv7lxLJZ7Ax706OGe6K9kQ7WZDBZ5t37DDXD77eHt55zjWkaa6OwJfZNy9evDs8+6ehiADz6AO+4INiZjiuO/g7nssqKJ5dhjYcKE4GLLdpZcTNJ07w433xxeHj06PNaFMZmqVy+4+GK4777wukMPhTfesM4oy8KSi0mqG25wSQbcw5WDBsGaNcHGZEw8U6cWvcveay9XpFs5kdGuTEyWXExSVa7sisd2390t5+fDWWeFH0IzJpPMmuV6l9i2zS03aOAeCP7880DDKhcsuZika97cdWgZ8t//Wv2LyTyLFrl6lY0b3XLDhjBnjhsYz5rSl50lF5MSxx8P110XXr75ZvvPajLH0qVuSOJ169xynTrw4Yew996l7yrGFGXJxaTMbbfBEUe4+cJCV/+Snx//GGNSbcUK1zfYihVuuXp1V8fSpk14H0swZWfJxaRM5crw/PPQuLFb/vVX133G1q3BxmUqrl9/hT593J0LQJUqrtfj3ChPaliCKRtLLial9tzT/QcNPUw5a5br8NKYdFu3zhWFffONW87Jgddei98jhSWY0rPkYlKuRw+4++7w8mOP2eiVJr3Wr3eJ5csv3bKI6+H4+OOLP9YSTOlYcjFpcdllMHhwePmSS1wFqjGptmEDHH20awkW8sQTbniIRFmCKTlLLiYtRNzdSocObnn7dlf/Eir7NiYVfv/dNTeeNSu87tFHYciQkp/LEkzJWHIxaVOzphvFL1TBv2YNnHii+wIwJtlCdyyffhpe95//wNChpT9nKMFYj9/Fs+Ri0qpFC1eJWrWqW1640HXZ/+efwcZlypdQHctnn4XX3XsvDBtW9nP36uW64TfxWXIxade1a9EK/bffhhEj3FgaxpTVunVw1FFFi8Luv98NYmfSx5KLCcSQIXDtteHlBx6AceOCi8eUD7/8Aj17gn94pv/8x/V6bNIrkOQiIqeKyEIRKRSRXR5fEpEWIlIgIlfHOP5FEZnnTctEZJ63vqWIbPZteyjFl2LK4Pbb4bTTwstXXw2vvBJcPCa75ee7HiG++soti8DDDyenKMyUXFCdSi8A+gMPx9g+DpgS62BV3fmVJCJ3Axt8m5eoaockxGhSLCcHJk503XB8/LErFhs82HUg2KNH6c87Y4arcLVy8YpjyRL35P2yZW459Ld11llBRlWxBXLnoqpfq+riaNtE5GTgB2BhcecREQEGAs8nNUCTNtWrw+uvQ7NmbnnrVjjppPDDbiUVGra2c+ekhWgy3JdfQrdu4cRSpYpr0WWJJVgZVeciIrWAa4ExCR7SHfhFVb/zrWslInNF5AMR6R7ntS4UkTwRyVu9enUZojZl1aCBe6Cyfn23/PvvrgnpDz+U7DyR46Gb8u/jj91d7i+/uOXq1V1rxFNOCTYuk8LkIiJTRWRBlKlvnMPGAONUtSDBlzmdonctq4AWqtoRuBJ4TkTqRDtQVR9R1VxVzW3UqFGCL2dSpVUrmDkTatVyy7/84lr8hHquLY4llornzTddc+MNXqF4nTrw7ruJdeliUi9ldS6q2qcUh3UBBojIWKAeUCgiW1T1/sgdRaQyrt6mk+81twJbvfk5IrIEaA3kRR5vMk/79jBliusOfft2d+fSpw988EH4wctoLLFkj7FjXZFlWT+nRx5xFfWh5uuNG7vEEuoBwgQvo4rFVLW7qrZU1ZbAeOD2aInF0wf4RlV3jhAiIo1EpJI3vw+wP67+xmSJ7t3h1VddhSy4HmyPOio8qFMkSyzZpXPnsnWfouoGnvv738OJpVUr+OQTSyyZJqimyP1EJB84DHhbRN5N4JjHIpotD2LXivwjgPki8iXwMjBMVWN8LZlMdcIJrsdaEbc8f76rg1m/vuh+lliyT1n659q6Fc45xw1CF9Kpk+veZb/9khqmSQJReyya3NxczcuzkrNM8/TTcPbZ4eVOneC992D33S2xZLuSfn5r10L//kV70j72WHf8brulLk4Tn4jMUdUoQ61lWLGYMX5nnVW0m5g5c1wdzOuvW2LJdiW5g/n2WzjssKKJZehQ1wmqJZbMZcnFZLQLLiiaYObOhQED3JPXlliyWyIJ5r33oEsX+M73sMGdd7q/iSpV0hOnKR1LLibjXXABPP54ePnPP+H66+Gnn4KLySRHrASjCuPHu6KvUF1blSrw0kuu54VQfZzJXJZcTFZo1Qpq1w4vf/stHH44LI7az4PJJpEJZvNmV3F/xRVQWOj2adgQ/vc/d9dqsoMlF5PxQpW/kye7ji0re09nLV/uEoy/a3WTnUIJZsAAaNcOnnoqvO2AA1yLwdyo1cYmU1lyMRktslVR//7wzjuumw9wo1n27Ome1jbZbetW2LbNdUIZcvTRrp6tadPg4jKlY8nFZKxYzVWPOsptCxWTbd4MJ5/sxu0w2WfHDhg1ytWvFPg6fjruONdjQ+iHhMkullxMRiruOYhDD3UDQoV+0RYWwkUXweWXuy8rkx1WrIDeveGOO8LrcnLg3HNdcefMmYGFZsrIkovJOIk+YNe6NcybB3/5S3jdPfe4jgsjn+Y3mef1111/ch99FF4XahE2YULpn+Q3mcGSi8koJX1yu3Fj93Bld9/gCu+95+5svv46dXGa0tu40fUN1q9fuM84EahZ0xWD9e/v1pWlqxgTPEsuJmOUtkuXWrVc8Yl/cKjFi+GQQ9y5TOb47DM4+OCiD8Y2bOi6y3/rLdcjtp8lmOxlycVkhLL2FZaT45qv3nRTeF1BAZx2mnteYtu25MVqSm7rVldp362be0Yp5Igj3AOTr70W+3O3BJOdLLmYjDB7dnL6Crv1Vnj0UTe6Zcj48e5L7fvvy3ZuUzqffeY6Hb3jjvBDkbVrwzXXwKJFro6luM/dEkz2seRiMsLIkcnrK2zoUJdI+vrGPM3Lg44dXU/LJj0KClzrva5dYeHC8PpevVzfcE88UbIfFJZgsoslF1Mu1avnBh27665wB4cFBa4L/4EDYfVqt27s2OR9Uc2Y4c6X6VJ9zaFirrZtXeu90KgetWrBvffCDTfAZZeV7k7VEkz2sORiyq2cHLjqKjeY1P77h9e/9JL74nv11bKPjBgSqjPq3Lls50mHVF7zkiWuKXj//kU7Fj36aFiwAA46CAYNKlsRqCWYLKGqFX7q1KmTmvLtjz9Uhw5Vdb+jw9Mpp6i++KJqw4aq06eX7tzTp5ft+CCUNebI4zdsUB05UrVq1aLvb6NGqk89pVpY6Pa7887kvU/Tp7vzmeAAeRrjezXwL/ZMmCy5VBzvvKO6115FvwBr11a95JLSfdlmY2IJKW3s/uO2b1d96CHVxo2LvqciqsOHq65bl5rYTWaIl1ysWMxUKMce64pnzj03vO6PP+D++92zFv36JV7Uku1DLZemeCl0zS+84DoNbdsWhg2DX38N73PoofD55/Dgg1C/fmpiN1kgVtapSJPduVRMM2eqHnCA7lJUVrWq6sSJ8Y/N5juWSIleS2i/225TPfjgXd+35s1Vn3suXARmyj/szsWYXfXo4fom+8c/XNcjIdu2ucGqjjsOfvxx1+Oy/Y4lUiJ3MFOnuqbddeu6B1W/+CK8rW5duP12+OYbOP10GyXSOJZcTIVWrZprGvvddzBkSNFtU6bAfvvB8OHwww9uXTITSyY1g46VYDZtcs+qHH20Kz70j7VSvbp7PumHH9yw0/4EbUzgRVKZMFmxmAmZPVv1qKN2LfKpVEn1yCNV69VLbmunZBStJbOILnSuZ55RveYa19gh8r2oWVP16qtVV60q++uZ7Ia1FrPkYkpmxgzVrl13/WIF1W7dVJ9/XnXr1rK/TrKbBJfF5s2qkyapdu4c/brr11cdNUr111/L/lqmfLDkYsnFlEJhoeq0aaqtWkX/sm3YUHXECNW5c8v2OsloElxaO3a4hg3Dh7vkEe06W7VSvfde96yQMX7xkovVuRgTg4ib/vgDzj/f1c9UqhTevmaN696kY0fXJPeWW+Crr8LdnSSqLE2CS1P3s3WrG/PmkkugWTPo2dMNEf3bb0X3228/1zz7kUfg0ktht91K9jqmgouVdVI5AacCC4FCIDfK9hZAAXB1jOM7AJ8B84A84BDftuuB74HFwNGJxGN3LiaayDuD6dNVd99d9ZxzVJs1i/4rH1T32cfdCUye7J5cL+3rlXW/kD//VJ0/39199O2rWqtW7Nj32MPVqTz7bOley1QsZFqxGNAG+AswM0ZyeQV4KU5yeQ841ps/DpjpzR8IfAlUA1oBS4BKxcVjycVEivWlGlr//vtuGjxYtUaN2F/WOTmqHTq4HgCeeUZ1wQL3VHtJXzfR7YWFqitWqE6ZonrrraonnOD2jxUfuKfrhw9XHTcu/jVbgjGR4iWXyim4GSqWqn4NIFEaxIvIycAPwMZ4pwDqePN1gZXefF/gBVXdCiwVke+BQ4BPkxK4qRDiFTn5i7AmTYJnnnHD9k6ZAq+84kZTLCgI719Y6J6lmTfP9QIArnitTRvYd19X9NSyJTRtCnvsAXvv7YqhBg6EF1+E3r3dMaquKGvwYLjvPjceypQp8PPPsGyZm5YscV3br19f/DXut5/rYPKkk9yAXR99lPg1l4dne0waxMo66ZiIuHMBauESwW7AaGLfubQBfgKWAyuAvb319wNn+vZ7HBgQ4xwX4orU8lq0aJHEXG6yWVmLprZudS3Nrr3W3bHk5MS/a0hkSsY5GjZUHTBA9f77VRcvTu41m4qLIIrFgKnAgihTX98+kcnlLmCgNx8vudwLnOLNDwSmevMPREkupxQXqxWLGdWSf3kmsv+GDarvvqt6882qJ57oukgpa6IobqpTR/Www1xR3FNPqX79tat3Sdc1m4ojXnIRtz0YIjITl0DyvOWPgObe5nq4Cv+bVfX+iOM2APVUVcWVrW1Q1Toicj2Aqt7h7fcuMFpV4xaL5ebmal5eXvIuzGSd0ra+Ks1xa9e6HgG+/95N+fmwapUr4lq71hWr/fGH64bGr2pV9xR8rVrQqFF42ntvaNXKFa+1aQN77ZVYFyzpvGZTPonIHFXNjboxVtZJx0SMCn0t/s7la6CnN38kMMebb0vRCv0fsAp9U4xMepDRb+pUd94bbkj++TP1mk12IQNbi/UD8oGtwC/Au1H2KZJcgMdCiQg4HJjjJZLPgU6+/W7AtRJbjNeirLjJkkvFlYldsEQ7Xyq6eMm0azbZJ15yCbRYLFNYsVjFNXasG6Y3GcU7M2bA7NmuM8eynidasVOyiqMy8ZpNdopXLGbJBUsuJnMUl0CsvsNkknjJxbp/MSZDJJI4StNVjDFBsORiTAYoyR2JJRiTDSy5GBOw0hR1WYIxmc6SizEBKksdiiUYk8ksuRgTkGRUzluCMZnKWosBIrIa+DHoOEqgIbAm6CACVE6uf88msHETbPijhAdGuf66taFWTVj5S7Kiy1Dl5LMvtUy7/r1VtVG0DZZcspCI5MVq/lcR2PVX3OuvyNcO2XX9VixmjDEm6Sy5GGOMSTpLLtnpkaADCJhdf8VVka8dsuj6rc7FGGNM0tmdizHGmKSz5GKMMSbpLLlkARGpJCJzReStiPVXi4iKSMOgYkuHaNcvIpeKyGIRWSgiY4OML9Uir19EOojIZyIyT0TyROSQoGNMFRFZJiJfha7VW7e7iLwvIt95/9YPOs5UiHHt/yci34jIfBF5TUTqBRxmTJZcssMI3OibO4lIc+Ao4KdAIkqvItcvIr2AvkB7VW0L3BVUYGkS+fmPBcaoagfgZm+5POulqh18z3dcB0xT1f2Bad5yeRV57e8DB6lqe+Bb4PrgQovPkkuGE5FmwPG4kTj9xgEjgXLdIiPG9Q8H/qWqWwFU9dcgYkuHGNevQB1vvi6wMt1xBawv8KQ3/yRwcnChpJeqvqeqO7zFz4BmQcYTjyWXzDcel0QKQytE5CRghap+GVRQaTSeiOsHWgPdReRzEflARDoHEll6jGfX678c+D8RWY67a8vYX69JoMB7IjJHRC701jVR1VUA3r+NA4sutaJdu995wJQ0x5QwSy4ZTEROAH5V1Tm+dTWBG3DFIeVatOv3VAbqA4cC1wCTRETSHV+qxbn+4cAVqtocuAJ4PO3BpU83VT0YOBa4WESOCDqgNIp57SJyA7ADeDao4IpTOegATFzdgJNE5DigOq4o5GmgFfCl933aDPhCRA5R1Z8DizQ1drl+EXkGyAdeVfeQ1iwRKcR16Lc6uFBTItb1n4irhwF4iV2LTMsNVV3p/furiLwGHAL8IiJNVXWViDQFymWxaIxr/1BEhgAnAEdqBj+oaHcuGUxVr1fVZqraEhgETFfVU1S1saq29NbnAweXw8QS6/rPBF4HegOISGugKpnVU2xSxLn+lUAPb7fewHcBhZhSIlJLRGqH5oG/AQuAN4Ah3m5DgMnBRJg6sa5dRI4BrgVOUtVNQcZYHLtzMdloAjBBRBYA24AhmfwLLgUuAO4RkcrAFiBaeXx50AR4zbtDrww8p6r/FZHZuKLQ83GtJU8NMMZUiXXt3wPVgPe9bZ+p6rDgwozNun8xxhiTdFYsZowxJuksuRhjjEk6Sy7GGGOSzpKLMcaYpLPkYowxJuksuRhjjEk6Sy7GGGOSzpKLMRWAiNwnIl+EOvkUkTYi8pCIvCwiw4OOz5Q/llyMKee87kMaA3/H9UmFqn7tPdk9EMiNc7gxpWLJxZgEiMhMETk6Yt3lIvJgnGMKUh/ZLq9ZwxuGoFJonapuBJoCM4F7ffueBHyMG3ALEakqIh963coYUyaWXIxJzPO4ziP9BnnrM8l5uB6j/wytEJEGQE3gD2DnelV9Q1W7AoO95W24RHNaWiM25ZIlF2MS8zJwgohUAxCRlsCewMcicqWILPCmyyMPFJGWXieboeWrRWS0b9s3IvKYd/yzItJHRD7xxog/xNvvTBGZ5Y2n/rD/ziTCYHbtJfhG3KBiC4EDvfP1FJF7ReRh4B3fvq975zCmTCy5GJMAVV0LzAKO8VYNAl4EDgbOBbrgBi+7QEQ6lvD0+wH3AO2BA4AzgMOBq4FRItIGdzfRTVU74O4+dkkAIlIV2EdVl/nWtQS6erF+DbT1rmemql6mqn9X1Qd8p1kAlOeRPU2aWHIxJnH+orFQkdjhwGuqulFVC4BXge4lPO9SVf1KVQtxdxfTvCEEvgJaAkcCnYDZIjLPW94nynkaAusj1v0DuNU7387kEotXnLYtNJaIMaVlFXfGJO514N8icjBQQ1W/SHDY3R0U/SFXPWL7Vt98oW+5EPd/VIAnVfX6Yl5ns//cItIB6A8cLiIPeNu+SiDearhxYowpNbtzMSZB3p3JTNxgZaGK/A+Bk0Wkptfktx/wUcShvwCNRaSBV2dzQglfehowQEQaA4jI7iKyd5T4fgMqiUgowdwJnOgbtfSvFHPn4lX+r1bV7SWM0Zgi7M7FmJJ5Hlf0NQjAu3uZiKuPAXhMVef6D1DV7SJyK/A5sBT4piQvqKqLRORG4D0RyQG2AxcDP0bZ/T3cnUohUEtVp/nO84s3fO7uqrouxsv1omgFvzGlYiNRGlOOeI0JrlTVs0p5/KvA9aq6OLmRmYrGisWMKUe8u6YZcZoqx+S1NnvdEotJBrtzMcYYk3R252KMMSbpLLkYY4xJOksuxhhjks6SizHGmKSz5GKMMSbpLLkYY4xJuv8HdmveoQwnelMAAAAASUVORK5CYII=\n",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
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    "ev_job.plot()"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 122,
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   "metadata": {},
   "outputs": [
    {
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