{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:42.208171Z",
     "start_time": "2020-07-17T05:46:40.705929Z"
    }
   },
   "outputs": [],
   "source": [
    "from pyiron import Project\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:42.965734Z",
     "start_time": "2020-07-17T05:46:42.209701Z"
    }
   },
   "outputs": [],
   "source": [
    "pr = Project('SPX_CHECK_ALL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:42.968689Z",
     "start_time": "2020-07-17T05:46:42.966912Z"
    }
   },
   "outputs": [],
   "source": [
    "a_Fe = 2.83\n",
    "a_Al = 4.024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:46.438112Z",
     "start_time": "2020-07-17T05:46:42.969610Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job spx_Fe_nonmag was saved and received the ID: 1\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_Fe_nonmag')\n",
    "job.structure = pr.create_structure('Fe', 'bcc', a_Fe)\n",
    "job.calc_static()\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:46.493542Z",
     "start_time": "2020-07-17T05:46:46.468312Z"
    }
   },
   "outputs": [],
   "source": [
    "if np.linalg.norm(job['output/generic/forces'])>1.0e-4:\n",
    "    raise ValueError('Forces wrong')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:46.516194Z",
     "start_time": "2020-07-17T05:46:46.494893Z"
    }
   },
   "outputs": [],
   "source": [
    "if np.linalg.norm(job.structure.positions-job['output/generic/positions'][-1])>1.0e-4:\n",
    "    raise ValueError('Positions not correctly parsed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:46.538382Z",
     "start_time": "2020-07-17T05:46:46.517467Z"
    }
   },
   "outputs": [],
   "source": [
    "if np.linalg.norm(job.structure.cell-job['output/generic/cells'][-1])>1.0e-4:\n",
    "    raise ValueError('Cells not correctly parsed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:46.561921Z",
     "start_time": "2020-07-17T05:46:46.540634Z"
    }
   },
   "outputs": [],
   "source": [
    "if 'atom_spins' in job['output/generic/dft'].list_nodes():\n",
    "    raise AssertionError('spins present')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:46.588769Z",
     "start_time": "2020-07-17T05:46:46.563524Z"
    }
   },
   "outputs": [],
   "source": [
    "if np.abs(job['output/generic/volume']-np.linalg.det(job.structure.cell)) > 1.0e-4:\n",
    "    raise ValueError('Volume wrong')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:46.607700Z",
     "start_time": "2020-07-17T05:46:46.589830Z"
    }
   },
   "outputs": [],
   "source": [
    "if np.linalg.norm(job.structure.positions-job['output/generic/positions'][0])>1.0e-4:\n",
    "    raise ValueError('Positions not parsed properly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:51.452912Z",
     "start_time": "2020-07-17T05:46:46.609304Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job spx_Fe_ferro was saved and received the ID: 2\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_Fe_ferro')\n",
    "job.structure = pr.create_structure('Fe', 'bcc', a_Fe)\n",
    "job.structure.set_initial_magnetic_moments([2, 2])\n",
    "job.calc_static()\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:46:51.547233Z",
     "start_time": "2020-07-17T05:46:51.492672Z"
    }
   },
   "outputs": [],
   "source": [
    "if pr.inspect('spx_Fe_ferro')['output/generic/energy_tot'][0]-pr.inspect('spx_Fe_nonmag')['output/generic/energy_tot'][0]>0:\n",
    "    raise ValueError('BCC Fe erromagnetic state has lower energy than nonmagnetic state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:00.787397Z",
     "start_time": "2020-07-17T05:46:51.548600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job spx_Fe_ferro_C was saved and received the ID: 3\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_Fe_ferro_C')\n",
    "job.structure = pr.create_structure('Fe', 'bcc', a_Fe)\n",
    "job.structure.set_initial_magnetic_moments([2, 2])\n",
    "job.structure += pr.create_atoms(elements=['C'], positions=[[0, 0, 0.5*a_Fe]], magmoms=[0])\n",
    "job.calc_static()\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:00.843790Z",
     "start_time": "2020-07-17T05:47:00.821981Z"
    }
   },
   "outputs": [],
   "source": [
    "if np.linalg.norm(job.structure.positions-job['output/generic/positions'][-1])>1.0e-4:\n",
    "    raise ValueError('Positions not correctly parsed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:04.485726Z",
     "start_time": "2020-07-17T05:47:00.844846Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job spx_Al was saved and received the ID: 4\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_Al')\n",
    "job.structure = pr.create_structure('Al', 'fcc', a_Al)\n",
    "job.calc_static()\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:08.940653Z",
     "start_time": "2020-07-17T05:47:04.523612Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:\n",
      "job.input.structure != job.structure. The current job.structure will overwrite any changes you may might have made to job.input.structure in the meantime. To disable this overwrite, set job.input.structure.locked = True. To disable this warning, call job.load_structure_group() after making changes to job.structure.\n",
      "The job spx_Al_restart was saved and received the ID: 5\n"
     ]
    }
   ],
   "source": [
    "job = job.restart()\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:09.238299Z",
     "start_time": "2020-07-17T05:47:08.980361Z"
    }
   },
   "outputs": [],
   "source": [
    "if 'spx_Al_restart' not in list(pr.job_table().job):\n",
    "    raise AssertionError('restart job not found')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:09.310923Z",
     "start_time": "2020-07-17T05:47:09.239289Z"
    }
   },
   "outputs": [],
   "source": [
    "if np.abs(pr.inspect('spx_Al')['output/generic/energy_tot'][-1]-pr.inspect('spx_Al_restart')['output/generic/energy_tot'][-1])>1.0e-3:\n",
    "    raise ValueError('Energy value after restart too different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:15.362892Z",
     "start_time": "2020-07-17T05:47:09.311826Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job spx_Al_minimize was saved and received the ID: 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/pyiron/base/generic/hdfio.py:694: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  np.array([np.array(v) for v in value]),\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_Al_minimize')\n",
    "job.structure = pr.create_structure('Al', 'fcc', a_Al)\n",
    "job.structure.positions[0,0] += 0.01\n",
    "job.calc_minimize()\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:15.427542Z",
     "start_time": "2020-07-17T05:47:15.402604Z"
    }
   },
   "outputs": [],
   "source": [
    "E = job['output/generic/energy_tot']\n",
    "if E[0]-E[1]<0:\n",
    "    raise AssertionError('Energy not decreased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:19.138907Z",
     "start_time": "2020-07-17T05:47:15.428992Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job spx_check_overlap was saved and received the ID: 7\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_check_overlap')\n",
    "job.structure = pr.create_structure('Fe', 'bcc', 2.832)\n",
    "job.set_check_overlap(False)\n",
    "job.calc_static()\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:22.861989Z",
     "start_time": "2020-07-17T05:47:19.178541Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job spx_symmetry was saved and received the ID: 8\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_symmetry')\n",
    "job.structure = pr.create_structure('Fe', 'bcc', 2.832)\n",
    "job.fix_symmetry = False\n",
    "job.calc_static()\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:31.746110Z",
     "start_time": "2020-07-17T05:47:22.907737Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job spx_Fe_ferro_constraint was saved and received the ID: 9\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_Fe_ferro_constraint')\n",
    "job.structure = pr.create_structure('Fe', 'bcc', a_Fe)\n",
    "job.structure.set_initial_magnetic_moments([2, 2])\n",
    "job.fix_spin_constraint = True\n",
    "job.calc_static()\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:31.809375Z",
     "start_time": "2020-07-17T05:47:31.788100Z"
    }
   },
   "outputs": [],
   "source": [
    "if np.linalg.norm(job['output/generic/dft/atom_spins']-job.structure.get_initial_magnetic_moments())>1.0e-4:\n",
    "    raise AssertionError('Magnetic moments either not properly parsed or constraining not working')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:35.491657Z",
     "start_time": "2020-07-17T05:47:31.810415Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job spx_Al_submit was saved and received the ID: 10\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_Al_submit')\n",
    "job.structure = pr.create_structure('Al', 'fcc', a_Al)\n",
    "job.calc_static()\n",
    "# job.server.queue = 'impi_hy'\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:39.231264Z",
     "start_time": "2020-07-17T05:47:35.532094Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job spx_Al_save_memory was saved and received the ID: 11\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_Al_save_memory')\n",
    "job.structure = pr.create_structure('Al', 'fcc', a_Al)\n",
    "job.input['SaveMemory'] = True\n",
    "job.calc_static()\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:47:55.861379Z",
     "start_time": "2020-07-17T05:47:39.270380Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job sxextopt_Al was saved and received the ID: 12\n",
      "The job spx_Al_interactive was saved and received the ID: 13\n",
      "Ending calculation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/pyiron/base/generic/hdfio.py:694: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  np.array([np.array(v) for v in value]),\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/h5io/_h5io.py:610: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return np.array(array_restore)\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'spx_Al_interactive')\n",
    "job.structure = pr.create_structure('Al', 'fcc', a_Al)\n",
    "job.structure.positions[0,0] += 0.01\n",
    "job.server.run_mode.interactive = True\n",
    "job.calc_static()\n",
    "minim = job.create_job(pr.job_type.SxExtOptInteractive, 'sxextopt_Al')\n",
    "minim.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:48:00.053512Z",
     "start_time": "2020-07-17T05:47:55.928137Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job nonmodal2 was saved and received the ID: 14\n"
     ]
    }
   ],
   "source": [
    "job = pr.create_job(pr.job_type.Sphinx, 'nonmodal2')\n",
    "job.structure = pr.create_structure('Al', 'fcc', a_Al)\n",
    "job.calc_static()\n",
    "job.save()\n",
    "job_reload = pr.load(job.job_name)\n",
    "job_reload.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:48:00.122114Z",
     "start_time": "2020-07-17T05:48:00.095619Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.013783, 5.85485 ])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job['output/generic/dft/bands_e_fermi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:48:11.122617Z",
     "start_time": "2020-07-17T05:48:00.123052Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job sxextopt_Fe was saved and received the ID: 15\n",
      "WARNING:\n",
      "job.input.structure != job.structure. The current job.structure will overwrite any changes you may might have made to job.input.structure in the meantime. To disable this overwrite, set job.input.structure.locked = True. To disable this warning, call job.load_structure_group() after making changes to job.structure.\n",
      "The job spx_sxextopt_Fe was saved and received the ID: 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/pyiron/sphinx/interactive.py:134: UserWarning: Spin constraint not set -> set fix_spin_constraint = True\n",
      "  warnings.warn(\"Spin constraint not set -> set fix_spin_constraint = True\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ending calculation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/pyiron/base/generic/hdfio.py:694: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  np.array([np.array(v) for v in value]),\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/h5io/_h5io.py:610: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return np.array(array_restore)\n"
     ]
    }
   ],
   "source": [
    "spx = pr.create_job('Sphinx', 'spx_sxextopt_Fe')\n",
    "spx.structure = pr.create_structure('Fe', 'bcc', 2)\n",
    "spx.structure.set_initial_magnetic_moments([2, 2])\n",
    "spx.server.run_mode.interactive = True\n",
    "spx.calc_static()\n",
    "sxextopt = pr.create_job('SxExtOptInteractive', 'sxextopt_Fe')\n",
    "sxextopt.ref_job = spx\n",
    "\n",
    "sxextopt.save()\n",
    "sxextopt = pr.load('sxextopt_Fe')\n",
    "sxextopt.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:48:11.418036Z",
     "start_time": "2020-07-17T05:48:11.155629Z"
    }
   },
   "outputs": [],
   "source": [
    "if not all(pr.job_table().status=='finished'):\n",
    "    jt = pr.job_table()\n",
    "    print(jt[jt.status!='finished'])\n",
    "    raise AssertionError('Some simulations failed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-17T05:48:16.320813Z",
     "start_time": "2020-07-17T05:48:11.419490Z"
    }
   },
   "outputs": [],
   "source": [
    "pr.remove(enable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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