From 663046134afdbe07e0e06f1f81a039d9dcda2f42 Mon Sep 17 00:00:00 2001 From: liamhuber Date: Fri, 5 Jan 2024 10:14:47 -0800 Subject: [PATCH 01/10] Update extras_require and manually sync notebooks-env to that --- .ci_support/environment-notebooks.yml | 6 +++--- setup.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.ci_support/environment-notebooks.yml b/.ci_support/environment-notebooks.yml index 067848fc..6e6fcd85 100644 --- a/.ci_support/environment-notebooks.yml +++ b/.ci_support/environment-notebooks.yml @@ -2,9 +2,9 @@ channels: - conda-forge dependencies: - ase =3.22.1 - - atomistics =0.1.12 + - atomistics =0.1.20 - lammps - phonopy =2.21.0 - - pyiron_atomistics =0.3.5 + - pyiron_atomistics =0.4.4 - pyiron-data =0.0.24 - - numpy =1.26.0 \ No newline at end of file + - numpy =1.26.3 \ No newline at end of file diff --git a/setup.py b/setup.py index 0a332415..ac6974be 100644 --- a/setup.py +++ b/setup.py @@ -39,10 +39,10 @@ extras_require={ "node_library": [ 'ase==3.22.1', - 'atomistics==0.1.19', - 'numpy==1.26.2', + 'atomistics==0.1.20', + 'numpy==1.26.3', 'phonopy==2.21.0', - 'pyiron_atomistics==0.4.1', + 'pyiron_atomistics==0.4.4', ], }, cmdclass=versioneer.get_cmdclass(), From 6054f24d3819d0ed9e5390e4642afa847973ba26 Mon Sep 17 00:00:00 2001 From: liamhuber Date: Fri, 5 Jan 2024 10:16:42 -0800 Subject: [PATCH 02/10] Update atomistics demo To use the new API --- notebooks/atomistics_nodes.ipynb | 34 ++++++++++++++++---------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/notebooks/atomistics_nodes.ipynb b/notebooks/atomistics_nodes.ipynb index 915ac795..6268eb88 100644 --- a/notebooks/atomistics_nodes.ipynb +++ b/notebooks/atomistics_nodes.ipynb @@ -39,7 +39,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -67,15 +67,15 @@ " structure=wf.structure, \n", " calculator=wf.calculator,\n", ")\n", - "wf.C = wf.elastic.outputs.result_dict[\"C\"]\n", + "wf.C = wf.elastic.outputs.result_dict[\"elastic_matrix\"]\n", "\n", "wf.phonons = wf.create.atomistics.macro.Phonons(\n", " structure=wf.structure, \n", " calculator=wf.calculator,\n", ")\n", "wf.dos_plot = wf.create.plotting.Scatter(\n", - " wf.phonons.outputs.result_dict[1][\"frequency_points\"],\n", - " wf.phonons.outputs.result_dict[1][\"total_dos\"],\n", + " wf.phonons.outputs.result_dict[\"total_dos_dict\"][\"frequency_points\"],\n", + " wf.phonons.outputs.result_dict[\"total_dos_dict\"][\"total_dos\"],\n", ")\n", "\n", "out = wf()" @@ -91,18 +91,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel accumulate_and_run was not connected to ran, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel accumulate_and_run was not connected to ran, andthus could not disconnect from it.\n", " warn(\n", - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel ran was not connected to accumulate_and_run, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel ran was not connected to accumulate_and_run, andthus could not disconnect from it.\n", " warn(\n", - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", " warn(\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -111,7 +111,7 @@ }, { "data": { - "image/png": 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", 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", 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MHSzTAECUrFRvIL7iVQ3D9zA1EIwAyEjxagFutnoD8RXPahi+h/YjGAGQcaJJemT/ktRCNYyzEIwAyChG0mP3TAIj6TFUrgD7l6QeqmGchQRWABkjUtJjQNKKl95W2/mOzuPsX5KaqIZxFoIRABkjUtKjJJ1uOadJFa+put5ruWKjvSOg2vdO6eW6JtW+d6rXSg7EjmoY52CZBkDGMJv0eLqlTQsrD2jp9FGmKzZ8n7axlJNk7R0Bufv30w9vGq3TLW0afFGOCvPJ50lHBCMAMobVZMZndr9v6ryahmY9s/t9S3koiE1veTwEIumHZRoAGcNIejRzqQpI+ujTc6Y+7m/qPoyp+RasIY/HeQhGADhGpJyNrD4u/f3XikMGDtFwSRo8MFunW9rCntN1KQexi0fnVaQelmkAOIKZ8tvqeq8eeqUhLu9nzK58c+zntcHEcg5b0cdHvDqvIrUQjABIe2Z6h0gKeU60Cv8c6Lj79zMVjNB8Kz7i2XkVqYNgBEBaizRt75L04KZDklxxC0Qk6R+/NUZ9+/ZRs+9TDR7YT39qaaP5VhLQedWZCEYApDUz0/bN/ta4v+8P/r/9+qStvddzaL4Vf3RedSYSWAGkNbum4yMFIhLNtxKBzqvOxMwIgLSWatPxgwdm6++/XkLzrQQyOq92T1gupMlc2iIYAZDWzEzbF+TnSHLpj/7Q58TT6ZZzKszPpZIjwWaVejSjuJCdlB2CYARAWjOm7RdWHpBLCgo2jMvSg98okaSQ5yQClRzJkdXHRdDnEOSMAEh7ZjZMC3eOx52rH9xQFNfxpNrSEZDqmBkBkJLaOwLa23hazb5PI26CZnbDtN6m9q8ZPkiLnz+oWBt3eqjkACwjGAGQFEZwYWZ9P1Q3VUOorqpWNkwLN7U/Z8wwrZZLdz13INpPUS5RyQFEwxUIBFK+gb/f75fb7ZbP51N+fr7dwwFgkZlW7V3PjdQp1SX12lXVCAWiKasNN9ZvXOPR+p2NkkLnnHxuQLYqvvVFKjmALsxevwlGACRUuOAiVMDQ3hHQlEe29drEzHitUSHT7A99rtH8ateyqZZnKsLN4oQKVAb1z9Ydk0dq8dRRzIgA3Zi9frNMAyBhzLRqX7m5QTOKC5XVxxWxm2rX10bqqmpsmPar3Y363uQiS4FCuKUcykmBxCAYAZAwVndYTURJ7EOvHNa/7mqMWzMsykmdy0peE+KLYARAwljdYTVRJbFdd+8lpwOhWMlrQvxZ6jNSUVGhL33pS8rLy9PQoUM1b948HTlypNfXbN++XS6Xq8fj97//fUwDB5D63j/ZYuo8IwiZcNnnNHhgv4jnuyQV5l8o3zVz32osE63c3KD2WGt34ThGXlP3WTwjiK2u99o0ssxhKRjZsWOHFi1apD179qimpkbnz5/XzJkz1dIS+Q/OkSNH5PV6Ox+jRo2KetAAUl91vVePbz3a6zkufdaXo7reqxsffV2nW9pMffwHv1GiB78ResO0ULouCQGGSHlNEkFsMlhapqmurg56/swzz2jo0KHav3+/brjhhl5fO3ToUA0aNMjyAAGkH+MPvBkPzC1WTUNzxHJeQ/ep81AbpvWGVu3pL565HVbzmpAYMeWM+Hw+SdLgwZG7DY4bN05nz55VcXGxfvzjH+urX/1qLG8NIIWZrYpZOv0LmlFcqCmPbOs1EMnLzdLKuaXyDOoftqvqr3Y36qFXDkd8T1q1p7d453ZYzWtCYkS9N00gEFB5ebmmTJmi0tLSsOd5PB6tX79eVVVVevHFFzV69GhNmzZNO3fuDPua1tZW+f3+oAeA9GH2D/epllb9andjxMDlzNl2eQb1V9kVQ8J2Vf3e5CJ53OFzSLouCSE9JSK3w2xwShCbWFHPjCxevFhvvfWWdu3a1et5o0eP1ujRozufl5WV6fjx43rsscfCLu1UVFRo5cqV0Q4NgM3MJq4+W/uB6Y8ZKcAxs3svrdrTl9WeNWZdWzRYHneumn1nQ35so3keQWxiRTUzcvfdd2vTpk16/fXXNXz4cMuvnzRpko4eDZ/Ytnz5cvl8vs7H8ePHoxkmABuYSVyNhpk7UzO79yI9WcntsMIIYqWeidAEscljaWYkEAjo7rvv1ksvvaTt27erqCi6bbcPHjwojyf8H4WcnBzl5ORE9bEB2MdK4qpZxp3phMs+p9r3TkVMWqRLqjMlMrfDCGK756IU0mckaSwFI4sWLdJzzz2nl19+WXl5eWpubpYkud1u9e/fX9KFWY2mpiY9++yzkqRVq1Zp5MiRKikpUVtbmyorK1VVVaWqqqo4fyoA7GY2cdUsI3z4xjUe3fjo66aTFrt2STUqL5p9n+p0S5sGX3ShPwkBSnpJdG4HQay9LAUja9eulSR95StfCTr+zDPP6Hvf+54kyev16tixY53/19bWpvvuu09NTU3q37+/SkpK9Morr2jOnDmxjRxAyol3xcGgAdn6q4nDtX5nY4/1fDNdVUNVXhjorplekpHbQat/+7BrL4C4eWLrO3HNFynI6yeXq09UO/OG2y24++vJJUkfxvdUCp2gzPcy9Zi9fkdd2gsAXSUicfWPZ9rCBiJS+KTF3iovuqO7ZvogQdm52CgPQMwSkbhqRfflIbO5K3TXTD/kdjgTwQiAmJm9+N87/QuSAnGfQemetGg1d4XumumF3A7nIRgBEDOzF/ORFw+w9HEL83MkufRHv7WkRasVFXTXBOxFzgiAmFkpu7Ry4e9tZ97eGlIZlReR0CIeSA0EIwBiZlz8zewLE+lcSerjktb89YWExGiSFo2ummayCOiumT7aOwKqfe+UXq5rUu17p0g8dhBKewHEhZWyy3DnGtb89TjNGTMs6FiobeMl9ZrISJ8R54j3br1IDrPXb4IRAHFj5YIR68XF7OvpwJr+wvWMob9I6iMYAWCLUDMY4S76Vs7tiotT5mjvCGjKI9vCVmv11vgO9jN7/aaaBshg0QYD8fo40ZRoJmoreaQmK7v1Uu6bvghGgAzU3hHQ6m3v6pndjfro03OdxwcPzNY3x35e04sLLc1SJHMtf88fTnFxyiCJ3K0XqYNgBMgw1fVe3f/i2/rok3M9/u90yzlt2P2+Nux+31RAEW65xMwmdlGPveptU+dycXKGRO/WG068Zg1hDsEIkEGq671a8Ocqlki8vrNaUHkgZGWLlPzlEjMb33VFIzNnSMZuvd1RuZN89BkBMkS0+8csfv6gtrzl7XHcylp+rKxsfEcjM2cxesZI1hrfRcsIerv/bBuzfdX1PX8XEDuCESBDmN0/pruOgHTXcz3/CG9taDb1+ngsl1gdO43MnCVZu/VGmu2T2OU5UVimATJErEHBg5sOdS65VNd7tWH3+6ZeF4/lErNjHzQgWw9/64tMpTtQMnbrpXLHPgQjQIaINSho9rdq9bZ3tXjqlaaWe+K5lm927L+4Zbwmj7o45vdDakr0br1U7tiHYATIEJESAc14fOs7kgKmlkwCit9yidkkxkncrSIGdlXugJwRIGP0lghoxTMml2e+P3lk3JZLkp3EiMxkZcNHxBfBCJBBZhQXaun0L8jdPzvoeP/sPqYDlK5N0iK9VzwlK4kRmYug1z7sTQNkiFC9Ewb1z9Ydk0dq8dRR+r/1zbrrOXM9SAb1z5bv03O9Lpkkaq8QmlEh0egzEj/sTQOgU7iGYb5Pz2nV1qMaXZinOWM8uvfEKD2+9WjEj3fH5CKt2vqOXFLQx0zG3WOikxiBZFTuIBjLNIDDWemdsHjqKBXmh0/OM9bMF0+9kiUTOJoR9N489vMqu2IIgUiCMTMCOJzV3gkPfqNYC//cMr63WQ/uHpEMLMtlBoIRwOGs9k4wEkW7r5m7/5xf0jUxlSUTJBK5G5mDZRrA4aLpnTCr1KNdy6bq3ulf0KA/V9589Ok5Pb71qKY8so39OZBw7BGTWQhGAIe7tmiwqTyQ7r0TahqatWrrOz1KebkYINHYIybzEIwADlfT0Kyz59tD/l+46hcuBrBTMneERmogGAEczJjq/uiT0I3KBg3IDln9YvZi8HjNO6p97xRBCeKKPWIyDwmsgEP1NrthyOnbJ2SnVLN/5Fe//q5Wv/4uSYWIq1TaI4ZqnuQgGAEcKtLshnRhJ95Q26Fb/SNv5JHQYwTxYHZjxETvEUM1T/KwTAM4VCxT3ZE2DOuOPBLEUyrsEUM1T3IRjAAOFctUdzQ7/JJUiHiyc2NEEriTz1IwUlFRoS996UvKy8vT0KFDNW/ePB05ciTi63bs2KEJEyYoNzdXl19+udatWxf1gAGYE+t26OEuBpGQVIh4MfrdPH/nJD3xnbF6/s5J2rVsasKXSKjmST5LwciOHTu0aNEi7dmzRzU1NTp//rxmzpyplpaWsK9pbGzUnDlzdP311+vgwYNasWKF7rnnHlVVVcU8eADhxWOqu+vFYPFXrzD1vslIKkx17R0B1b53Si/XNVFtFCM79oihmif5LCWwVldXBz1/5plnNHToUO3fv1833HBDyNesW7dOl156qVatWiVJuvrqq7Vv3z499thjmj9/fnSjBmBKuNbuhRaS8IyLwbVFg1V1oMn2pMJUR9Jj+kulap5MEVM1jc/nkyQNHhz+j09tba1mzpwZdOymm27Shg0bdO7cOWVnZ/d4TWtrq1pbWzuf+/3+WIYJZLR4bWhnzLQsrDwgl3rfRC9TGUmP3YM1qo3SS6pU82SSqBNYA4GAysvLNWXKFJWWloY9r7m5WQUFBUHHCgoKdP78eZ08eTLkayoqKuR2uzsfI0aMiHaYABS/qW47kwpTHUmPzpEK1TyZJuqZkcWLF+utt97Srl27Ip7rcgV/wwKBQMjjhuXLl6u8vLzzud/vJyABUkS8ZlqcxkrSIzsdR2Z3s7F4LHHCvKiCkbvvvlubNm3Szp07NXz48F7PLSwsVHNzc9CxEydOqG/fvhoyJPQvZE5OjnJycqIZGoAE6n6B+PqYYRkfhBhIeoyfVMm7IfBOHkvBSCAQ0N13362XXnpJ27dvV1FRUcTXlJWVafPmzUHHXn31VU2cODFkvgiA1JQqF4hURdJjfKRa3o2xxInEspQzsmjRIlVWVuq5555TXl6empub1dzcrE8//bTznOXLl+v222/vfL5gwQJ98MEHKi8v1+HDh/XLX/5SGzZs0H333Re/zwJAQtGNMrJY+7qAvJtMZikYWbt2rXw+n77yla/I4/F0Pl544YXOc7xer44dO9b5vKioSFu2bNH27ds1duxYPfTQQ3ryyScp6wXSBBcIc0h6jB3NxjKX5WWaSH71q1/1OHbjjTfqwIEDVt4KQIogMdM8kh5jQ95N5mLXXgC94gJhDUmP0SPvJnMRjADoFRcI60h6jA7NxjIXu/YC6BWJmUgW8m4yF8EIgF5xgUAy0eU3M7kCZrJSbeb3++V2u+Xz+ZSfn2/3cICMRJ8RJJPdHVhTfTzpwuz1m2AEgGn8QUYmIhCPHsEIAAAxCtcR1gjBWTrqndnrNzkjAACEQMO/5CEYAQAgBDrCJg99RgAApmRazhAN/5KHYAQAEFEmJnHS8C95WKYBABu1dwRU+94pvVzXpNr3TqVk/kGm7tpMw7/kYWYEAGySDrMNZpI4H9x0SDOKCx23ZGM0/FtYeUAuKehrQMO/+GJmBAASpLdZj3SZbYiUxClJzf5Wrd72bpJGlFx0hE0OZkYAIAF6m/WYUVyYNrMNZpMzH9/6jkYXXuTIizM7MSceMyMAEGeRZj1WbzuaNrMNVpIzndxzw9iJ+eaxn1fZFUMIROKMYAQA4shMjsUzu9839bEe3/qO7cs1RhKnGfTcQLRYpgGAODLTKOujT8+Z/ngrXnpbLa3t+uiTNg2+KEeF+cldIjCSOBdUHjB1Pj03EA2CEQCII7MX40H9s00FJadbzul///ubQceSXXEzo7hQfzF+uP7jwH9HPJeeG4gGyzQAEEdmL8Z3TC6K+j28Say4qa73asoj2yIGIvTcQCyYGQEcJtNadqcaI8ei2Xc2ZN6ISxfKQhdPvVJSQI9vPRrV+wR0YQln6lUF6tc3MfeV4Xas7S6Tem7w+5UYBCOAg6RDEy2nM9soS5ImXjZY7v7Z8lnIIenqdMs5Tap4TT//Zmncv7+9JeJ2V5ghP2P8fiWOKxAIpHwdlt/vl9vtls/nU35+vt3DAVJSuLtY4wJIg6bk6u3CJanH/8XCJekXfz1OnxuYoxNnzurigTmSSzr5cWvUd++1753SLU/viXje33/tan1vcpHjZwf4/YqO2es3MyOAA0QqJ3XpwsUvFZpoZYpwjbJqGppNLX1YEZC0+PmDCtfiI5q7d7OJuBfn5Tj+Z4rfr8QjgRVwADPlpPSASL7ujbIkmV76sKq3XmPRtJhnx9rP8PuVeAQjgAOYvYulB4Q9jD1qHq85ErelGSuMOMVKh1R2rP0Mv1+JxzIN4ADcxaauULkjduh6927M0kjhq0PYsfYz/H4lHsEI4ABmy0kz4S42lZgtjU2mrnfvkapDjB1ru5/TtXomE0pd+f1KPIIRwAG4i009VkpjDS5J7gHZyu2bpWZ/cIDwjWs8empnY8zjMu7ewwVKRn6JUR3S2461mVLqyu9X4hGMAA5h5i4WyRMp6TGch7/1xbAX/wH9+kbdJE26cOH8rz+c0oTLPmepOsRIxO3KbDDjFPx+JRZ9RgCHyYRp83Twcl2TlmysM31+H5e0+pbxmjMm/EWtvSOgyQ9vC5o1icbAnCy1tLZHPO/5Oyf1CEKMcUx5ZFvYYMtYtti1bKrjfvb4/bKGPiNAhgp1F4vks5rM2BGQPjewX6/nZPVx6cFvmN9BNxwzgYgUvjrESqmr034W+f1KDEp7ASABIpXGhmKmNHRWqUf3Th8V/cAsCBdQWS11NUqbX65rUu17p0yXFyNzWA5Gdu7cqblz52rYsGFyuVz6zW9+0+v527dvl8vl6vH4/e9/H+2YAYTAH/zUYiQ9WmF2NmXx1FEqzE9cGWmkHiJmx/n+yU86d/295ek9WrKxTrc8vUdTHtmWlB2HkT4sL9O0tLTommuu0R133KH58+ebft2RI0eC1osuueQSq28NIIxMqWpIN0bS44ObDqnZ3xr2PKulocZyzcI/L9fEM+w0Ux0SqdTV8PjWd0Ied2qSK6JneWZk9uzZ+ulPf6pvfetbll43dOhQFRYWdj6ysrKsvjWAEIyqhu5r+NG0AEf8zSr1aPf903Tv9C+E/P9oS0ONQKfQHTxLUZifo0EDsqMdrgrduRGDBGPWJ9ogKJqOsHC2pCWwjhs3TmfPnlVxcbF+/OMf66tf/WrYc1tbW9Xa+tldhN/vT8YQgbTDBl7pIauPS0umj9LowoviWhra22Z8VpNcB+Zk6X9df7kWTx3V+bPSW+WIkbsSbamxE5JcqayJn4QHIx6PR+vXr9eECRPU2tqqX//615o2bZq2b9+uG264IeRrKioqtHLlykQPDUh7mVzVkI56ayAWrVDVHbNKPVp363jd/+Lb+uiTc6Y+Tktru1ZtParRhXmaVeoxtfQ38uKBUY/bkK77ubA0Gl8x9RlxuVx66aWXNG/ePEuvmzt3rlwulzZt2hTy/0PNjIwYMYI+I0A3ZntZPPGdsbp57OcTPyCklPaOgJb9x1v6jwP/bfo1g/pn63vXjdQTrx3tMeNmhEzGMk7te6d0y9N7YhpjuF4mqSxcw7fuXx+Y7zNiS2nvpEmTdPRo+Km9nJwc5efnBz0A9MQGXuhNVh+XHvmLMZYqbz769JxWhQhEpJ65HtGULxvSddffSEujErkw0bAlGDl48KA8HqJGIFZs845IjMqbeGUydF3661q+3NvH7/5/6byfi5WlUZhnORj5+OOPVVdXp7q6OklSY2Oj6urqdOzYMUnS8uXLdfvtt3eev2rVKv3mN7/R0aNHdejQIS1fvlxVVVVavHhxfD4DIIP1djFI5z/4iC+j8mbwwOirbLozcj3CVfUYBg3IlrtbdY+Zip1UZbXhG8yxnMC6b9++oEqY8vJySdJ3v/td/epXv5LX6+0MTCSpra1N9913n5qamtS/f3+VlJTolVde0Zw5c+IwfABs4AUzZpV6NPWqAk2qeE2nW9pi/nhdl/5mlXrU0SHd9VzPCh7fJ+cUkHTv9FEaefHAtK86YWk0MdgoD3AIygxhhpF8KUXXLC3UJniRNs6TLvQ/2X3/tLT/mTQ+13AN35y8SWA0UjqBFUBkVtu7GyWeN4/9vMquGMIfQoQUaVmlN+GW/iLlUUhSs79Vq7e9a/k9Uw1Lo4nBrr1ACqKHARLJ6Hey571TWvTcAX30qbleJOGW/szmRzy+9R2NLrwo7X+GWRqNP5ZpgBSz5S1vyLV3w73TRwV1yQRiEW7ZxvXn52ZyPaz0G/E4aAmDpdHIzF6/CUaAFLLlrQ+1+PmDitSiwN0/W9+fPJKgBHER60ycmZyRrtKx0RmiQzACpJnqeq/l/UQGDcjWw9/6ItPCiFmsd/lWfn7pCJw5SGAF0ojR1dGqjz45pwWVB7TlrQ8TMCpkklgToI2N88yg7BXdEYwAKcBMNUJvFj9/UFve8sZxRIB1i6eO6rX1PB2BEQ7BCJACYu3W2BG40HCqup6ABPbp2nqesldYQTACpIB4TVuzQRfsFq6PSTq3gI/Eak8g9ESfESAFGBvehevqaJaxQReVCrCT0cckE8pe6QkUH8yMACnA7O6nZjT7Po19QMhY8brLz4SOwEaPlu75Xs2+s1pYybKpFcyMACkiXFfHQQOy1Xa+Q5+0tZv6OA+9clj9+2VxVwbLuMs3z6iACxWqBXThpmLl5gbNKC50ZCAWb/QZAVJMqH4PkvTka0f15GtHIy7jGH/2nLo+j8Qw7vK7/3zx8xSa2a6zmd7gzez1m5kRIMUY09vd3TvjCxpdkNdrq3iJuzJYl0p3+enSYt1sBVyslXKZgmAESCNzxni0rs94rXjpbZ1uCb+5WUAks8K8SH1ukvXzlE7LRGYr4GjwZg4JrECamVXq0d9/vcTUudyVwYxUuMtPt2RQowIu3JwNDd6sIRgB0lBvXS674q4MZiTrLj9cpU6kZSIp9Xro9FYBR4M361imAdJQpL4kLl1oMsVdGcxIxs9Tb0sw7v79UmKZyKpwFXCFKbq0lMoIRoA0ZNyVLaw8IJcUdAHhrgxWJfrnKVyljrEE8/3JI019nFRcdsykBm+JxDINkKYyse02EidRP09mlmBeqmsy9bFSddkxExq8JRozI0Aa464M8ZSInyczlTqnW85p8MB++lNLG8uOGYpgBEhz4fqSANGI98+T2aWVeWOH6Znd77PsmKFYpgEAJIzZpZUZxYUsO2YwZkaAFJEunScBK6xU6mT1cbHsmKEIRoAUkE6dJwErzFbqSBf2ezGCkK+PGUYQkkHYKA+wGRuUIROECrgH9c/WHZNHatTQPD30CsG4E5m9fhOMADZq7whoyiPbwlYbGFPYu5ZN5S4Raa+9I6DV297VM7sb9dGn4fdWktIzGGeptSd27QXSQKpsUAYkQ01Ds1ZtfSdk7kh36bb7NEutsaGaBrDR1oZmU+elYudJwIremp+F0zUYT2XptslfKiIYAWxSXe/Vht3vmzo3VTtPAmZFmgXsTSoH4+m4yV8qIhgBbGD8AYuEbcjhFLEEFKkcjFtZakV45IwANjB7lxgQnSfhDNEEFOnQBt5skJXKszupgJkRwAbNfnN/mL4/eSTJb3AEo/mZ2bA6XdrAmw2yUnl2JxVYDkZ27typuXPnatiwYXK5XPrNb34T8TU7duzQhAkTlJubq8svv1zr1q2LZqyAI1TXe/XQ/zlk6twZxYUJHg2QHEbzM0mmApJ0aQMfKchiqdUcy8FIS0uLrrnmGq1evdrU+Y2NjZozZ46uv/56HTx4UCtWrNA999yjqqoqy4MF0p2RdX+6JXKPBf6AwWlmlXpC7j9jGDwwW387eaSev3OSdi2bmvKBiNR7kJUuszupwHLOyOzZszV79mzT569bt06XXnqpVq1aJUm6+uqrtW/fPj322GOaP3++1bcH0pbZ0kb+gMHJZpV61NEh3fXcgR7/96eWc/rl7vf1pTRrFmYEWd37jBTSZ8S0hCew1tbWaubMmUHHbrrpJm3YsEHnzp1TdnZ2j9e0traqtbW187nf70/0MIGEM5u0OnhgP/3sm6X8AYMjtXcE9NAroSvJ0q3RWVezSj1s8heDhCewNjc3q6CgIOhYQUGBzp8/r5MnT4Z8TUVFhdxud+djxIgRiR4mkHBmG5z9+GtXE4jAsZxcCpvVx6WyK4bo5rGfV9kVQwhELEhKNY3LFfwNMbbD6X7csHz5cvl8vs7H8ePHEz5GIJGsNDgrdPdP7GAAG1EKi1ASvkxTWFio5ubgO8ITJ06ob9++GjIk9F4bOTk5ysnJSfTQgKSw0uAs1XsqALGiFBahJHxmpKysTDU1NUHHXn31VU2cODFkvgjgNDQ4Az5DKSxCsRyMfPzxx6qrq1NdXZ2kC6W7dXV1OnbsmKQLSyy333575/kLFizQBx98oPLych0+fFi//OUvtWHDBt13333x+QyAFGc2V4QGZ8gElMIiFMvByL59+zRu3DiNGzdOklReXq5x48bpH/7hHyRJXq+3MzCRpKKiIm3ZskXbt2/X2LFj9dBDD+nJJ5+krBcZwUquCA3OkCnC9RtJl0ZniD9XwMgmTWF+v19ut1s+n0/5+fl2Dwcwpb0joCmPbIu4RGPkiuxaNpW7QWSU9o4ApbAOZ/b6zUZ5QIKQKwL0ziiFBQhGgAQhVwTITMz4WEcwAiQAuSJAZqqu9/ZoC++hLXxESWl6BmSStvMdWvFSfcTzKGEEnMXYCLP78myz76wWVh5Qdb3XppGlPoIRII6q672aVLFVp1vaIp5LrgjgHL1thGkcW7m5Qe0dKV8zYguCESBOjLui0y3nTJ1PrgjgHE7ecycZCEaAOOjtrigcckUA52DPndiQwArEgdkyXok9aAAnYs+d2DAzAsSB2TJeA7kigLOw505sCEaAGFkp4x0ysB/trgEHYs+d2BCMADFo7wjowU0Nps4dPDBbtcunEYgADsWeO9EjZwSIweptR9XsN5cr8vNvflH9+hL/A042q9SjGcWFdGC1iGAEiFJ1vVePbz1q6lzKeIHMwZ471nGbBkTByvKMRBkvAPSGYASIgpXlGTLoAaB3LNMAFrR3BLR627uml2ckMugBIBKCEcCk6nqvHtx0SM3+VtOvuXf6F8gVAYAICEYAE4x9Z6y0ey/Mz9HiqVcmbEwA4BTkjAARGMmqVvfafPAbJSzPAIAJBCNABFaSVQ0szwCAeQQjQC+s9BIxsDwDANYQjABhWO0lIl3Yg4LlGQCwhgRWIAyryzMed64emFvM8gwAWEQwAoRgdXnm3umjtHjqKGZEAARp7wiwT40JBCNAN1aXZ+6d/gUtmT4qgSMCkI6q671aublBXt9nM6zMoIZGzgjQjZXlGZJVAYRi9CbqGohIUrPvrBZWHlB1vdemkaUmghHgz9o7Anpi61FLyzMkqwLorr0joJWbQ/cmMo6t3Nyg9g6r3Yuci2UaQLR6BxA/extP95gR6Sogyes7q72Np1V2xZDkDSyFEYwg4215y6u7njtg6TUszwAI58QZc8u8Zs/LBAQjyGhb3vpQi58/aPl1LM8ACGdoXm5cz8sE5IwgY1XXe3XXcwdlddmW5RkAvbm2aLA87lyFu11x6UJVzbVFg5M5rJRGMIKMFE13VYnlGQCRZfVx6YG5xZLUIyAxnj8wt5jZ1S4IRpCRotn8jlbvAMyaVerR2lvHq9AdvBRT6M7V2lvHM7vaTVQ5I2vWrNGjjz4qr9erkpISrVq1Stdff33Ic7dv366vfvWrPY4fPnxYV111VTRvD8Rky1vWN7+jUREAq2aVejSjuJAOrCZYDkZeeOEFLV26VGvWrNHkyZP11FNPafbs2WpoaNCll14a9nVHjhxRfn5+5/NLLrkkuhEDMYgmYZVW7wCildXHRfmuCa5AIGApfe/LX/6yxo8fr7Vr13Yeu/rqqzVv3jxVVFT0ON+YGfnTn/6kQYMGRTVIv98vt9stn88XFNAAZrV3BLR627t6fOs7pl/TxyWtvmW85oxhNgQAomH2+m0pZ6StrU379+/XzJkzg47PnDlTv/vd73p97bhx4+TxeDRt2jS9/vrrvZ7b2toqv98f9ACiVV3v1eSHX7MUiEjS6lvGEYgAQBJYCkZOnjyp9vZ2FRQUBB0vKChQc3NzyNd4PB6tX79eVVVVevHFFzV69GhNmzZNO3fuDPs+FRUVcrvdnY8RI0ZYGSbQactbXi2oPGCps6p0oXx3zphhCRoVAKCrqBJYXa7gtfNAINDjmGH06NEaPXp05/OysjIdP35cjz32mG644YaQr1m+fLnKy8s7n/v9fgISWBZtQzPKdwEguSzNjFx88cXKysrqMQty4sSJHrMlvZk0aZKOHg1fzZCTk6P8/PygB2DFhRbv1huaSZTvAkCyWQpG+vXrpwkTJqimpiboeE1Nja677jrTH+fgwYPyeFiLR2JcmBGxtteMdCFhdc1fU/8PAMlmeZmmvLxct912myZOnKiysjKtX79ex44d04IFCyRdWGJpamrSs88+K0latWqVRo4cqZKSErW1tamyslJVVVWqqqqK72cC6LMZkWiQsAoA9rAcjHz729/WqVOn9JOf/ERer1elpaXasmWLLrvsMkmS1+vVsWPHOs9va2vTfffdp6amJvXv318lJSV65ZVXNGfOnPh9FoCizxGhoRkA2MtynxE70GcEvYmmh4iBhmYAkDhmr99RVdMAqcAIQn656w/ynT1v6bU0NAOA1EEwgrRUXe/V/S++rY8+ORfV68kPAYDUQTCCtHMhSdV6tYzEjAgApCKCEaSVaJNUDcyIAEDqIRhB2oilbJcZEQBIXQQjSGntHQHtbTyt/3vIq3+r/SDqj8OMCACkLoIRpKzqeq9Wbm6Q13c26o9BDxEASH0EI0hJsSSpGughAgDpgWAEKaW9I6AnXzuqJ18Lv5FiJJ8bkK2Kb32R2RAASBMEI0gZsfYOcUlaMm2U7p7GbAgApBOCEaSEeCzL/OKvx2nOmGFxGhEAIFkIRmCbeFXKULYLAOmNYAS2iEeljIGyXQBIbwQjSLp4LMlIlO0CgFMQjCBp4lEp45L0vetGamZJoa4tGkyiKgA4AMEIEq69I6DV297VUzvf0ydt7TF9LJJUAcB5CEaQEEZyak1Ds/7/ff+tj1vPx/Tx6B0CAM5FMIK4MmZBntndqI8+ja5fSFf0DgEA5yMYQVzEcymmK5ZlADiBMVt84sxZDc3LJeetG4IRxCRRQQiVMgCcIlQrA/7GBSMYgWXxzgcxUCkDwGmq671aWHlAgW7Hm31ntbDygNbeOp6ARAQjsCDe+SDdsSQDwEnaOwJaubmhRyAiSQFduAFbublBM4oLM/7mi2AEESVqKcZApQwAJ9rbeLrXLtMBSV7fWe1tPK2yK4Ykb2ApiGAEYSU6CBnYL0v/64bLtXgqlTIAnOfEGXPbXZg9z8kIRiDpszyQZt+nOt3Spv/+6FP9exzzQboa1D9bd0weSRACwNGG5uXG9TwnIxjJcInOAzFclJOlb08coenFJKcCyAzXFg2Wx52rZt/ZkHkjLkmF7gtlvpmOYCRDJXoJxsBSDIBMldXHpQfmFmth5QG5pKCAxPhr+MDcYv42imAkIyRzCcZAEAIA0qxSj9beOr5Hn5FC+owEIRhxoK7Bx+53T6rm8An5ErgE0xX5IAAQbFapRzOKC+nA2guCkTQXatbj5boPdbqlLWljIB8EAHqX1ceV8eW7vSEYSQPdA47BF+Vo6EU5euP90/rV795PaOJpb1iKAQDEQ8YGI103Lbp4YI7kkk74z+p0S5sGDeinjz757F/j4m/HOcleZjGDIAQAohPu5jLUdSHZ157CfPuWj6IKRtasWaNHH31UXq9XJSUlWrVqla6//vqw5+/YsUPl5eU6dOiQhg0bph/+8IdasGBB1IOOVahNixAZ+SAAEL10uPbYtYGf5WDkhRde0NKlS7VmzRpNnjxZTz31lGbPnq2GhgZdeumlPc5vbGzUnDlzdOedd6qyslK7d+/WXXfdpUsuuUTz58+PyydhRbhNixDa4IHZ+ubYz5MPAgAxSJdrj9emDfxcgUDA0tfmy1/+ssaPH6+1a9d2Hrv66qs1b948VVRU9Dh/2bJl2rRpkw4fPtx5bMGCBXrzzTdVW1tr6j39fr/cbrd8Pp/y8/OtDDdIe0dAUx7ZltJRqd0GZPfRnC96NHnUJbZO2QGAU6TbtcdoxrZr2dSY//6bvX5bmhlpa2vT/v37df/99wcdnzlzpn73u9+FfE1tba1mzpwZdOymm27Shg0bdO7cOWVnZ/d4TWtrq1pbW4M+mXiItGlRJmMJBgASI92uPXZs4GcpGDl58qTa29tVUFAQdLygoEDNzc0hX9Pc3Bzy/PPnz+vkyZPyeHpOA1VUVGjlypVWhmYKmxEFYwkGABIvXa89yRx3VAmsLlfwRSsQCPQ4Fun8UMcNy5cvV3l5eedzv9+vESNGRDPUIJm+GZE7t69mFBewBAMASZSu155kjttSMHLxxRcrKyurxyzIiRMnesx+GAoLC0Oe37dvXw0ZEnr6JycnRzk5OVaGZkqkTYucZPDAbN18zTAN/9wA20u2ACCTGdeedFmqsWMDP0vBSL9+/TRhwgTV1NTom9/8Zufxmpoa3XzzzSFfU1ZWps2bNwcde/XVVzVx4sSQ+SKJ1NumRemOWQ8ASE1drz3pct1J9gZ+lqtpXnjhBd12221at26dysrKtH79ej399NM6dOiQLrvsMi1fvlxNTU169tlnJV0o7S0tLdUPfvAD3XnnnaqtrdWCBQv0/PPPmy7tjVc1jSEdar1D6RpwGA1sTn7cyj4HAJAG0uHaE+8+IwmpppGkb3/72zp16pR+8pOfyOv1qrS0VFu2bNFll10mSfJ6vTp27Fjn+UVFRdqyZYvuvfde/eIXv9CwYcP05JNP2tJjxNB906JU7sBqdOhjtgMA0lvXaw8dWINZnhmxQ7xnRgAAQOKZvX73SeKYAAAAeiAYAQAAtiIYAQAAtiIYAQAAtiIYAQAAtiIYAQAAtiIYAQAAtiIYAQAAtiIYAQAAtrLcDt4ORpNYv99v80gAAIBZxnU7UrP3tAhGzpw5I0kaMWKEzSMBAABWnTlzRm63O+z/p8XeNB0dHfrwww+Vl5cnlyu9Norz+/0aMWKEjh8/zr46CcbXOrn4eicPX+vk4WsdX4FAQGfOnNGwYcPUp0/4zJC0mBnp06ePhg8fbvcwYpKfn88PdpLwtU4uvt7Jw9c6efhax09vMyIGElgBAICtCEYAAICtCEYSLCcnRw888IBycnLsHorj8bVOLr7eycPXOnn4WtsjLRJYAQCAczEzAgAAbEUwAgAAbEUwAgAAbEUwAgAAbEUwkmBr1qxRUVGRcnNzNWHCBP3nf/6n3UNynIqKCn3pS19SXl6ehg4dqnnz5unIkSN2DysjVFRUyOVyaenSpXYPxZGampp06623asiQIRowYIDGjh2r/fv32z0sRzp//rx+/OMfq6ioSP3799fll1+un/zkJ+ro6LB7aBmBYCSBXnjhBS1dulQ/+tGPdPDgQV1//fWaPXu2jh07ZvfQHGXHjh1atGiR9uzZo5qaGp0/f14zZ85US0uL3UNztDfeeEPr16/XmDFj7B6KI/3pT3/S5MmTlZ2drd/+9rdqaGjQP/3TP2nQoEF2D82RHnnkEa1bt06rV6/W4cOH9Y//+I969NFH9S//8i92Dy0jUNqbQF/+8pc1fvx4rV27tvPY1VdfrXnz5qmiosLGkTnb//zP/2jo0KHasWOHbrjhBruH40gff/yxxo8frzVr1uinP/2pxo4dq1WrVtk9LEe5//77tXv3bmZTk+TrX/+6CgoKtGHDhs5j8+fP14ABA/TrX//axpFlBmZGEqStrU379+/XzJkzg47PnDlTv/vd72waVWbw+XySpMGDB9s8EudatGiRvva1r2n69Ol2D8WxNm3apIkTJ+ov//IvNXToUI0bN05PP/203cNyrClTpui1117TO++8I0l68803tWvXLs2ZM8fmkWWGtNgoLx2dPHlS7e3tKigoCDpeUFCg5uZmm0blfIFAQOXl5ZoyZYpKS0vtHo4jbdy4UQcOHNAbb7xh91Ac7Q9/+IPWrl2r8vJyrVixQnv37tU999yjnJwc3X777XYPz3GWLVsmn8+nq666SllZWWpvb9fPfvYz3XLLLXYPLSMQjCSYy+UKeh4IBHocQ/wsXrxYb731lnbt2mX3UBzp+PHjWrJkiV599VXl5ubaPRxH6+jo0MSJE/Xzn/9ckjRu3DgdOnRIa9euJRhJgBdeeEGVlZV67rnnVFJSorq6Oi1dulTDhg3Td7/7XbuH53gEIwly8cUXKysrq8csyIkTJ3rMliA+7r77bm3atEk7d+7U8OHD7R6OI+3fv18nTpzQhAkTOo+1t7dr586dWr16tVpbW5WVlWXjCJ3D4/GouLg46NjVV1+tqqoqm0bkbH/3d3+n+++/X9/5znckSV/84hf1wQcfqKKigmAkCcgZSZB+/fppwoQJqqmpCTpeU1Oj6667zqZROVMgENDixYv14osvatu2bSoqKrJ7SI41bdo0vf3226qrq+t8TJw4UX/zN3+juro6ApE4mjx5co8S9XfeeUeXXXaZTSNytk8++UR9+gRfErOysijtTRJmRhKovLxct912myZOnKiysjKtX79ex44d04IFC+wemqMsWrRIzz33nF5++WXl5eV1zka53W7179/f5tE5S15eXo9cnIEDB2rIkCHk6MTZvffeq+uuu04///nP9Vd/9Vfau3ev1q9fr/Xr19s9NEeaO3eufvazn+nSSy9VSUmJDh48qH/+53/W97//fbuHlhkCSKhf/OIXgcsuuyzQr1+/wPjx4wM7duywe0iOIynk45lnnrF7aBnhxhtvDCxZssTuYTjS5s2bA6WlpYGcnJzAVVddFVi/fr3dQ3Isv98fWLJkSeDSSy8N5ObmBi6//PLAj370o0Bra6vdQ8sI9BkBAAC2ImcEAADYimAEAADYimAEAADYimAEAADYimAEAADYimAEAADYimAEAADYimAEAADYimAEAADYimAEAADYimAEAADYimAEAADY6v8BEKVk8pHuah4AAAAASUVORK5CYII=", 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", 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" ] From 4af9419171408254d6d11c81df2344a09bcb8e3e Mon Sep 17 00:00:00 2001 From: pyiron-runner Date: Fri, 5 Jan 2024 18:17:41 +0000 Subject: [PATCH 03/10] [dependabot skip] Update env file --- .binder/environment.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.binder/environment.yml b/.binder/environment.yml index 9d891bbb..3a32b6e0 100644 --- a/.binder/environment.yml +++ b/.binder/environment.yml @@ -12,9 +12,9 @@ dependencies: - toposort =1.10 - typeguard =4.1.5 - ase =3.22.1 -- atomistics =0.1.12 +- atomistics =0.1.20 - lammps - phonopy =2.21.0 -- pyiron_atomistics =0.3.5 +- pyiron_atomistics =0.4.4 - pyiron-data =0.0.24 -- numpy =1.26.0 +- numpy =1.26.3 From 17e77b5248fafd7a2e9db19cbcd88c8a94bae0a8 Mon Sep 17 00:00:00 2001 From: liamhuber Date: Fri, 5 Jan 2024 10:27:56 -0800 Subject: [PATCH 04/10] bump pyiron-data --- .ci_support/environment-notebooks.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.ci_support/environment-notebooks.yml b/.ci_support/environment-notebooks.yml index 6e6fcd85..c58a6a0c 100644 --- a/.ci_support/environment-notebooks.yml +++ b/.ci_support/environment-notebooks.yml @@ -6,5 +6,5 @@ dependencies: - lammps - phonopy =2.21.0 - pyiron_atomistics =0.4.4 - - pyiron-data =0.0.24 + - pyiron-data =0.0.26 - numpy =1.26.3 \ No newline at end of file From cb6a7e665fc09a7d5ddd9454a240419796fe1a87 Mon Sep 17 00:00:00 2001 From: pyiron-runner Date: Fri, 5 Jan 2024 18:28:26 +0000 Subject: [PATCH 05/10] [dependabot skip] Update env file --- .binder/environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.binder/environment.yml b/.binder/environment.yml index 3a32b6e0..497ebdc0 100644 --- a/.binder/environment.yml +++ b/.binder/environment.yml @@ -16,5 +16,5 @@ dependencies: - lammps - phonopy =2.21.0 - pyiron_atomistics =0.4.4 -- pyiron-data =0.0.24 +- pyiron-data =0.0.26 - numpy =1.26.3 From 6724a0e2beaed4a363ea56aae351f52f92d4cda2 Mon Sep 17 00:00:00 2001 From: liamhuber Date: Fri, 5 Jan 2024 11:25:10 -0800 Subject: [PATCH 06/10] Debug resource path in deepdive --- notebooks/deepdive.ipynb | 654 ++++++++++++++++++++++----------------- 1 file changed, 362 insertions(+), 292 deletions(-) diff --git a/notebooks/deepdive.ipynb b/notebooks/deepdive.ipynb index 33b2b06f..31ebca21 100644 --- a/notebooks/deepdive.ipynb +++ b/notebooks/deepdive.ipynb @@ -524,9 +524,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel ran was not connected to run, andthus could not disconnect from it.\n", - " warn(\n", - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", " warn(\n" ] }, @@ -985,13 +983,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", " warn(\n" ] }, { "data": { - "image/png": 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Br0lJSVFKSkowowEAEFPiabn5oN4zkpycrIKCAjU1Nfltb2pqUklJyZivWbJkiU6ePKkvv/xydNtHH32khIQEzZ49O4SRAQCIbfG23HzQ64xUVlbqxRdf1K5du3T06FFt2rRJnZ2dqqiokHT2EsuaNWtG97/ttts0ffp03XnnnfJ4PHrvvff04IMP6te//vWYl2gAAIhn8bjcfNDvGSkvL9epU6e0bds2eb1e5eXlqaGhQXPmzJEkeb1edXZ2ju7/ox/9SE1NTfrNb36jwsJCTZ8+XatWrdIjjzwycb8FAAAxIpjl5ifz/SrhFPQ6IzawzggAIF78b9tnuv8vbT+435O3XqWfXzVr8ge6AJOyzggAAJhc8bjcPDECAEAEGVlufrwbeB06e1dNLC03T4wAABBBEhMcqirLlaSAIBl5XlWWG1PrjRAjAABEmHhbbj6kFVgBAMDkiqfl5okRAAAi1GQvNx8puEwDAACsIkYAAIBVXKYBxhEvn5YJALYRI8AY4unTMgHANi7TAN8Rb5+WCQC2ESPAOeLx0zIBwDZiBDhHMJ+WCQCYGMQIcI7u/vFDJJT9AAA/jBgBzhGPn5YJALYRI8A54vHTMgHANmIEOEc8flomANhGjADfEW+flgkAtrHoGTCGePq0TACwjRgBxhEvn5YJALZxmQYAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArAopRnbs2KGcnBw5nU4VFBSoubn5vF534MABJSUl6aqrrgrlxwIAgBgUdIzU1dVp48aN2rp1q44cOaKlS5dqxYoV6uzs/N7X+Xw+rVmzRv/zP/8T8rAAACD2OIwxJpgXLF68WPn5+aqtrR3dtnDhQq1cuVI1NTXjvu7WW2/V/PnzlZiYqLffflttbW3n/TP7+vrkcrnk8/mUlpYWzLgAAMCS8/37HdSZkdOnT6u1tVWlpaV+20tLS3Xw4MFxX/fSSy/pk08+UVVV1Xn9nMHBQfX19fk9AABAbAoqRnp6ejQ0NKTMzEy/7ZmZmerq6hrzNR9//LE2b96sPXv2KCkp6bx+Tk1NjVwu1+gjOzs7mDEBAEAUCekNrA6Hw++5MSZgmyQNDQ3ptttuU3V1tRYsWHDe33/Lli3y+Xyjj+PHj4cyJgAAiALnd6ri/5sxY4YSExMDzoJ0d3cHnC2RpP7+fh06dEhHjhzRfffdJ0kaHh6WMUZJSUnau3evrrvuuoDXpaSkKCUlJZjRAABAlArqzEhycrIKCgrU1NTkt72pqUklJSUB+6elpemDDz5QW1vb6KOiokKXXXaZ2tratHjx4gubHgAARL2gzoxIUmVlpW6//XYVFhaquLhYzz//vDo7O1VRUSHp7CWWzz77TK+88ooSEhKUl5fn9/qMjAw5nc6A7QAAID4FHSPl5eU6deqUtm3bJq/Xq7y8PDU0NGjOnDmSJK/X+4NrjgAAAIwIep0RG1hnBACA6DMp64wAAABMNGIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACrkmwPAAAAftjQsFFLR6+6+weUkepUUU66EhMctseaEMQIAAARrrHdq+p6j7y+gdFtbpdTVWW5Wp7ntjjZxOAyDQAAEayx3av1uw/7hYgkdfkGtH73YTW2ey1NNnGIEQAAItTQsFF1vUdmjK+NbKuu92hoeKw9ogcxAgBAhGrp6A04I3IuI8nrG1BLR2/4hpoExAgAABGqu3/8EAllv0hFjAAAEKEyUp0Tul+kIkYAAIhQRTnpcrucGu8GXofO3lVTlJMezrEmHDECAECESkxwqKosV5ICgmTkeVVZbtSvN0KMAAAQwZbnuVW7Ol9ZLv9LMVkup2pX58fEOiMsegYAQIRbnufWstwsVmAFAAD2JCY4VDxvuu0xJgWXaQAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsCqkGNmxY4dycnLkdDpVUFCg5ubmcfd98803tWzZMl1yySVKS0tTcXGx3n333ZAHBgAAsSXoGKmrq9PGjRu1detWHTlyREuXLtWKFSvU2dk55v7vvfeeli1bpoaGBrW2turaa69VWVmZjhw5csHDAwCA6OcwxphgXrB48WLl5+ertrZ2dNvChQu1cuVK1dTUnNf3uOKKK1ReXq6HHnrovPbv6+uTy+WSz+dTWlpaMOMCAABLzvfvd1BnRk6fPq3W1laVlpb6bS8tLdXBgwfP63sMDw+rv79f6enp4+4zODiovr4+vwcAAIhNQcVIT0+PhoaGlJmZ6bc9MzNTXV1d5/U9Hn/8cX311VdatWrVuPvU1NTI5XKNPrKzs4MZEwAARJGQ3sDqcDj8nhtjAraN5bXXXtPDDz+suro6ZWRkjLvfli1b5PP5Rh/Hjx8PZUwAABAFkoLZecaMGUpMTAw4C9Ld3R1wtuS76urqtG7dOr3++uu6/vrrv3fflJQUpaSkBDMaAACIUkGdGUlOTlZBQYGampr8tjc1NamkpGTc17322mu644479Oqrr+qmm24KbVIAABCTgjozIkmVlZW6/fbbVVhYqOLiYj3//PPq7OxURUWFpLOXWD777DO98sorks6GyJo1a/Tkk0/q6quvHj2rMnXqVLlcrgn8VQAAQDQKOkbKy8t16tQpbdu2TV6vV3l5eWpoaNCcOXMkSV6v12/Nkeeee05nzpzRvffeq3vvvXd0+9q1a/Xyyy9f+G8AAACiWtDrjNjAOiMAAESfSVlnBAAAYKIRIwAAwKqg3zOC6DM0bNTS0avu/gFlpDpVlJOuxIQfXhcGAIBwIEZiXGO7V9X1Hnl9A6Pb3C6nqspytTzPbXEyAADO4jJNDGts92r97sN+ISJJXb4Brd99WI3tXkuTAQDwX8RIjBoaNqqu92isW6VGtlXXezQ0HPE3UwEAYhwxEqNaOnoDzoicy0jy+gbU0tEbvqEAABgDMRKjuvvHD5FQ9gMAYLIQIzEqI9U5ofsBADBZiJEYVZSTLrfLqfFu4HXo7F01RTnp4RwLAIAAxEiMSkxwqKosV5ICgmTkeVVZLuuNAACsI0Zi2PI8t2pX5yvL5X8pJsvlVO3qfNYZAQBEBBY9i3HL89xalpvFCqwAgIhFjMSBxASHiudNtz0GAABj4jINAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArEqyPQAAAPFmaNiopaNX3f0Dykh1qignXYkJDttjWUOMAAAQRo3tXlXXe+T1DYxuc7ucqirL1fI8t8XJ7OEyDQAAYdLY7tX63Yf9QkSSunwDWr/7sBrbvZYms4sYAQAgDIaGjarrPTJjfG1kW3W9R0PDY+0R24gRAADCoKWjN+CMyLmMJK9vQC0dveEbKkIQIwAAhEF3//ghEsp+sYQYAQAgDDJSnRO6XywhRgAACIOinHS5XU6NdwOvQ2fvqinKSQ/nWBGBGAEAIAwSExyqKsuVpIAgGXleVZYbl+uNECMAAITJ8jy3alfnK8vlfykmy+VU7er8uF1nhEXPAAAIo+V5bi3LzWIF1nMQIwAAhFligkPF86bbHiNicJkGAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYFRUrsBpjJEl9fX2WJwEAAOdr5O/2yN/x8URFjPT390uSsrOzLU8CAACC1d/fL5fLNe7XHeaHciUCDA8P6+TJk0pNTZXDcf4fJNTX16fs7GwdP35caWlpkzghzgfHI/JwTCILxyPycEwujDFG/f39mjlzphISxn9nSFScGUlISNDs2bNDfn1aWhr/JYogHI/IwzGJLByPyMMxCd33nREZwRtYAQCAVcQIAACwKqZjJCUlRVVVVUpJSbE9CsTxiEQck8jC8Yg8HJPwiIo3sAIAgNgV02dGAABA5CNGAACAVcQIAACwihgBAABWESMAAMCqqI6RHTt2KCcnR06nUwUFBWpubv7e/fft26eCggI5nU7NnTtXzz77bJgmjR/BHJM333xTy5Yt0yWXXKK0tDQVFxfr3XffDeO08SHYfycjDhw4oKSkJF111VWTO2CcCfZ4DA4OauvWrZozZ45SUlI0b9487dq1K0zTxodgj8mePXu0aNEiXXTRRXK73brzzjt16tSpME0bo0yU+stf/mKmTJliXnjhBePxeMz9999vpk2bZj799NMx9z927Ji56KKLzP333288Ho954YUXzJQpU8wbb7wR5sljV7DH5P777zePPvqoaWlpMR999JHZsmWLmTJlijl8+HCYJ49dwR6TEV988YWZO3euKS0tNYsWLQrPsHEglONx8803m8WLF5umpibT0dFh/vnPf5oDBw6EcerYFuwxaW5uNgkJCebJJ580x44dM83NzeaKK64wK1euDPPksSVqY6SoqMhUVFT4bbv88svN5s2bx9z/d7/7nbn88sv9tt19993m6quvnrQZ402wx2Qsubm5prq6eqJHi1uhHpPy8nLzxz/+0VRVVREjEyjY4/G3v/3NuFwuc+rUqXCMF5eCPSZ/+tOfzNy5c/22PfXUU2b27NmTNmM8iMrLNKdPn1Zra6tKS0v9tpeWlurgwYNjvub9998P2P+GG27QoUOH9O23307arPEilGPyXcPDw+rv71d6evpkjBh3Qj0mL730kj755BNVVVVN9ohxJZTj8c4776iwsFCPPfaYZs2apQULFuiBBx7QN998E46RY14ox6SkpEQnTpxQQ0ODjDH6/PPP9cYbb+imm24Kx8gxKyo+tfe7enp6NDQ0pMzMTL/tmZmZ6urqGvM1XV1dY+5/5swZ9fT0yO12T9q88SCUY/Jdjz/+uL766iutWrVqMkaMO6Eck48//libN29Wc3OzkpKi8n8eIlYox+PYsWPav3+/nE6n3nrrLfX09Oiee+5Rb28v7xuZAKEck5KSEu3Zs0fl5eUaGBjQmTNndPPNN+vpp58Ox8gxKyrPjIxwOBx+z40xAdt+aP+xtiN0wR6TEa+99poefvhh1dXVKSMjY7LGi0vne0yGhoZ02223qbq6WgsWLAjXeHEnmH8jw8PDcjgc2rNnj4qKinTjjTfqiSee0Msvv8zZkQkUzDHxeDzasGGDHnroIbW2tqqxsVEdHR2qqKgIx6gxKyr/r8+MGTOUmJgYUK7d3d0BhTsiKytrzP2TkpI0ffr0SZs1XoRyTEbU1dVp3bp1ev3113X99ddP5phxJdhj0t/fr0OHDunIkSO67777JJ39Y2iMUVJSkvbu3avrrrsuLLPHolD+jbjdbs2aNUsul2t028KFC2WM0YkTJzR//vxJnTnWhXJMampqtGTJEj344IOSpCuvvFLTpk3T0qVL9cgjj3CWPURReWYkOTlZBQUFampq8tve1NSkkpKSMV9TXFwcsP/evXtVWFioKVOmTNqs8SKUYyKdPSNyxx136NVXX+Wa6wQL9pikpaXpgw8+UFtb2+ijoqJCl112mdra2rR48eJwjR6TQvk3smTJEp08eVJffvnl6LaPPvpICQkJmj179qTOGw9COSZff/21EhL8/3QmJiZK+u/ZdoTA1jtnL9TI7Vg7d+40Ho/HbNy40UybNs385z//McYYs3nzZnP77beP7j9ya++mTZuMx+MxO3fu5NbeCRbsMXn11VdNUlKS2b59u/F6vaOPL774wtavEHOCPSbfxd00EyvY49Hf329mz55tbrnlFvPhhx+affv2mfnz55u77rrL1q8Qc4I9Ji+99JJJSkoyO3bsMJ988onZv3+/KSwsNEVFRbZ+hZgQtTFijDHbt283c+bMMcnJySY/P9/s27dv9Gtr164111xzjd/+f//7381Pf/pTk5ycbC699FJTW1sb5oljXzDH5JprrjGSAh5r164N/+AxLNh/J+ciRiZesMfj6NGj5vrrrzdTp041s2fPNpWVlebrr78O89SxLdhj8tRTT5nc3FwzdepU43a7za9+9Stz4sSJME8dWxzGcF4JAADYE5XvGQEAALGDGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwKr/B+Y4x3CewomWAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
" ] @@ -1251,7 +1249,7 @@ "\n", "\n", - "\n", "\n", "clustersimple\n", "\n", "simple: Workflow\n", + "\n", + "clustersimplesum\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sum: Add\n", + "\n", + "\n", + "clustersimplesumInputs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Inputs\n", + "\n", + "\n", + "clustersimplesumOutputs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Outputs\n", + "\n", "\n", "clustersimpleInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clustersimpleOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clustersimplea\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "a: AddOne\n", "\n", "\n", "clustersimpleaInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clustersimpleaOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clustersimpleb\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "b: AddOne\n", "\n", "\n", "clustersimplebInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clustersimplebOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", - "\n", - "clustersimplesum\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sum: Add\n", - "\n", - "\n", - "clustersimplesumInputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Inputs\n", - "\n", - "\n", - "clustersimplesumOutputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Outputs\n", - "\n", "\n", "\n", "clustersimpleInputsrun\n", @@ -1415,9 +1413,9 @@ "\n", "\n", "clustersimpleInputsax->clustersimpleaInputsx\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -1434,9 +1432,9 @@ "\n", "\n", "clustersimpleInputsb__x->clustersimplebInputsx\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -1480,7 +1478,7 @@ "clustersimpleaOutputsran->clustersimplesumInputsaccumulate_and_run\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", @@ -1491,9 +1489,9 @@ "\n", "\n", "clustersimpleaOutputsy->clustersimpleOutputsay\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -1504,9 +1502,9 @@ "\n", "\n", "clustersimpleaOutputsy->clustersimplesumInputsx\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -1530,9 +1528,9 @@ "\n", "\n", "clustersimplebOutputsran->clustersimplesumInputsaccumulate_and_run\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -1549,9 +1547,9 @@ "\n", "\n", "clustersimplebOutputsy->clustersimplesumInputsy\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -1575,15 +1573,15 @@ "\n", "\n", "clustersimplesumOutputssum->clustersimpleOutputsa + b + 2\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 41, @@ -1617,18 +1615,10 @@ "id": "ae500d5e-e55b-432c-8b5f-d5892193cdf5", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/huber/anaconda3/envs/pyiron_311/lib/python3.11/site-packages/h5py/__init__.py:36: UserWarning: h5py is running against HDF5 1.14.3 when it was built against 1.14.2, this may cause problems\n", - " _warn((\"h5py is running against HDF5 {0} when it was built against {1}, \"\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79f71f503b1540ffafcc45934f0e1956", + "model_id": "893f572bcd43499b8911a35681c00a5d", "version_major": 2, "version_minor": 0 }, @@ -1637,17 +1627,25 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/huber/work/pyiron/pyiron_atomistics/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", + " warnings.warn(\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "The job JUSTAJOBNAME was saved and received the ID: 9562\n" + "The job JUSTAJOBNAME was saved and received the ID: 9563\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 42, @@ -1666,21 +1664,25 @@ } ], "source": [ - "wf.register(\"pyiron_atomistics\", \"pyiron_workflow.node_library.pyiron_atomistics\")\n", - "wf.register(\"plotting\", \"pyiron_workflow.node_library.plotting\")\n", + "try:\n", + " wf.register(\"pyiron_atomistics\", \"pyiron_workflow.node_library.pyiron_atomistics\")\n", + " wf.register(\"plotting\", \"pyiron_workflow.node_library.plotting\")\n", "\n", - "wf = Workflow(\"with_prebuilt\")\n", + " wf = Workflow(\"with_prebuilt\")\n", "\n", - "wf.structure = wf.create.pyiron_atomistics.Bulk(cubic=True, name=\"Al\")\n", - "wf.engine = wf.create.pyiron_atomistics.Lammps(structure=wf.structure)\n", - "wf.calc = wf.create.pyiron_atomistics.CalcMd(job=wf.engine)\n", - "wf.plot = wf.create.plotting.Scatter(\n", - " x=wf.calc.outputs.steps, \n", - " y=wf.calc.outputs.temperature\n", - ")\n", + " wf.structure = wf.create.pyiron_atomistics.Bulk(cubic=True, name=\"Al\")\n", + " wf.engine = wf.create.pyiron_atomistics.Lammps(structure=wf.structure)\n", + " wf.calc = wf.create.pyiron_atomistics.CalcMd(job=wf.engine)\n", + " wf.plot = wf.create.plotting.Scatter(\n", + " x=wf.calc.outputs.steps, \n", + " y=wf.calc.outputs.temperature\n", + " )\n", "\n", - "out = wf.run()\n", - "out.plot__fig" + " out = wf.run()\n", + " out.plot__fig\n", + "except:\n", + " from pyiron_base import state\n", + " raise RuntimeError(f\"State configuration:{state.settings.configuration}\")" ] }, { @@ -1703,7 +1705,7 @@ "\n", "\n", - "\n", "\n", "clusterwith_prebuilt\n", "\n", "with_prebuilt: Workflow\n", - "\n", - "clusterwith_prebuiltInputs\n", + "\n", + "clusterwith_prebuiltOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", - "Inputs\n", + "\n", + "Outputs\n", "\n", - "\n", - "clusterwith_prebuiltOutputs\n", + "\n", + "clusterwith_prebuiltInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", - "Outputs\n", + "\n", + "Inputs\n", "\n", "\n", "\n", @@ -1907,7 +1909,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 43, @@ -1965,7 +1967,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", " warn(\n" ] }, @@ -2148,7 +2150,7 @@ "\n", "\n", - "\n", "\n", "clusterphase_preference\n", "\n", "phase_preference: Workflow\n", - "\n", - "clusterphase_preferenceInputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Inputs\n", - "\n", "\n", "clusterphase_preferenceOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clusterphase_preferenceelement\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "element: UserInput\n", "\n", "\n", "clusterphase_preferenceelementInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferenceelementOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clusterphase_preferencemin_phase1\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "min_phase1: LammpsMinimize\n", "\n", "\n", "clusterphase_preferencemin_phase1Inputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencemin_phase1Outputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", - "\n", - "clusterphase_preferencemin_phase2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "min_phase2: LammpsMinimize\n", - "\n", - "\n", - "clusterphase_preferencemin_phase2Outputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Outputs\n", - "\n", - "\n", - "clusterphase_preferencemin_phase2Inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Inputs\n", - "\n", "\n", "clusterphase_preferencee1\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "e1: GetItem\n", "\n", "\n", "clusterphase_preferencee1Inputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencee1Outputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clusterphase_preferencen1\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "n1: Length\n", "\n", "\n", "clusterphase_preferencen1Inputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencen1Outputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", + "\n", + "clusterphase_preferenceInputs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Inputs\n", + "\n", "\n", "clusterphase_preferencee2\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "e2: GetItem\n", "\n", "\n", "clusterphase_preferencee2Inputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencee2Outputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clusterphase_preferencen2\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "n2: Length\n", "\n", "\n", "clusterphase_preferencen2Inputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencen2Outputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clusterphase_preferencee2__getitem_Divide_n2__len\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "e2__getitem_Divide_n2__len: Divide\n", "\n", "\n", "clusterphase_preferencee2__getitem_Divide_n2__lenInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencee2__getitem_Divide_n2__lenOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", + "\n", + "clusterphase_preferencemin_phase2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "min_phase2: LammpsMinimize\n", + "\n", + "\n", + "clusterphase_preferencemin_phase2Inputs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Inputs\n", + "\n", + "\n", + "clusterphase_preferencemin_phase2Outputs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Outputs\n", + "\n", "\n", "clusterphase_preferencee1__getitem_Divide_n1__len\n", "\n", @@ -2543,9 +2545,9 @@ "\n", "\n", "clusterphase_preferenceInputselement->clusterphase_preferenceelementInputsuser_input\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2562,9 +2564,9 @@ "\n", "\n", "clusterphase_preferenceInputsphase1->clusterphase_preferencemin_phase1Inputscrystalstructure\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2581,9 +2583,9 @@ "\n", "\n", "clusterphase_preferenceInputslattice_guess1->clusterphase_preferencemin_phase1Inputslattice_guess\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2600,9 +2602,9 @@ "\n", "\n", "clusterphase_preferenceInputsphase2->clusterphase_preferencemin_phase2Inputscrystalstructure\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2619,9 +2621,9 @@ "\n", "\n", "clusterphase_preferenceInputslattice_guess2->clusterphase_preferencemin_phase2Inputslattice_guess\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2638,9 +2640,9 @@ "\n", "\n", "clusterphase_preferenceInputse1__item->clusterphase_preferencee1Inputsitem\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2657,9 +2659,9 @@ "\n", "\n", "clusterphase_preferenceInputse2__item->clusterphase_preferencee2Inputsitem\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2701,9 +2703,9 @@ "\n", "\n", "clusterphase_preferenceelementOutputsuser_input->clusterphase_preferencemin_phase1Inputselement\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2714,9 +2716,9 @@ "\n", "\n", "clusterphase_preferenceelementOutputsuser_input->clusterphase_preferencemin_phase2Inputselement\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2752,9 +2754,9 @@ "\n", "\n", "clusterphase_preferencemin_phase1Outputsstructure->clusterphase_preferencen1Inputsobj\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2771,9 +2773,9 @@ "\n", "\n", "clusterphase_preferencemin_phase1Outputsenergy->clusterphase_preferencee1Inputsobj\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2809,9 +2811,9 @@ "\n", "\n", "clusterphase_preferencemin_phase2Outputsstructure->clusterphase_preferencen2Inputsobj\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2830,7 +2832,7 @@ "clusterphase_preferencemin_phase2Outputsenergy->clusterphase_preferencee2Inputsobj\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "\n", @@ -2866,9 +2868,9 @@ "\n", "\n", "clusterphase_preferencee1Outputsgetitem->clusterphase_preferencee1__getitem_Divide_n1__lenInputsobj\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2904,9 +2906,9 @@ "\n", "\n", "clusterphase_preferencen1Outputslen->clusterphase_preferencee1__getitem_Divide_n1__lenInputsother\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2942,9 +2944,9 @@ "\n", "\n", "clusterphase_preferencee2Outputsgetitem->clusterphase_preferencee2__getitem_Divide_n2__lenInputsobj\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2980,9 +2982,9 @@ "\n", "\n", "clusterphase_preferencen2Outputslen->clusterphase_preferencee2__getitem_Divide_n2__lenInputsother\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -3018,9 +3020,9 @@ "\n", "\n", "clusterphase_preferencee2__getitem_Divide_n2__lenOutputstruediv->clusterphase_preferencecompareInputsobj\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -3056,9 +3058,9 @@ "\n", "\n", "clusterphase_preferencee1__getitem_Divide_n1__lenOutputstruediv->clusterphase_preferencecompareInputsother\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -3088,15 +3090,15 @@ "\n", "\n", "clusterphase_preferencecompareOutputssub->clusterphase_preferenceOutputscompare__sub\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 49, @@ -3114,12 +3116,34 @@ "id": "b51bef25-86c5-4d57-80c1-ab733e703caf", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/huber/work/pyiron/pyiron_atomistics/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job JUSTAJOBNAME was saved and received the ID: 9563\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/huber/work/pyiron/pyiron_atomistics/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", + " warnings.warn(\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "The job JUSTAJOBNAME was saved and received the ID: 9562\n", - "The job JUSTAJOBNAME was saved and received the ID: 9562\n", + "The job JUSTAJOBNAME was saved and received the ID: 9563\n", "Al: E(hcp) - E(fcc) = 1.17 eV/atom\n" ] } @@ -3139,16 +3163,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel ran was not connected to accumulate_and_run, andthus could not disconnect from it.\n", - " warn(\n" + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel ran was not connected to accumulate_and_run, andthus could not disconnect from it.\n", + " warn(\n", + "/Users/huber/work/pyiron/pyiron_atomistics/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1997--Liu-X-Y--Al-Mg--LAMMPS--ipr1\n", + " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The job JUSTAJOBNAME was saved and received the ID: 9562\n", - "The job JUSTAJOBNAME was saved and received the ID: 9562\n", + "The job JUSTAJOBNAME was saved and received the ID: 9563\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/huber/work/pyiron/pyiron_atomistics/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1997--Liu-X-Y--Al-Mg--LAMMPS--ipr1\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job JUSTAJOBNAME was saved and received the ID: 9563\n", "Mg: E(hcp) - E(fcc) = -4.54 eV/atom\n" ] } @@ -3180,15 +3220,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel job was not connected to job, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel job was not connected to job, andthus could not disconnect from it.\n", " warn(\n", - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel accumulate_and_run was not connected to ran, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel accumulate_and_run was not connected to ran, andthus could not disconnect from it.\n", " warn(\n", - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel element was not connected to user_input, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel element was not connected to user_input, andthus could not disconnect from it.\n", " warn(\n", - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel structure was not connected to obj, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel structure was not connected to obj, andthus could not disconnect from it.\n", " warn(\n", - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel energy was not connected to obj, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel energy was not connected to obj, andthus could not disconnect from it.\n", " warn(\n" ] } @@ -3218,16 +3258,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel ran was not connected to accumulate_and_run, andthus could not disconnect from it.\n", - " warn(\n" + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel ran was not connected to accumulate_and_run, andthus could not disconnect from it.\n", + " warn(\n", + "/Users/huber/work/pyiron/pyiron_atomistics/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", + " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The job JUSTAJOBNAME was saved and received the ID: 9562\n", - "The job JUSTAJOBNAME was saved and received the ID: 9562\n", + "The job JUSTAJOBNAME was saved and received the ID: 9563\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/huber/work/pyiron/pyiron_atomistics/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job JUSTAJOBNAME was saved and received the ID: 9563\n", "Al: E(hcp) - E(fcc) = -5.57 eV/atom\n" ] } @@ -3248,16 +3304,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel ran was not connected to accumulate_and_run, andthus could not disconnect from it.\n", - " warn(\n" + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel ran was not connected to accumulate_and_run, andthus could not disconnect from it.\n", + " warn(\n", + "/Users/huber/work/pyiron/pyiron_atomistics/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job JUSTAJOBNAME was saved and received the ID: 9563\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/huber/work/pyiron/pyiron_atomistics/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", + " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "The job JUSTAJOBNAME was saved and received the ID: 9562\n", - "The job JUSTAJOBNAME was saved and received the ID: 9562\n", + "The job JUSTAJOBNAME was saved and received the ID: 9563\n", "Al: E(hcp) - E(fcc) = 0.03 eV/atom\n" ] } @@ -3309,7 +3381,7 @@ "output_type": "stream", "text": [ "None 1\n", - " \n" + " \n" ] } ], @@ -3391,7 +3463,7 @@ "output_type": "stream", "text": [ "None 1\n", - " \n", + " \n", "Finally 5\n", "b (Add) output single-value: 6\n" ] @@ -3453,7 +3525,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "6.013545583002269\n" + "6.0082737900083885\n" ] } ], @@ -3485,7 +3557,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.503649155027233\n" + "2.4378735430072993\n" ] } ], @@ -3626,9 +3698,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel run was not connected to true, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel run was not connected to true, andthus could not disconnect from it.\n", " warn(\n", - "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:164: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", " warn(\n" ] } @@ -3709,11 +3781,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.684 > 0.2\n", - "0.598 > 0.2\n", - "0.846 > 0.2\n", - "0.021 <= 0.2\n", - "Finally 0.021\n" + "0.361 > 0.2\n", + "0.097 <= 0.2\n", + "Finally 0.097\n" ] } ], From dcc5d485b5b2581690a29e729db7628762ab55fa Mon Sep 17 00:00:00 2001 From: liamhuber Date: Fri, 5 Jan 2024 11:41:17 -0800 Subject: [PATCH 07/10] Check file status --- notebooks/deepdive.ipynb | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/notebooks/deepdive.ipynb b/notebooks/deepdive.ipynb index 31ebca21..e240c311 100644 --- a/notebooks/deepdive.ipynb +++ b/notebooks/deepdive.ipynb @@ -1682,7 +1682,12 @@ " out.plot__fig\n", "except:\n", " from pyiron_base import state\n", - " raise RuntimeError(f\"State configuration:{state.settings.configuration}\")" + " from pathlib import Path\n", + " import os\n", + " file_path='/usr/share/miniconda3/envs/my-env/share/pyiron/lammps/bin/run_lammps_2020.03.03.sh'\n", + " raise RuntimeError(\n", + " f\"Resource paths:{state.settings.configuration['resource_paths']}; exists {Path(file_path).exists()}; is file {Path(file_path).is_file()}; executable {os.access(file_path, os.X_OK)}\"\n", + " )" ] }, { From f02b8d5347d775f5d09e11bf6a9ecc729d7d8099 Mon Sep 17 00:00:00 2001 From: liamhuber Date: Fri, 5 Jan 2024 13:07:40 -0800 Subject: [PATCH 08/10] Bump pyiron-data --- .ci_support/environment-notebooks.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.ci_support/environment-notebooks.yml b/.ci_support/environment-notebooks.yml index c58a6a0c..4cb244ca 100644 --- a/.ci_support/environment-notebooks.yml +++ b/.ci_support/environment-notebooks.yml @@ -6,5 +6,5 @@ dependencies: - lammps - phonopy =2.21.0 - pyiron_atomistics =0.4.4 - - pyiron-data =0.0.26 + - pyiron-data =0.0.27 - numpy =1.26.3 \ No newline at end of file From c6db33d0cc1ea4259d9c977e479439752e604d14 Mon Sep 17 00:00:00 2001 From: pyiron-runner Date: Fri, 5 Jan 2024 21:08:08 +0000 Subject: [PATCH 09/10] [dependabot skip] Update env file --- .binder/environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.binder/environment.yml b/.binder/environment.yml index 497ebdc0..3dda474e 100644 --- a/.binder/environment.yml +++ b/.binder/environment.yml @@ -16,5 +16,5 @@ dependencies: - lammps - phonopy =2.21.0 - pyiron_atomistics =0.4.4 -- pyiron-data =0.0.26 +- pyiron-data =0.0.27 - numpy =1.26.3 From 6e6dcb213cef939c45f58dd9a4477a0fe68f26dc Mon Sep 17 00:00:00 2001 From: liamhuber Date: Fri, 5 Jan 2024 13:18:56 -0800 Subject: [PATCH 10/10] Revert debug stuff in the deepdive notebook --- notebooks/deepdive.ipynb | 356 +++++++++++++++++++-------------------- 1 file changed, 174 insertions(+), 182 deletions(-) diff --git a/notebooks/deepdive.ipynb b/notebooks/deepdive.ipynb index e240c311..f61042a0 100644 --- a/notebooks/deepdive.ipynb +++ b/notebooks/deepdive.ipynb @@ -524,6 +524,8 @@ "name": "stderr", "output_type": "stream", "text": [ + "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel ran was not connected to run, andthus could not disconnect from it.\n", + " warn(\n", "/Users/huber/work/pyiron/pyiron_workflow/pyiron_workflow/channels.py:166: UserWarning: The channel run was not connected to ran, andthus could not disconnect from it.\n", " warn(\n" ] @@ -989,7 +991,7 @@ }, { "data": { - "image/png": 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nJ4fNknR1dYXNpkhSf3+/Dh06pJaWFj3wwAOSpKGhIRljlJKSoj179uiGG24IOy8tLU1paWlOSgMAABcIRzMnqampKigoUGNjY0h7Y2OjSkpKwvp7PB699957OnLkyMhRUVGhyy+/XEeOHFFRUdH5VQ8AAC44jmZOJGn9+vW64447tHDhQhUXF+sPf/iDOjo6VFFRIensksx//vMf/fGPf1RSUpLy8vJCzp85c6bcbndYOwAAgBRBOCkvL1dPT482b94sv9+vvLw81dfXKzs7W5Lk9/u/cc8TAACAsTje5yQW2OcEAID4E5V9TgAAACYb4QQAAFiFcAIAAKxCOAEAAFZxfLcOAEyW4JDRwfZedfUPaOY0twpz0pWcFL77NIALG+EEgBUaWv2q2t0mf2BgpM3ndauyLFdL83wxrAxAtLGsAyDmGlr9Wr3jcEgwkaTOwIBW7zishlZ/jCoDEAuEEwAxFRwyqtrdptE2XBpuq9rdpuCQ9VsyAZgghBMAMXWwvTdsxuRcRpI/MKCD7b3RKwpATBFOAMRUV//YwSSSfgDiH+EEQEzNnOae0H4A4h/hBEBMFeaky+d1a6wbhl06e9dOYU56NMsCEEOEEwAxlZzkUmVZriSFBZThx5Vluex3AiQQwgmAmFua51PtynxlekOXbjK9btWuzGefEyDBsAkbACsszfNpSW4mO8QCIJwAsEdykkvF86bHugwAMcayDgAAsArhBAAAWIVwAgAArEI4AQAAViGcAAAAqxBOAACAVQgnAADAKoQTAABgFcIJAACwCuEEAABYhXACAACsQjgBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGCVlFgXAADA+QgOGR1s71VX/4BmTnOrMCddyUmuWJeF80A4AQDErYZWv6p2t8kfGBhp83ndqizL1dI8Xwwrw/lgWQcAEJcaWv1aveNwSDCRpM7AgFbvOKyGVn+MKsP5IpwAAOJOcMioanebzCg/G26r2t2m4NBoPWA7wgkAIO4cbO8NmzE5l5HkDwzoYHtv9IrChCGcAADiTlf/2MEkkn6wC+EEABB3Zk5zT2g/2IVwAgCIO4U56fJ53RrrhmGXzt61U5iTHs2yMEEIJwCAuJOc5FJlWa4khQWU4ceVZbnsdxKnCCeIC8Eho3eP9ej/jvxH7x7r4Qp8AFqa51PtynxlekOXbjK9btWuzGefkzjGJmywHpssARjL0jyfluRmskPsBcZljLH+T9C+vj55vV4FAgF5PJ5Yl4MoGt5k6av/SIf/2+GvIwCwV6Sf3yzrwFpssgQAiYlwAmuxyRIAJCbCCazFJksAkJgIJ7AWmywBQGIinMBabLIEAImJcAJrsckSACQmwgmsxiZLAJB42IQN1mOTJQBILIQTxIXkJJeK502PdRkAgChgWQcAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCrcSgwA3yA4ZNhnB4iiiGZOampqlJOTI7fbrYKCAjU1NY3Z97XXXtOSJUt06aWXyuPxqLi4WG+99VbEBQNANDW0+nXdlrf1o+f/rgf/dEQ/ev7vum7L22po9ce6NOCC5Tic1NXVae3atdq0aZNaWlq0ePFiLVu2TB0dHaP2f+edd7RkyRLV19erublZ3//+91VWVqaWlpbzLh4AJlNDq1+rdxyWPzAQ0t4ZGNDqHYcJKMAkcRljjJMTioqKlJ+fr9ra2pG2BQsWaPny5aqurh7Xc1x55ZUqLy/XI488Mq7+fX198nq9CgQC8ng8TsoFgIgEh4yu2/J2WDAZ5tLZ73ja98sbWOJJMCzzjV+kn9+Orjk5ffq0mpubtWHDhpD20tJSHThwYFzPMTQ0pP7+fqWnj/0194ODgxocHBx53NfX56RMADhvB9t7xwwmkmQk+QMDOtjey1crJJCGVr+qdreF/Nvwed2qLMvli0gnkKNlne7ubgWDQWVkZIS0Z2RkqLOzc1zP8cQTT+izzz7TihUrxuxTXV0tr9c7cmRlZTkpEwDOW1f/2MEkkn6IfyzzRU9EF8S6XKHTV8aYsLbR7Nq1S48++qjq6uo0c+bMMftt3LhRgUBg5Dhx4kQkZQJAxGZOc09oP8S34JBR1e42jXYdxHBb1e42BYccXSmBMTgKJzNmzFBycnLYLElXV1fYbMpX1dXV6Z577tGf//xn3XjjjV/bNy0tTR6PJ+QAgGgqzEmXz+vWWH92uXR2Or8wZ+wlalw4nCzz4fw5CiepqakqKChQY2NjSHtjY6NKSkrGPG/Xrl2666679NJLL+mWW26JrFIAiKLkJJcqy3IlKSygDD+uLMvlQsgEwTJfdDle1lm/fr22bt2q7du36+jRo1q3bp06OjpUUVEh6eySzKpVq0b679q1S6tWrdITTzyha6+9Vp2dners7FQgEJi4dwEAk2Bpnk+1K/OV6Q1dusn0ulW7Mp8LIBMIy3zR5XiH2PLycvX09Gjz5s3y+/3Ky8tTfX29srOzJUl+vz9kz5PnnntOZ86c0f3336/7779/pP3OO+/Uiy++eP7vAAAm0dI8n5bkZnLraIIbXubrDAyMet3J8K3lLPNNDMf7nMQC+5wAAGJt+G4dSSEBZTimMpsWLtLPb774DwCAcWCZL3r44j8AAMaJZb7oIJwAAOBAcpKLXYEnGcs6AADAKoQTAABgFcIJAACwCuEEAABYhXACAACsQjgBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCqEEwAAYBXCCQAAsArhBAAAWIVwAgAArEI4AQAAViGcAAAAqxBOAACAVQgnAADAKoQTAABgFcIJAACwCuEEAABYhXACAACsQjgBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCqEEwAAYBXCCQAAsArhBAAAWIVwAgAArEI4AQAAVkmJdQEAgMgFh4wOtveqq39AM6e5VZiTruQkV6zLAs4L4QQA4lRDq19Vu9vkDwyMtPm8blWW5Wppni+GlQHnh2UdAIhDDa1+rd5xOCSYSFJnYECrdxxWQ6s/RpUB549wAgBxJjhkVLW7TWaUnw23Ve1uU3BotB6A/QgnABBnDrb3hs2YnMtI8gcGdLC9N3pFAROIcAIAcaarf+xgEkk/wDaEEwCIMzOnuSe0H2AbwgkAxJnCnHT5vG6NdcOwS2fv2inMSY9mWcCEIZwAQJxJTnKpsixXksICyvDjyrJc9jtB3CKcAEAcWprnU+3KfGV6Q5duMr1u1a7MZ58TxLWE3YSNXRUBxLuleT4tyc3k/zJccCKaOampqVFOTo7cbrcKCgrU1NT0tf337t2rgoICud1uzZ07V88++2xExU6Uhla/rtvytn70/N/14J+O6EfP/13XbXmbTYsAxJ3kJJeK503X/14zW8XzphNMcEFwHE7q6uq0du1abdq0SS0tLVq8eLGWLVumjo6OUfu3t7fr5ptv1uLFi9XS0qJf/epXWrNmjV599dXzLj4S7KoIAIDdXMYYR1sIFhUVKT8/X7W1tSNtCxYs0PLly1VdXR3W/5e//KXefPNNHT16dKStoqJC//rXv/Tuu++O6zX7+vrk9XoVCATk8XiclBsiOGR03Za3x9y8yKWz67X7fnkDf30AAHCeIv38djRzcvr0aTU3N6u0tDSkvbS0VAcOHBj1nHfffTes/0033aRDhw7pyy+/dPLy541dFQEAsJ+jC2K7u7sVDAaVkZER0p6RkaHOzs5Rz+ns7By1/5kzZ9Td3S2fL/yK8sHBQQ0ODo487uvrc1LmmNhVEQAA+0V0QazLFbrkYYwJa/um/qO1D6uurpbX6x05srKyIikzDLsqAgBgP0fhZMaMGUpOTg6bJenq6gqbHRmWmZk5av+UlBRNnz591HM2btyoQCAwcpw4ccJJmWNiV0UAAOznKJykpqaqoKBAjY2NIe2NjY0qKSkZ9Zzi4uKw/nv27NHChQs1ZcqUUc9JS0uTx+MJOSYCuyoCAGA/x8s669ev19atW7V9+3YdPXpU69atU0dHhyoqKiSdnfVYtWrVSP+Kigp99NFHWr9+vY4ePart27dr27ZteuihhybuXTjArooAANjN8Q6x5eXl6unp0ebNm+X3+5WXl6f6+nplZ2dLkvx+f8ieJzk5Oaqvr9e6dev0zDPPaNasWXrqqaf0gx/8YOLehUPsqggAgL0c73MSCxO1zwkAAIieqOxzAgAAMNkIJwAAwCqEEwAAYBXCCQAAsArhBAAAWIVwAgAArEI4AQAAViGcAAAAqxBOAACAVRxvXx8Lw5vY9vX1xbgSAAAwXsOf2043o4+LcNLf3y9JysrKinElAADAqf7+fnm93nH3j4vv1hkaGtKpU6c0bdo0uVyJ9+V8fX19ysrK0okTJ/huIYsxTvGBcYoPjFN8+KZxMsaov79fs2bNUlLS+K8kiYuZk6SkJM2ZMyfWZcScx+PhlzQOME7xgXGKD4xTfPi6cXIyYzKMC2IBAIBVCCcAAMAqhJM4kJaWpsrKSqWlpcW6FHwNxik+ME7xgXGKD5M1TnFxQSwAAEgczJwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwoklampqlJOTI7fbrYKCAjU1NY3Z97XXXtOSJUt06aWXyuPxqLi4WG+99VYUq01cTsbpXPv371dKSoquueaayS0QkpyP0+DgoDZt2qTs7GylpaVp3rx52r59e5SqTVxOx2nnzp26+uqrdfHFF8vn8+nuu+9WT09PlKpNTO+8847Kyso0a9YsuVwuvfHGG994zt69e1VQUCC32625c+fq2Wefdf7CBjH3pz/9yUyZMsU8//zzpq2tzTz44INm6tSp5qOPPhq1/4MPPmi2bNliDh48aD744AOzceNGM2XKFHP48OEoV55YnI7TsE8++cTMnTvXlJaWmquvvjo6xSawSMbp1ltvNUVFRaaxsdG0t7ebf/zjH2b//v1RrDrxOB2npqYmk5SUZH73u9+Z48ePm6amJnPllVea5cuXR7nyxFJfX282bdpkXn31VSPJvP7661/b//jx4+biiy82Dz74oGlrazPPP/+8mTJlinnllVccvS7hxAKFhYWmoqIipO2KK64wGzZsGPdz5ObmmqqqqokuDeeIdJzKy8vNr3/9a1NZWUk4iQKn4/SXv/zFeL1e09PTE43y8F9Ox+k3v/mNmTt3bkjbU089ZebMmTNpNSLUeMLJL37xC3PFFVeEtP30pz811157raPXYlknxk6fPq3m5maVlpaGtJeWlurAgQPjeo6hoSH19/crPT19MkqEIh+nF154QceOHVNlZeVklwhFNk5vvvmmFi5cqMcff1yzZ8/W/Pnz9dBDD+mLL76IRskJKZJxKikp0cmTJ1VfXy9jjD7++GO98soruuWWW6JRMsbp3XffDRvXm266SYcOHdKXX3457ueJiy/+u5B1d3crGAwqIyMjpD0jI0OdnZ3jeo4nnnhCn332mVasWDEZJUKRjdOHH36oDRs2qKmpSSkp/KpFQyTjdPz4ce3bt09ut1uvv/66uru7dd9996m3t5frTiZJJONUUlKinTt3qry8XAMDAzpz5oxuvfVWPf3009EoGePU2dk56rieOXNG3d3d8vl843oeZk4s4XK5Qh4bY8LaRrNr1y49+uijqqur08yZMyerPPzXeMcpGAzq9ttvV1VVlebPnx+t8vBfTn6fhoaG5HK5tHPnThUWFurmm2/Wk08+qRdffJHZk0nmZJza2tq0Zs0aPfLII2publZDQ4Pa29tVUVERjVLhwGjjOlr71+HPuRibMWOGkpOTw/5a6OrqCkufX1VXV6d77rlHL7/8sm688cbJLDPhOR2n/v5+HTp0SC0tLXrggQcknf0QNMYoJSVFe/bs0Q033BCV2hNJJL9PPp9Ps2fPDvla9wULFsgYo5MnT+qyyy6b1JoTUSTjVF1drUWLFunhhx+WJF111VWaOnWqFi9erMcee2zcf5FjcmVmZo46rikpKZo+ffq4n4eZkxhLTU1VQUGBGhsbQ9obGxtVUlIy5nm7du3SXXfdpZdeeok11yhwOk4ej0fvvfeejhw5MnJUVFTo8ssv15EjR1RUVBSt0hNKJL9PixYt0qlTp/Tpp5+OtH3wwQdKSkrSnDlzJrXeRBXJOH3++edKSgr9yEpOTpb0//8yR+wVFxeHjeuePXu0cOFCTZkyZfxP5OjyWUyK4Vvqtm3bZtra2szatWvN1KlTzb///W9jjDEbNmwwd9xxx0j/l156yaSkpJhnnnnG+P3+keOTTz6J1VtICE7H6au4Wyc6nI5Tf3+/mTNnjvnhD39o3n//fbN3715z2WWXmXvvvTdWbyEhOB2nF154waSkpJiamhpz7Ngxs2/fPrNw4UJTWFgYq7eQEPr7+01LS4tpaWkxksyTTz5pWlpaRm75/uo4Dd9KvG7dOtPW1ma2bdvGrcTx7JlnnjHZ2dkmNTXV5Ofnm71794787M477zTXX3/9yOPrr7/eSAo77rzzzugXnmCcjNNXEU6ix+k4HT161Nx4443moosuMnPmzDHr1683n3/+eZSrTjxOx+mpp54yubm55qKLLjI+n8/8+Mc/NidPnoxy1Ynlr3/969d+3ow2Tn/729/M9773PZOammq+853vmNraWsev6zKG+TAAAGAPrjkBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCr/DzdtWXPqJwiFAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -1258,127 +1260,127 @@ "clustersimple\n", "\n", "simple: Workflow\n", - "\n", - "clustersimplesum\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sum: Add\n", - "\n", - "\n", - "clustersimplesumInputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Inputs\n", - "\n", - "\n", - "clustersimplesumOutputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Outputs\n", - "\n", "\n", "clustersimpleInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clustersimpleOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clustersimplea\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "a: AddOne\n", "\n", "\n", "clustersimpleaInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clustersimpleaOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clustersimpleb\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "b: AddOne\n", "\n", "\n", "clustersimplebInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clustersimplebOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", + "\n", + "clustersimplesum\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sum: Add\n", + "\n", + "\n", + "clustersimplesumInputs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Inputs\n", + "\n", + "\n", + "clustersimplesumOutputs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Outputs\n", + "\n", "\n", "\n", "clustersimpleInputsrun\n", @@ -1581,7 +1583,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 41, @@ -1618,7 +1620,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "893f572bcd43499b8911a35681c00a5d", + "model_id": "a009a947809e492687034b79a297274f", "version_major": 2, "version_minor": 0 }, @@ -1645,7 +1647,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 42, @@ -1664,30 +1666,21 @@ } ], "source": [ - "try:\n", - " wf.register(\"pyiron_atomistics\", \"pyiron_workflow.node_library.pyiron_atomistics\")\n", - " wf.register(\"plotting\", \"pyiron_workflow.node_library.plotting\")\n", + "wf.register(\"pyiron_atomistics\", \"pyiron_workflow.node_library.pyiron_atomistics\")\n", + "wf.register(\"plotting\", \"pyiron_workflow.node_library.plotting\")\n", "\n", - " wf = Workflow(\"with_prebuilt\")\n", + "wf = Workflow(\"with_prebuilt\")\n", "\n", - " wf.structure = wf.create.pyiron_atomistics.Bulk(cubic=True, name=\"Al\")\n", - " wf.engine = wf.create.pyiron_atomistics.Lammps(structure=wf.structure)\n", - " wf.calc = wf.create.pyiron_atomistics.CalcMd(job=wf.engine)\n", - " wf.plot = wf.create.plotting.Scatter(\n", - " x=wf.calc.outputs.steps, \n", - " y=wf.calc.outputs.temperature\n", - " )\n", + "wf.structure = wf.create.pyiron_atomistics.Bulk(cubic=True, name=\"Al\")\n", + "wf.engine = wf.create.pyiron_atomistics.Lammps(structure=wf.structure)\n", + "wf.calc = wf.create.pyiron_atomistics.CalcMd(job=wf.engine)\n", + "wf.plot = wf.create.plotting.Scatter(\n", + " x=wf.calc.outputs.steps, \n", + " y=wf.calc.outputs.temperature\n", + ")\n", "\n", - " out = wf.run()\n", - " out.plot__fig\n", - "except:\n", - " from pyiron_base import state\n", - " from pathlib import Path\n", - " import os\n", - " file_path='/usr/share/miniconda3/envs/my-env/share/pyiron/lammps/bin/run_lammps_2020.03.03.sh'\n", - " raise RuntimeError(\n", - " f\"Resource paths:{state.settings.configuration['resource_paths']}; exists {Path(file_path).exists()}; is file {Path(file_path).is_file()}; executable {os.access(file_path, os.X_OK)}\"\n", - " )" + "out = wf.run()\n", + "out.plot__fig" ] }, { @@ -1719,27 +1712,27 @@ "clusterwith_prebuilt\n", "\n", "with_prebuilt: Workflow\n", - "\n", - "clusterwith_prebuiltOutputs\n", + "\n", + "clusterwith_prebuiltInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", - "Outputs\n", + "\n", + "Inputs\n", "\n", - "\n", - "clusterwith_prebuiltInputs\n", + "\n", + "clusterwith_prebuiltOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", - "Inputs\n", + "\n", + "Outputs\n", "\n", "\n", "\n", @@ -1914,7 +1907,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 43, @@ -2164,292 +2157,292 @@ "clusterphase_preference\n", "\n", "phase_preference: Workflow\n", + "\n", + "clusterphase_preferenceInputs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Inputs\n", + "\n", "\n", "clusterphase_preferenceOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clusterphase_preferenceelement\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "element: UserInput\n", "\n", "\n", "clusterphase_preferenceelementInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferenceelementOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clusterphase_preferencemin_phase1\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "min_phase1: LammpsMinimize\n", "\n", "\n", "clusterphase_preferencemin_phase1Inputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencemin_phase1Outputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", + "\n", + "clusterphase_preferencemin_phase2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "min_phase2: LammpsMinimize\n", + "\n", + "\n", + "clusterphase_preferencemin_phase2Inputs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Inputs\n", + "\n", + "\n", + "clusterphase_preferencemin_phase2Outputs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Outputs\n", + "\n", "\n", "clusterphase_preferencee1\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "e1: GetItem\n", "\n", "\n", "clusterphase_preferencee1Inputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencee1Outputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clusterphase_preferencen1\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "n1: Length\n", "\n", "\n", "clusterphase_preferencen1Inputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencen1Outputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", - "\n", - "clusterphase_preferenceInputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Inputs\n", - "\n", "\n", "clusterphase_preferencee2\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "e2: GetItem\n", "\n", "\n", "clusterphase_preferencee2Inputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencee2Outputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clusterphase_preferencen2\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "n2: Length\n", "\n", "\n", "clusterphase_preferencen2Inputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencen2Outputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", "\n", "clusterphase_preferencee2__getitem_Divide_n2__len\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "e2__getitem_Divide_n2__len: Divide\n", "\n", "\n", "clusterphase_preferencee2__getitem_Divide_n2__lenInputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Inputs\n", "\n", "\n", "clusterphase_preferencee2__getitem_Divide_n2__lenOutputs\n", "\n", - "\n", + "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Outputs\n", "\n", - "\n", - "clusterphase_preferencemin_phase2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "min_phase2: LammpsMinimize\n", - "\n", - "\n", - "clusterphase_preferencemin_phase2Inputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Inputs\n", - "\n", - "\n", - "clusterphase_preferencemin_phase2Outputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Outputs\n", - "\n", "\n", "clusterphase_preferencee1__getitem_Divide_n1__len\n", "\n", @@ -3103,7 +3096,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 49, @@ -3386,7 +3379,7 @@ "output_type": "stream", "text": [ "None 1\n", - " \n" + " \n" ] } ], @@ -3468,7 +3461,7 @@ "output_type": "stream", "text": [ "None 1\n", - " \n", + " \n", "Finally 5\n", "b (Add) output single-value: 6\n" ] @@ -3530,7 +3523,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "6.0082737900083885\n" + "6.014687465998577\n" ] } ], @@ -3562,7 +3555,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.4378735430072993\n" + "2.447272224992048\n" ] } ], @@ -3786,9 +3779,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.361 > 0.2\n", - "0.097 <= 0.2\n", - "Finally 0.097\n" + "0.040 <= 0.2\n", + "Finally 0.040\n" ] } ], @@ -3861,7 +3853,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" } }, "nbformat": 4,