Commit fd2bd145 authored by Angelo Ziletti's avatar Angelo Ziletti
Browse files

Change parsing_utils to utils_parsing in LASSO_LO.

parent a2841cb2
......@@ -67,7 +67,7 @@
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 352
"height": 316
},
"evaluatorReader": true,
"lineCount": 16
......@@ -231,7 +231,7 @@
"</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;",
"",
"",
"<a target=\"_blank\" href=\"https://nomad-forum.rz-berlin.mpg.de//\" class=\"btn btn-primary\"> Tell us what you think</a>"
"<a target=\"_blank\" href=\"http://forum.analytics-toolkit.nomad-coe.eu/\" class=\"btn btn-primary\"> Tell us what you think</a>"
],
"hidden": true
},
......@@ -240,7 +240,7 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\nvar run_lasso = function() {\n $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");\n getFeatures();\n getOperators();\n getMaxDim();\n getUnits();\n beaker.evaluate(\"calc_cell\"); // evaluate cells with tag \"calc_cell\"\n // view_result()\n};\nvar reset_lasso = function(){\n beaker.evaluate(\"lasso-settings-cell\");\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n e.style.display = 'block';\n b.style.display = 'inline';\n};\nvar getFeatures = function() {\n beaker.selected_feature_list = [];\n $('#lasso_features_select input:checkbox').each(function () {\n if(this.checked )\n beaker.selected_feature_list.push(this.value);\n });\n};\nvar getOperators = function() {\n beaker.allowed_operations = [];\n $('#lasso_operators_select input:checkbox').each(function () {\n if(this.checked )\n beaker.allowed_operations.push(this.value);\n });\n}; \nvar getMaxDim = function() {\n beaker.max_dim = $(\"#lasso_max_dim_selector\").val();\n};\n \nvar getUnits = function() {\n beaker.units = $(\"#units_select\").val();\n};\nvar toggle_settings = function(){\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n if(e.style.display == 'block'){\n e.style.display = 'none';\n b.style.display = 'none';\n }\n else{\n e.style.display = 'block';\n b.style.display = 'inline';\n }\n};\nbeaker.view_result = function(result_link) {\n// beaker.evaluate(\"lasso_viewer_result\").then(function(x) {\n $(\"#lasso_result_button\").attr(\"href\", result_link);\n// }); \n $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");\n}\n</script>\n<style type=\"text/css\">\n .lasso_instructions{\n font-size: 15px;\n } \n</style>\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-motivation-modal\">\n Background\n</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n\n<!-- Modal -->\n<div style=\"display: none;\" class=\"modal fade\" id=\"lasso-motivation-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-motivation-modal-label\">\n <div class=\"modal-dialog modal-lg\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"lasso-motivation-modal-label\">Background</h4>\n </div>\n <div class=\"modal-body lasso_instructions\">\n <p> In this tutorial we present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material.</p>\n\n <p>In this example, we address only Rocksalt (RS) and Zincblende (ZB) crystal structures, that are the most common for the material class of octet binaries. Specifically, the tool predicts the difference in total energy between RS and ZB equilibrated structures (i.e., each structure is relaxed to its local minimum).</p>\n\n <p>The prediction of RS vs ZB structure from a simple descriptor has a notable history in materials science [1-7], where descriptors were designed by chemically/physically-inspired intuition. The tool presented here allows for the machine-learning-aided automatic discovery of a descriptor and a model for the prediction of the difference in energy between RS and ZB for 82 octet binary materials.</p>\n\n <p>The tool is based on Compressed-sensing (LASSO performed on a tailor-made feature space, followed by L0-regularized minimization, click <a href=\"https://gitlab.rzg.mpg.de/nomad-lab/public-wiki/wikis/analytics/LASSO_L0\" target=\"_blank\">here</a> for more info on the LASSO+L0 method), as introduced in: </p>\n\n <p> \"Big Data of Materials Science: Critical Role of the Descriptor\". L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, and M. Scheffler Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\"> (Click here for the free access pdf) </a></p>\n\n <p> By running the tutorial with the default setting, the results of the <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a> can be recovered. In particular, by clicking on “View interactive 2D plot”, an interactive structure-map (a chart where different structures are located in different regions of a low-dimensional representation, here two dimensional) will be opened in a new tab, similar to the following (an extended version of Fig. 2 in <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a>): </p>\n \n <img style=\"width:67%;height:67%\" src=\"https://gitlab.mpcdf.mpg.de/nomad-lab/public-wiki/uploads/eb33f1415db1b6d489cbbc3e6a899942/2016-08-02_ZB_RS3-2.png\">\n <br>\n <br>\n <p> In this map the octet binaries are located via the descriptor found by our LASSO+L0 approach. The descriptor is based purely on free-atom data, namely radii of the <i>s</i> and <i>p</i> valence orbitals (rs and rp) of the atomic species and their Ionizaiton Potential and Electron Affinity (IP and EA). Materials in the red (blue) region crystallize preferably in the zincblende (rocksalt) structure. The distance to the green line is proportional to the difference in energy between the two structures. In the interactive plot accessible at the end of the learning performed by the present tool, one can obtain information on the materials by hovering and clicking on the data points. </p>\n <p>References:</p>\n <ol>\n <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>\n <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>\n <li>J. St. John and A.N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>\n <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>\n <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>\n <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>\n <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>\n </ol>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-instructions-modal\">\n Instructions\n</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n\n<!-- Modal -->\n<div class=\"modal fade\" id=\"lasso-instructions-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-instructions-modal-label\">\n <div class=\"modal-dialog\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"lasso-instructions-modal-label\">Instructions</h4>\n </div>\n <div class=\"modal-body lasso_instructions\">\n<p>In this example, you can run a compressed-sensing based algorithm for finding the optimal descriptor (and model) that predicts the difference in energy between crystal structures (here, rocksalt vs. zincblende). </p>\n\n<p>The descriptor is selected out of a large number of candidates constructed as functions of basic input features, the primary features. </p>\n\n<p>You can select the primary features as well as which kind of unary and binary operations are allowed from the checklist below. You can also select the maximum dimensionality of the descriptor. </p>\n<p> After the wished features have been selected, click <b>RUN</b> to perform the calculations (loading the values of the primary features, creation of the feature space, and optimization via LASSO+L0). </p>\n\nDuring the run, a brief summary is printed out below the <b>RUN</b> button. At the end of the run: \n <ul>\n <li> the solution (machine-learned descriptor, model, and its performance in terms of training error) is printed out underneath starting from the one-dimensional solution to the selected maximum dimensionality and</li>\n<li> the “View interactive 2D scatter plot” button unlocks; by clicking, the scatter plot with the two-dimensional descriptor appears in a separate tab. In case a dimensionality higher than 2 was selected for the descriptor, the plot displays the first two dimensions.</li>\n</ul>\n<p>Note: the plot stays active also after another run is performed, so that the output of several sets of input parameters can be compared in the viewer tabs.</p>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" onclick=\"toggle_settings()\">\n Settings\n</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n\n\n<a target=\"_blank\" href=\"https://nomad-forum.rz-berlin.mpg.de//\" class=\"btn btn-primary\"> Tell us what you think</a>"
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\nvar run_lasso = function() {\n $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");\n getFeatures();\n getOperators();\n getMaxDim();\n getUnits();\n beaker.evaluate(\"calc_cell\"); // evaluate cells with tag \"calc_cell\"\n // view_result()\n};\nvar reset_lasso = function(){\n beaker.evaluate(\"lasso-settings-cell\");\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n e.style.display = 'block';\n b.style.display = 'inline';\n};\nvar getFeatures = function() {\n beaker.selected_feature_list = [];\n $('#lasso_features_select input:checkbox').each(function () {\n if(this.checked )\n beaker.selected_feature_list.push(this.value);\n });\n};\nvar getOperators = function() {\n beaker.allowed_operations = [];\n $('#lasso_operators_select input:checkbox').each(function () {\n if(this.checked )\n beaker.allowed_operations.push(this.value);\n });\n}; \nvar getMaxDim = function() {\n beaker.max_dim = $(\"#lasso_max_dim_selector\").val();\n};\n \nvar getUnits = function() {\n beaker.units = $(\"#units_select\").val();\n};\nvar toggle_settings = function(){\n var e = document.getElementById('lasso-hidden-settings-div');\n var b = document.getElementById('lasso-hidden-settings-button');\n if(e.style.display == 'block'){\n e.style.display = 'none';\n b.style.display = 'none';\n }\n else{\n e.style.display = 'block';\n b.style.display = 'inline';\n }\n};\nbeaker.view_result = function(result_link) {\n// beaker.evaluate(\"lasso_viewer_result\").then(function(x) {\n $(\"#lasso_result_button\").attr(\"href\", result_link);\n// }); \n $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");\n}\n</script>\n<style type=\"text/css\">\n .lasso_instructions{\n font-size: 15px;\n } \n</style>\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-motivation-modal\">\n Background\n</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n\n<!-- Modal -->\n<div style=\"display: none;\" class=\"modal fade\" id=\"lasso-motivation-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-motivation-modal-label\">\n <div class=\"modal-dialog modal-lg\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"lasso-motivation-modal-label\">Background</h4>\n </div>\n <div class=\"modal-body lasso_instructions\">\n <p> In this tutorial we present a tool for predicting the crystal structure of octet binary compounds, by using a set of descriptive parameters (a descriptor) based on free-atom data of the atomic species constituting the binary material.</p>\n\n <p>In this example, we address only Rocksalt (RS) and Zincblende (ZB) crystal structures, that are the most common for the material class of octet binaries. Specifically, the tool predicts the difference in total energy between RS and ZB equilibrated structures (i.e., each structure is relaxed to its local minimum).</p>\n\n <p>The prediction of RS vs ZB structure from a simple descriptor has a notable history in materials science [1-7], where descriptors were designed by chemically/physically-inspired intuition. The tool presented here allows for the machine-learning-aided automatic discovery of a descriptor and a model for the prediction of the difference in energy between RS and ZB for 82 octet binary materials.</p>\n\n <p>The tool is based on Compressed-sensing (LASSO performed on a tailor-made feature space, followed by L0-regularized minimization, click <a href=\"https://gitlab.rzg.mpg.de/nomad-lab/public-wiki/wikis/analytics/LASSO_L0\" target=\"_blank\">here</a> for more info on the LASSO+L0 method), as introduced in: </p>\n\n <p> \"Big Data of Materials Science: Critical Role of the Descriptor\". L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, and M. Scheffler Phys. Rev. Lett. 114, 105503 (2015) <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\"> (Click here for the free access pdf) </a></p>\n\n <p> By running the tutorial with the default setting, the results of the <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a> can be recovered. In particular, by clicking on “View interactive 2D plot”, an interactive structure-map (a chart where different structures are located in different regions of a low-dimensional representation, here two dimensional) will be opened in a new tab, similar to the following (an extended version of Fig. 2 in <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\">PRL 2015</a>): </p>\n \n <img style=\"width:67%;height:67%\" src=\"https://gitlab.mpcdf.mpg.de/nomad-lab/public-wiki/uploads/eb33f1415db1b6d489cbbc3e6a899942/2016-08-02_ZB_RS3-2.png\">\n <br>\n <br>\n <p> In this map the octet binaries are located via the descriptor found by our LASSO+L0 approach. The descriptor is based purely on free-atom data, namely radii of the <i>s</i> and <i>p</i> valence orbitals (rs and rp) of the atomic species and their Ionizaiton Potential and Electron Affinity (IP and EA). Materials in the red (blue) region crystallize preferably in the zincblende (rocksalt) structure. The distance to the green line is proportional to the difference in energy between the two structures. In the interactive plot accessible at the end of the learning performed by the present tool, one can obtain information on the materials by hovering and clicking on the data points. </p>\n <p>References:</p>\n <ol>\n <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>\n <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>\n <li>J. St. John and A.N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>\n <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>\n <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>\n <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>\n <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>\n </ol>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-instructions-modal\">\n Instructions\n</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n\n<!-- Modal -->\n<div class=\"modal fade\" id=\"lasso-instructions-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-instructions-modal-label\">\n <div class=\"modal-dialog\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"lasso-instructions-modal-label\">Instructions</h4>\n </div>\n <div class=\"modal-body lasso_instructions\">\n<p>In this example, you can run a compressed-sensing based algorithm for finding the optimal descriptor (and model) that predicts the difference in energy between crystal structures (here, rocksalt vs. zincblende). </p>\n\n<p>The descriptor is selected out of a large number of candidates constructed as functions of basic input features, the primary features. </p>\n\n<p>You can select the primary features as well as which kind of unary and binary operations are allowed from the checklist below. You can also select the maximum dimensionality of the descriptor. </p>\n<p> After the wished features have been selected, click <b>RUN</b> to perform the calculations (loading the values of the primary features, creation of the feature space, and optimization via LASSO+L0). </p>\n\nDuring the run, a brief summary is printed out below the <b>RUN</b> button. At the end of the run: \n <ul>\n <li> the solution (machine-learned descriptor, model, and its performance in terms of training error) is printed out underneath starting from the one-dimensional solution to the selected maximum dimensionality and</li>\n<li> the “View interactive 2D scatter plot” button unlocks; by clicking, the scatter plot with the two-dimensional descriptor appears in a separate tab. In case a dimensionality higher than 2 was selected for the descriptor, the plot displays the first two dimensions.</li>\n</ul>\n<p>Note: the plot stays active also after another run is performed, so that the output of several sets of input parameters can be compared in the viewer tabs.</p>\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n</div>\n\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" onclick=\"toggle_settings()\">\n Settings\n</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n\n\n<a target=\"_blank\" href=\"http://forum.analytics-toolkit.nomad-coe.eu/\" class=\"btn btn-primary\"> Tell us what you think</a>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
......@@ -374,7 +374,7 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<div class=\"lasso_control\">\n\n <p style=\"margin-top: 1ex;\"></p>\n <button class=\"btn btn-default\" onclick=\"run_lasso()\" style=\"font-weight: bold;\">RUN</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n <div id=\"lasso-hidden-settings-button\"><button class=\"btn btn-default\" onclick=\"reset_lasso()\">RESET</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</div>\n <label title=\"This button becomes active when the\nrun is finished. By clicking it, an interactive plot of the first 2\ndimensions of the optimized descriptor will be opened\"> \n <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\">View interactive 2D scatter plot</a> </label>\n</div>"
"object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<div class=\"lasso_control\">\n\n <p style=\"margin-top: 1ex;\"></p>\n <button class=\"btn btn-default\" onclick=\"run_lasso()\" style=\"font-weight: bold;\">RUN</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n <div style=\"display: none;\" id=\"lasso-hidden-settings-button\"><button class=\"btn btn-default\" onclick=\"reset_lasso()\">RESET</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</div>\n <label title=\"This button becomes active when the\nrun is finished. By clicking it, an interactive plot of the first 2\ndimensions of the optimized descriptor will be opened\"> \n <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\">View interactive 2D scatter plot</a> </label>\n</div>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
......@@ -391,7 +391,7 @@
"body": [
"from nomad_sim.wrappers import get_json_list, calc_descriptor ",
"from nomad_sim.wrappers import calc_model, calc_embedding, plot",
"#from nomad_sim.parsing_utils import read_gdb_7k",
"from nomad_sim.utils_parsing import read_gdb_7k",
"from nomad_sim.utils_crystals import create_supercell",
"from nomad_sim.utils_crystals import create_vacancies",
"from nomad_sim.utils_crystals import random_displace_atoms",
......@@ -436,10 +436,10 @@
},
"output": {
"state": {},
"selectedType": "Results",
"selectedType": "BeakerDisplay",
"pluginName": "IPython",
"shellId": "6EF6C49F5D2D40F383601486E08EC433",
"elapsedTime": 3516
"shellId": "FC7AB4B3E63044AB93210497D20C6A4D",
"elapsedTime": 947
},
"evaluatorReader": true,
"tags": "calc_cell",
......@@ -545,8 +545,8 @@
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
"shellId": "6EF6C49F5D2D40F383601486E08EC433",
"elapsedTime": 425,
"shellId": "FC7AB4B3E63044AB93210497D20C6A4D",
"elapsedTime": 409,
"height": 986
},
"evaluatorReader": true,
......@@ -605,8 +605,8 @@
"state": {},
"selectedType": "Results",
"pluginName": "IPython",
"shellId": "6EF6C49F5D2D40F383601486E08EC433",
"elapsedTime": 17191,
"shellId": "FC7AB4B3E63044AB93210497D20C6A4D",
"elapsedTime": 15397,
"height": 100
},
"evaluatorReader": true,
......@@ -635,8 +635,8 @@
"state": {},
"selectedType": "Results",
"pluginName": "IPython",
"shellId": "6EF6C49F5D2D40F383601486E08EC433",
"elapsedTime": 12106,
"shellId": "FC7AB4B3E63044AB93210497D20C6A4D",
"elapsedTime": 8112,
"height": 78
},
"evaluatorReader": true,
......@@ -675,8 +675,8 @@
"selectedType": "Results",
"height": 78,
"pluginName": "IPython",
"shellId": "6EF6C49F5D2D40F383601486E08EC433",
"elapsedTime": 10695
"shellId": "FC7AB4B3E63044AB93210497D20C6A4D",
"elapsedTime": 7813
},
"evaluatorReader": true,
"lineCount": 18,
......@@ -698,7 +698,7 @@
"selectedType": "BeakerDisplay",
"height": 78,
"pluginName": "JavaScript",
"elapsedTime": 85
"elapsedTime": 54
},
"evaluatorReader": true,
"lineCount": 2,
......@@ -727,7 +727,7 @@
"n_sis": "50",
"n_comb": "1",
"viewer_result": "d934cec27120a732",
"units": "eV_angstrom"
"units": "J_m"
},
"locked": true
}
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment