diff --git a/custom-analytics-example/custom-analytics-example-dev.bkr b/custom-analytics-example/custom-analytics-example-dev.bkr
index 72ec3844cd1a670e821bd8916887a02ad2669b5c..8c58589dedc4f85201604434c4af29debfe53010 100644
--- a/custom-analytics-example/custom-analytics-example-dev.bkr
+++ b/custom-analytics-example/custom-analytics-example-dev.bkr
@@ -127,8 +127,8 @@
"body": [
"### Introduction",
"",
- "This Notebook allows you to retreive your queries on the NOMAD Archive, and subsequently perform state-of-the-art machine learning analysis on the set of data retrieved from the NOMAD Archive. <br>",
- "Given a user-specified query, the structures of the top-5 space groups (for crystals) or point groups (for molecules) relative to this query are returned, and then a structural-similarity model is calculated on-the-fly. <br>",
+ "This Notebook allows to retrieve your query results from the NOMAD Archive, and subsequently perform state-of-the-art machine learning analysis on this dataset. <br>",
+ "Given a user-specified query, the structures of the top-5 space groups (for crystals) or point groups (for molecules) within this dataset are returned, and then a structural-similarity model is built on-the-fly. <br>",
"Please see \"Read Usage Instructions\" and the \"Reference\" below for more details.",
"",
"<p style=\"font-size: 15px;\"> <b> Machine learning method: </b> Nonlinear dimensionality reduction based on SOAP similarity kernel [<a href=\"https://arxiv.org/abs/1601.04077\" target=\"_blank\"> S. De, A. Bartók, G. Csányi, and M. Ceriotti: \"Comparing molecules and solids across structural and chemical space\", Phys. Chem. Chem. Phys. (2016)</a>]"
@@ -212,33 +212,38 @@
" <p>The task is to retrieve a query previously performed on the NOMAD Archive and build on-the-fly structural-similarity models for the navigation and exploration of materials. The procedure is composed of two steps, reflected in the control panel further below:</p>",
" <ol>",
" <li> <b>Query of the NOMAD Archive</b> <br/>",
- " ",
- " <i> Name of query to analyze </i> specifies which query results will be retrieved from the NOMAD Archive and then analyzed. By default the last query is used.",
- " The keyword <i> Max number or results for each space group/point group </i> allows to choose the maximum number of results shown for each of the top-5 spacegroups. Small number of results will lead to fast analysis.",
+ " <ul>",
+ " <li> <i> Name of query to analyze </i> <br/>",
+ " It specifies which query results will be retrieved from the NOMAD Archive and then analyzed. By default the last query is used. </li>",
+ " ",
+ " <li> <i> Max number or results for each space group/point group </i> <br/>",
+ " It defines the maximum number of results shown for each of the top-5 spacegroups (for crystals) or point groups (for molecules). Small number of results will lead to fast analysis.",
" </li>",
- "",
+ " <br/>",
+ " </ul>",
+ " ",
" <li> <b>On-the-fly data analysis</b> <br/>",
" A structural-similarity map of the results retrieved from the NOMAD Archive is calculated on-the-fly using the SOAP framework, as detailed below.",
" For additional information (and references) about the SOAP method, please refer to the section \"Motivation\" of this notebook.",
" </li>",
" <ul>",
- " <li> <i>Definition of the atomic density</i> ",
+ " <li> <i>Definition of the atomic density</i> <br/>",
" Within the SOAP framework, the atomic density is modelled as a linear superposition of atom-centered spherical Gaussians.",
" The density coefficient of these Gaussians as well as their width can be chosen atom-type-dependent (based on, e.g., covalent radius, ",
" core charge, valence charge, etc.) or be set constant. Additionally, the density expansions can be performed separately for each atomic type ",
" (leading to an element-specific representation and hence cross-terms in the power spectrum) or for the total, summed density (leading to an element-agnostic representation).</li>",
" <br>",
- " <li> <i>Specification of the basis set</i> ",
+ " <li> <i>Specification of the basis set</i> <br/>",
" Prior to rotational averaging, the atomic density is expanded in terms of radial Gaussians and spherical harmonics. The radial basis set can be chosen ",
" equispaced or adaptive. In the latter case, the centres and widths of the Gaussians are optimized such that radial and angular resolution are matched, while providing",
" sufficient overlap between neighbouring Gaussians. The effective radial cutoff is then determined by the number of radial basis functions and the width of the innermost Gaussian.",
" The angular basis set is fully specified by the angular-momentum cutoff <i>L</i>. </li>",
" <br>",
- " <li> <i>Configuration of the base kernel</i> ",
+ " <li> <i>Configuration of the base kernel</i> <br/>",
" The base kernel defines how the similarity between pairs of atomic environments is to be computed. ",
" SOAP has a native kernel, which is simply the dot-product between the descriptor vectors, raised to some positive power (3 is usually a good choice). </li>",
" <br>",
- " <li> <i>Configuration of the top kernel</i> ",
+ " <li> <i>Configuration of the top kernel</i> <br/>",
" The top kernel specifies how two sets of atomic environments (i.e., structures rather than individual atomic environments) should be compared. ",
" A straightforward way to accomplish this is to compute an \"average\" descriptor for both structures, and submit these to the base kernel. This \"average\" can either be formed",
" coherently (in which case the rotational averaging is performed after summation of the atomic density expansions) or incoherently (through direct averaging of the atomic descriptor vectors).",
@@ -246,8 +251,8 @@
" and a spectral graph kernel computed from the spectral overlap of the Markov random field induced by the graph Laplacian of a structure. ",
" The top kernel can be applied hierarchically: In this case, \"super-atom\" environments/kernels are recursively built from the original atomic environments ",
" via a sequence of subgraphs. The subgraphs are obtained sequentially, starting from a base cutoff radius, which is then scaled by a constant factor from ",
- " one level of the hierarchy to the next.</li><br>",
- " <li> <i>Specification of the embedding procedure</i> ",
+ " one level of the hierarchy to the next.</li><br/>",
+ " <li> <i>Specification of the embedding procedure</i> <br/> ",
" The similarity/distance between structures can be visualized through decomposition of the kernel/distance matrix. Here, this is achieved",
" via a spectral decomposition of the kernel matrix and subsequent projection onto principal components (Kernel PCA) i.e., ",
" by an approximate representation of relative distances as induced by the kernel in a low-dimensional space. </li> ",
@@ -277,14 +282,14 @@
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- "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#modal-credits\" style=\"font-size:larger;\">Motivation</button>\n<div class=\"modal fade\" id=\"modal-credits\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-credits\">\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 <h1 class=\"modal-title\" id=\"glosim-intro-modal-label\">Motivation</h1>\n <hr>\n </div>\n <div class=\"modal-body glosim_instructions\">\n <!--p> In this tutorial we present a tool for ...</p-->\n <p>\n The initial compilation and early-stage analysis of materials datasets relies on generic tools that enable\n </p><ul>\n <li>insight into how materials within the dataset relate to each other in terms of chemical composition and structure, </li>\n <li>feedback on whether or not the selection covers the intended configurational space (or whether it perhaps has \"holes\" that need to be filled),</li>\n <li>identification of potential subsets/-classes within the chosen data for further subselection and analysis.</li>\n </ul> \n <p></p>\n \n <p>\n Tailored to this purpose, the toolchain presented in this tutorial helps to map and visualize materials databases using generic\n kernel- and graph-based similarity measures together with the powerful SOAP descriptor for atomic environments.\n As such it also serves as a basis for a range of automated, quantitative data-mining techniques:\n </p>\n \n <p> \"Evaluating the <a href=\"http://pubs.rsc.org/is/content/articlelanding/2016/cp/c6cp00415f#!divAbstract\" target=\"_blank\">(dis)similarity of crystalline, disordered and molecular compounds</a> is a critical \n step in the development of algorithms to navigate automatically the configuration space of complex materials. \n For instance, a structural similarity metric is crucial for classifying structures, searching chemical space for \n better compounds and materials, and to drive the next generation of machine-learning techniques for predicting the\n stability and properties of molecules and materials. In the last few years several strategies have been designed to \n compare atomic coordination environments. In particular, the <a href=\"http://journals.aps.org/prb/abstract/10.1103/PhysRevB.87.184115\" target=\"_blank\">Smooth Overlap of Atomic Positions (SOAP)</a> has emerged as \n a natural framework to obtain translation, rotation and permutation-invariant descriptors of groups of atoms, driven by \n the design of various classes of machine-learned inter-atomic potentials.\"\n </p>\n \n <br>\n <br>\n\n <p>\n References:</p>\n <ol>\n <li>\n Sandip De, Albert P. Bartók, Gábor Csányi, Michele Ceriotti: \"Comparing molecules and \n solids across structural and alchemical space\". Phys. Chem. Chem. Phys. 18, 13754-13769 (2016)</li>\n <li>Albert Bartók, Risi Kondor, Gábor Csányi: \"On Representing Chemical Environments\". Physical Review B 87, 184115 (2013)</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\n\n<button type=\"button\" class=\"btn btn-info\" data-toggle=\"modal\" data-target=\"#modal-instructions\" style=\"font-size:larger;\">Read usage instructions</button>\n<div class=\"modal fade\" id=\"modal-instructions\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-instructions\">\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 <h1 class=\"modal-title\" id=\"modal-instructions-label\">Instructions</h1>\n <hr>\n </div>\n <div class=\"modal-body modal-instruction-body\">\n <p>The task is to retrieve a query previously performed on the NOMAD Archive and build on-the-fly structural-similarity models for the navigation and exploration of materials. The procedure is composed of two steps, reflected in the control panel further below:</p>\n <ol>\n <li> <b>Query of the NOMAD Archive</b> <br>\n \n <i> Name of query to analyze </i> specifies which query results will be retrieved from the NOMAD Archive and then analyzed. By default the last query is used.\n The keyword <i> Max number or results for each space group/point group </i> allows to choose the maximum number of results shown for each of the top-5 spacegroups. Small number of results will lead to fast analysis.\n </li>\n\n <li> <b>On-the-fly data analysis</b> <br>\n A structural-similarity map of the results retrieved from the NOMAD Archive is calculated on-the-fly using the SOAP framework, as detailed below.\n For additional information (and references) about the SOAP method, please refer to the section \"Motivation\" of this notebook.\n </li>\n <ul>\n <li> <i>Definition of the atomic density</i> \n Within the SOAP framework, the atomic density is modelled as a linear superposition of atom-centered spherical Gaussians.\n The density coefficient of these Gaussians as well as their width can be chosen atom-type-dependent (based on, e.g., covalent radius, \n core charge, valence charge, etc.) or be set constant. Additionally, the density expansions can be performed separately for each atomic type \n (leading to an element-specific representation and hence cross-terms in the power spectrum) or for the total, summed density (leading to an element-agnostic representation).</li>\n <br>\n <li> <i>Specification of the basis set</i> \n Prior to rotational averaging, the atomic density is expanded in terms of radial Gaussians and spherical harmonics. The radial basis set can be chosen \n equispaced or adaptive. In the latter case, the centres and widths of the Gaussians are optimized such that radial and angular resolution are matched, while providing\n sufficient overlap between neighbouring Gaussians. The effective radial cutoff is then determined by the number of radial basis functions and the width of the innermost Gaussian.\n The angular basis set is fully specified by the angular-momentum cutoff <i>L</i>. </li>\n <br>\n <li> <i>Configuration of the base kernel</i> \n The base kernel defines how the similarity between pairs of atomic environments is to be computed. \n SOAP has a native kernel, which is simply the dot-product between the descriptor vectors, raised to some positive power (3 is usually a good choice). </li>\n <br>\n <li> <i>Configuration of the top kernel</i> \n The top kernel specifies how two sets of atomic environments (i.e., structures rather than individual atomic environments) should be compared. \n A straightforward way to accomplish this is to compute an \"average\" descriptor for both structures, and submit these to the base kernel. This \"average\" can either be formed\n coherently (in which case the rotational averaging is performed after summation of the atomic density expansions) or incoherently (through direct averaging of the atomic descriptor vectors).\n Other options in this tutorial include the regularized-entropy kernel, which interpolates between the incoherent average above and an atomic pairwise best match, \n and a spectral graph kernel computed from the spectral overlap of the Markov random field induced by the graph Laplacian of a structure. \n The top kernel can be applied hierarchically: In this case, \"super-atom\" environments/kernels are recursively built from the original atomic environments \n via a sequence of subgraphs. The subgraphs are obtained sequentially, starting from a base cutoff radius, which is then scaled by a constant factor from \n one level of the hierarchy to the next.</li><br>\n <li> <i>Specification of the embedding procedure</i> \n The similarity/distance between structures can be visualized through decomposition of the kernel/distance matrix. Here, this is achieved\n via a spectral decomposition of the kernel matrix and subsequent projection onto principal components (Kernel PCA) i.e., \n by an approximate representation of relative distances as induced by the kernel in a low-dimensional space. </li> \n </ul>\n </ol>\n \n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\" style=\"font-size:larger;\">Close</button>\n </div>\n </div>\n </div>\n</div>\n\n\n\n\n\n\n\n<p></p>"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#modal-credits\" style=\"font-size:larger;\">Motivation</button>\n<div class=\"modal fade\" id=\"modal-credits\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-credits\" style=\"display: none;\">\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 <h1 class=\"modal-title\" id=\"glosim-intro-modal-label\">Motivation</h1>\n <hr>\n </div>\n <div class=\"modal-body glosim_instructions\">\n <!--p> In this tutorial we present a tool for ...</p-->\n <p>\n The initial compilation and early-stage analysis of materials datasets relies on generic tools that enable\n </p><ul>\n <li>insight into how materials within the dataset relate to each other in terms of chemical composition and structure, </li>\n <li>feedback on whether or not the selection covers the intended configurational space (or whether it perhaps has \"holes\" that need to be filled),</li>\n <li>identification of potential subsets/-classes within the chosen data for further subselection and analysis.</li>\n </ul> \n <p></p>\n \n <p>\n Tailored to this purpose, the toolchain presented in this tutorial helps to map and visualize materials databases using generic\n kernel- and graph-based similarity measures together with the powerful SOAP descriptor for atomic environments.\n As such it also serves as a basis for a range of automated, quantitative data-mining techniques:\n </p>\n \n <p> \"Evaluating the <a href=\"http://pubs.rsc.org/is/content/articlelanding/2016/cp/c6cp00415f#!divAbstract\" target=\"_blank\">(dis)similarity of crystalline, disordered and molecular compounds</a> is a critical \n step in the development of algorithms to navigate automatically the configuration space of complex materials. \n For instance, a structural similarity metric is crucial for classifying structures, searching chemical space for \n better compounds and materials, and to drive the next generation of machine-learning techniques for predicting the\n stability and properties of molecules and materials. In the last few years several strategies have been designed to \n compare atomic coordination environments. In particular, the <a href=\"http://journals.aps.org/prb/abstract/10.1103/PhysRevB.87.184115\" target=\"_blank\">Smooth Overlap of Atomic Positions (SOAP)</a> has emerged as \n a natural framework to obtain translation, rotation and permutation-invariant descriptors of groups of atoms, driven by \n the design of various classes of machine-learned inter-atomic potentials.\"\n </p>\n \n <br>\n <br>\n\n <p>\n References:</p>\n <ol>\n <li>\n Sandip De, Albert P. Bartók, Gábor Csányi, Michele Ceriotti: \"Comparing molecules and \n solids across structural and alchemical space\". Phys. Chem. Chem. Phys. 18, 13754-13769 (2016)</li>\n <li>Albert Bartók, Risi Kondor, Gábor Csányi: \"On Representing Chemical Environments\". Physical Review B 87, 184115 (2013)</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\n\n<button type=\"button\" class=\"btn btn-info\" data-toggle=\"modal\" data-target=\"#modal-instructions\" style=\"font-size:larger;\">Read usage instructions</button>\n<div class=\"modal fade\" id=\"modal-instructions\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"modal-instructions\" style=\"display: none;\">\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 <h1 class=\"modal-title\" id=\"modal-instructions-label\">Instructions</h1>\n <hr>\n </div>\n <div class=\"modal-body modal-instruction-body\">\n <p>The task is to retrieve a query previously performed on the NOMAD Archive and build on-the-fly structural-similarity models for the navigation and exploration of materials. The procedure is composed of two steps, reflected in the control panel further below:</p>\n <ol>\n <li> <b>Query of the NOMAD Archive</b> <br>\n <ul>\n <li> <i> Name of query to analyze </i> <br>\n It specifies which query results will be retrieved from the NOMAD Archive and then analyzed. By default the last query is used. </li>\n \n <li> <i> Max number or results for each space group/point group </i> <br>\n It defines the maximum number of results shown for each of the top-5 spacegroups (for crystals) or point groups (for molecules). Small number of results will lead to fast analysis.\n </li>\n <br>\n </ul>\n \n </li><li> <b>On-the-fly data analysis</b> <br>\n A structural-similarity map of the results retrieved from the NOMAD Archive is calculated on-the-fly using the SOAP framework, as detailed below.\n For additional information (and references) about the SOAP method, please refer to the section \"Motivation\" of this notebook.\n </li>\n <ul>\n <li> <i>Definition of the atomic density</i> <br>\n Within the SOAP framework, the atomic density is modelled as a linear superposition of atom-centered spherical Gaussians.\n The density coefficient of these Gaussians as well as their width can be chosen atom-type-dependent (based on, e.g., covalent radius, \n core charge, valence charge, etc.) or be set constant. Additionally, the density expansions can be performed separately for each atomic type \n (leading to an element-specific representation and hence cross-terms in the power spectrum) or for the total, summed density (leading to an element-agnostic representation).</li>\n <br>\n <li> <i>Specification of the basis set</i> <br>\n Prior to rotational averaging, the atomic density is expanded in terms of radial Gaussians and spherical harmonics. The radial basis set can be chosen \n equispaced or adaptive. In the latter case, the centres and widths of the Gaussians are optimized such that radial and angular resolution are matched, while providing\n sufficient overlap between neighbouring Gaussians. The effective radial cutoff is then determined by the number of radial basis functions and the width of the innermost Gaussian.\n The angular basis set is fully specified by the angular-momentum cutoff <i>L</i>. </li>\n <br>\n <li> <i>Configuration of the base kernel</i> <br>\n The base kernel defines how the similarity between pairs of atomic environments is to be computed. \n SOAP has a native kernel, which is simply the dot-product between the descriptor vectors, raised to some positive power (3 is usually a good choice). </li>\n <br>\n <li> <i>Configuration of the top kernel</i> <br>\n The top kernel specifies how two sets of atomic environments (i.e., structures rather than individual atomic environments) should be compared. \n A straightforward way to accomplish this is to compute an \"average\" descriptor for both structures, and submit these to the base kernel. This \"average\" can either be formed\n coherently (in which case the rotational averaging is performed after summation of the atomic density expansions) or incoherently (through direct averaging of the atomic descriptor vectors).\n Other options in this tutorial include the regularized-entropy kernel, which interpolates between the incoherent average above and an atomic pairwise best match, \n and a spectral graph kernel computed from the spectral overlap of the Markov random field induced by the graph Laplacian of a structure. \n The top kernel can be applied hierarchically: In this case, \"super-atom\" environments/kernels are recursively built from the original atomic environments \n via a sequence of subgraphs. The subgraphs are obtained sequentially, starting from a base cutoff radius, which is then scaled by a constant factor from \n one level of the hierarchy to the next.</li><br>\n <li> <i>Specification of the embedding procedure</i> <br> \n The similarity/distance between structures can be visualized through decomposition of the kernel/distance matrix. Here, this is achieved\n via a spectral decomposition of the kernel matrix and subsequent projection onto principal components (Kernel PCA) i.e., \n by an approximate representation of relative distances as induced by the kernel in a low-dimensional space. </li> \n </ul>\n </ol>\n \n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\" style=\"font-size:larger;\">Close</button>\n </div>\n </div>\n </div>\n</div>\n\n\n\n\n\n\n\n<p></p>"
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"elapsedTime": 0,
"height": 101
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"evaluatorReader": true,
- "lineCount": 128,
+ "lineCount": 133,
"isError": false
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@@ -337,18 +342,9 @@
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"selectedType": "Results",
"pluginName": "IPython",
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- "elapsedTime": 3312,
- "height": 61,
- "result": {
- "type": "Results",
- "outputdata": [
- {
- "type": "err",
- "value": "Using TensorFlow backend.\n"
- }
- ]
- }
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 3361,
+ "height": 61
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"evaluatorReader": true,
"lineCount": 37,
@@ -376,17 +372,18 @@
"#query = 'program_name:FHI-aims AND system_reweighted_composition:ZrO2'",
"nomad_query_result = nomad_query.query(query=query, group_by='', context='', timeout=10)",
"nomad_query.save(name='VASP', query=nomad_query_result, resolve=False)"
- ]
+ ],
+ "hidden": true
},
"output": {
"state": {},
"selectedType": "Html",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 982,
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 844,
"dataresult": {
"name": "VASP",
- "timestamp": 1516186096,
+ "timestamp": 1516195612,
"uri": [
"nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/CC5fKTH2kdJRI-FZJwdNbfielxLZ9",
"nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/CFwfKX9KoRAm9nqtlQ2wgbIBQcm_d",
@@ -1989,7 +1986,7 @@
"nmd://NxztWmbdchjpJagL0juCNKI3ZlNP7/C1ndH9CKOJnYwVTE8r8UwezAM24CK",
"nmd://NUMBpwzrUeuOHrlgiKX7e8asGCTVq/CDB8zTkgGo7jl_diLmle6vz_s85Rp"
],
- "filename": "2018-01-17 104816 VASP.json",
+ "filename": "2018-01-17 132652 VASP.json",
"version": 1,
"query": {
"filter": "system_reweighted_composition:Fe100 AND program_name:VASP",
@@ -1998,11 +1995,11 @@
"endpoint": "https://analytics-toolkit.nomad-coe.eu/api/",
"context": "single_configuration_calculation"
},
- "path": "data/2018-01-17 104816 VASP.json",
+ "path": "/data/private/angeloziletti/nomad-query/data/2018-01-17 132652 VASP.json",
"data": {}
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"height": 50,
- "result": "<div class=\"output_subarea output_text\"><pre>{'data': {},\n 'filename': '2018-01-17 104816 VASP.json',\n 'name': 'VASP',\n 'path': 'data/2018-01-17 104816 VASP.json',\n 'query': {'context': 'single_configuration_calculation',\n 'endpoint': 'https://analytics-toolkit.nomad-coe.eu/api/',\n 'filter': 'system_reweighted_composition:Fe100 AND program_name:VASP',\n 'group_by': '',\n 'url': 'https://analytics-toolkit.nomad-coe.eu/api/query/single_configuration_calculation?filter=system_reweighted_composition%3AFe100%20AND%20program_name%3AVASP'},\n 'timestamp': 1516186096,\n 'uri': [u'nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/CC5fKTH2kdJRI-FZJwdNbfielxLZ9',\n u'nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/CFwfKX9KoRAm9nqtlQ2wgbIBQcm_d',\n u'nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/Cj3fi-s191NUfeBfCXzFQ4RE227mF',\n u'nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/CTk6cGD641gB85Xe_Y3jXghanulwC',\n u'nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/CZwmUbEdN3OpT_GfTX9KzRBFaTDes',\n 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+ "result": "<div class=\"output_subarea output_text\"><pre>{'data': {},\n 'filename': '2018-01-17 132652 VASP.json',\n 'name': 'VASP',\n 'path': '/data/private/angeloziletti/nomad-query/data/2018-01-17 132652 VASP.json',\n 'query': {'context': 'single_configuration_calculation',\n 'endpoint': 'https://analytics-toolkit.nomad-coe.eu/api/',\n 'filter': 'system_reweighted_composition:Fe100 AND program_name:VASP',\n 'group_by': '',\n 'url': 'https://analytics-toolkit.nomad-coe.eu/api/query/single_configuration_calculation?filter=system_reweighted_composition%3AFe100%20AND%20program_name%3AVASP'},\n 'timestamp': 1516195612,\n 'uri': [u'nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/CC5fKTH2kdJRI-FZJwdNbfielxLZ9',\n u'nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/CFwfKX9KoRAm9nqtlQ2wgbIBQcm_d',\n u'nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/Cj3fi-s191NUfeBfCXzFQ4RE227mF',\n u'nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/CTk6cGD641gB85Xe_Y3jXghanulwC',\n u'nmd://N0ZOfcyLEp8fDX8aWdX8cCB2cBIIP/CZwmUbEdN3OpT_GfTX9KzRBFaTDes',\n 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- "2018-01-15 06:44:12"
+ "gaussian_all",
+ "/data/private/angeloziletti/nomad-query/data/2018-01-08 125655 gaussian_all.json",
+ "2018-01-08 12:56:55"
]
],
"hasIndex": "true",
@@ -2445,14 +2304,45 @@
},
"selectedType": "Table",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 253,
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 258,
"dataresult": 1,
"height": 785
},
"evaluatorReader": true,
"lineCount": 14,
- "initialization": true
+ "initialization": true,
+ "tags": "query_list"
+ },
+ {
+ "id": "coden1uLjO",
+ "type": "code",
+ "evaluator": "HTML",
+ "input": {
+ "body": [
+ "<script>",
+ "var reload_query_list = function() {",
+ " beaker.evaluate(\"query_list\");",
+ "};",
+ " ",
+ "</script>",
+ "<button class=\"btn btn-default\" onclick='reload_query_list()' >Reload Query List</button> "
+ ],
+ "hidden": true
+ },
+ "output": {
+ "state": {},
+ "result": {
+ "type": "BeakerDisplay",
+ "innertype": "Html",
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\nvar reload_query_list = function() {\n beaker.evaluate(\"query_list\");\n};\n \n</script>\n<button class=\"btn btn-default\" onclick=\"reload_query_list()\">Reload Query List</button> "
+ },
+ "selectedType": "BeakerDisplay",
+ "elapsedTime": 0,
+ "height": 72
+ },
+ "evaluatorReader": true,
+ "lineCount": 7
},
{
"id": "codeU7KLkT",
@@ -2671,7 +2561,7 @@
" <tr style=\"line-height:40px\">",
" <td><b>Max number or results for each space group/point group</b> </td>",
" <td>",
- " <input type=\"number\" name=\"quantity\" id=\"max_nb_res\" min=\"1\" max=\"100\" value=\"25\" step=\"1\"> ",
+ " <input type=\"number\" name=\"quantity\" id=\"max_nb_res\" min=\"5\" max=\"100\" value=\"25\" step=\"1\"> ",
" </td>",
" </tr> ",
" ",
@@ -2771,7 +2661,7 @@
" ",
"<hr style=\"border-width:2px\">",
" ",
- " <button class=\"btn btn-default\" onclick='run_soap()' >RUN Search & Analysis</button> ",
+ " <button class=\"btn btn-default\" onclick='run_soap()' >RUN Retrieval & Analysis</button> ",
" <button class=\"btn btn-default\" onclick='reset_soap()'>RESET </button> ",
" <label title=\"Activate once run is complete.\"> ",
" <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"plot_result_button\" >View 2D similarity map</a> ",
@@ -2785,7 +2675,7 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
- "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\nvar run_soap = function() {\n $(\"#plot_result_button\").removeClass(\"active\").addClass(\"disabled\");\n get_options();\n beaker.evaluate(\"cell_soap_run\");\n};\n \nvar show_hide = function(tag, bt_tag) {\n $(\"#\"+tag).toggle();\n $(\"#\"+bt_tag).toggleClass(\"active\");\n}\n\nvar allow = function(yesno) {\n if (yesno) {\n $(\"#option_el_spec_dens\").removeAttr('disabled');\n $(\"#atomic_density_type\").val('number_density').change();\n }\n else {\n if ($(\"#atomic_density_type\").val() == \"number_density\") {\n $(\"#atomic_density_type\").val('number_density_generic').change();\n }\n $(\"#option_el_spec_dens\").attr('disabled', 'disabled');\n }\n}\n \nvar reset_soap = function() {\n beaker.evaluate(\"cell_soap_gui\");\n};\n \nvar get_options = function() {\n var query_name = $(\"#query_name\").val(); \n var max_nb_res = $(\"#max_nb_res\").val(); \n \n // Determine kernel adaptor function\n // [i.e., choose from specific/generic/global-specific/global-generic] \n var density_type = $(\"#atomic_density_type\").val(); \n var adaptor_type = \"?\";\n if (density_type == \"number_density\") {\n adaptor_type = \"specific\";\n }\n else { // element-agnostic, core charge, valence charge, electronegativity\n adaptor_type = \"generic\";\n }\n var kernel_method = $(\"#kernel_method\").val();\n if (kernel_method == \"global\") {\n adaptor_type = \"global-\" + adaptor_type;\n }\n \n var n_procs = parseInt($(\"#exe_n_procs\").val());\n if ($(\"#threading_mode\").val() == \"single_threaded\") n_procs = 1;\n \n beaker.options = {\n \"query\": {\n \"query_name\": $(\"#query_name\").val(),\n \"max_nb_res\": $(\"#max_nb_res\").val()\n },\n \"run\": {\n \"config_folder\": $(\"#dataset_example\").val(),\n \"n_configs\": -1,\n \"n_procs\": n_procs,\n \"n_blocksize\": parseInt($(\"#exe_batch_size\").val()),\n \"queue\": \"?\",\n \"walltime\": 12,\n \"out_folder\": \"out.kernel\",\n \"verbose\": false\n },\n \"atomic_density\": {\n \"density_type\": $(\"#atomic_density_type\").val(),\n \"atomic_radius\": parseFloat($(\"#atomic_density_atomic_radius\").val()),\n \"use_covrad\": $(\"#atomic_density_use_covrad:checked\").val() == \"true\"\n },\n \"kernel\": {\n \"method\": kernel_method\n },\n \n \"basekernel\": {\n \"type\": \"dot\",\n \"dot\": {\n \"xi\": parseFloat($(\"#basekernel_kernel_xi\").val()),\n \"delta\": 1.0\n }\n },\n \n \"topkernel\": {\n \"type\": \"rematch\",\n \"rematch\": {\n \"gamma\": 0.01\n },\n \"rematch-atomic\": {\n \"gamma\": 0.01\n },\n \"average\": {\n \"xi\": 1.0,\n \"delta\": 1.0\n }\n },\n \n \"laplacian\": {\n \"inverse_dist\": true,\n \"coulomb\": true,\n \"scale\": 0.01,\n \"eta\": 1.0,\n \"gamma\": 0.01,\n \"optimize_eta_gamma\": false\n },\n \"dimred\": {\n \"method\": $(\"#dimred_method\").val()\n },\n \"graph\": {\n \"descriptor\": \"soap\",\n \"pca\": \"none\",\n \"hierarchical\": $(\"#kernel_hierarchical\").val() == \"true\",\n \"r0\": 1.0,\n \"alpha\": 2.0,\n \"n_levels\": 3,\n \"optimize_hierarchy\": false\n },\n \"lamatch\": {\n \"gamma\": 0.3,\n \"optimize_rematch\": false\n },\n\n \"descriptor\": {\n \"type\": \"soap-atomic-dmap\",\n \"soap-atomic\": {\n \"spectrum.global\": false,\n \"spectrum.gradients\": false,\n \"spectrum.2l1_norm\": false,\n \"_spectrum.2l1_norm\": true,\n \"radialbasis.type\" : \"gaussian\",\n \"radialbasis.mode\" : $(\"#soap_radialbasis_mode\").val(),\n \"radialbasis.N\" : parseInt($(\"#soap_radialbasis_N\").val()),\n \"radialbasis.sigma\": parseFloat($(\"#soap_radialbasis_sigma\").val()),\n \"radialbasis.integration_steps\": 15,\n \"radialcutoff.Rc\": parseFloat($(\"#soap_radialbasis_Rc\").val()),\n \"radialcutoff.Rc_width\": parseFloat($(\"#soap_radialbasis_sigma\").val()),\n \"radialcutoff.type\": \"heaviside\",\n \"radialcutoff.center_weight\": 1.0,\n \"angularbasis.type\": \"spherical-harmonic\",\n \"angularbasis.L\": parseInt($(\"#soap_angularbasis_L\").val()),\n \"kernel.adaptor\": \"specific-unique\",\n \"exclude_centers\": [],\n \"exclude_targets\": [],\n \"type_list\": []\n },\n \"soap-atomic-dmap\": {\n \"spectrum.global\": false,\n \"spectrum.gradients\": false,\n \"spectrum.2l1_norm\": false,\n \"radialbasis.type\" : \"gaussian\",\n \"radialbasis.mode\" : \"adaptive\",\n \"radialbasis.N\" : 9,\n \"radialbasis.sigma\": 0.5,\n \"radialbasis.integration_steps\": 15,\n \"radialcutoff.Rc\": 3.5,\n \"radialcutoff.Rc_width\": 0.5,\n \"radialcutoff.type\": \"heaviside\",\n \"radialcutoff.center_weight\": 1.0,\n \"angularbasis.type\": \"spherical-harmonic\",\n \"angularbasis.L\": 6,\n \"kernel.adaptor\": \"specific-unique-dmap\",\n \"exclude_centers\": [],\n \"exclude_targets\": [],\n \"type_list\": []\n }\n }\n\n \n };\n\n}; \n \nbeaker.view_result = function(result_link) {\n $(\"#plot_result_button\").attr(\"href\", result_link);\n $(\"#plot_result_button\").removeClass(\"disabled\"); //.addClass(\"active\");\n}\n</script>\n<style type=\"text/css\">\n .in11f {\n width: 3.5em;\n }\n .in3d {\n width: 5em;\n }\n .select_main {\n width: 20em;\n }\n .btn.active, .btn:active {\n background: #cecece;\n text-decoration: none;\n }\n</style>\n\n <p style=\"color:#20335d; font-weight:900; font-size:18pt;\"> Control options Query <br> </p><hr style=\"border-width:2px\"> <p></p>\n\n <table class=\"query_control\">\n \n <!-- SYSTEM REWEIHGTED COMPOSITION --> \n <tbody><tr style=\"line-height:40px\">\n <td><b>Name of query to analyze</b> (if empty: load last query)</td>\n <td>\n <input type=\"text\" id=\"query_name\" value=\"\"> \n\n </td>\n </tr>\n \n \n <!-- BASE KERNEL --> \n <tr style=\"line-height:40px\">\n <td><b>Max number or results for each space group/point group</b> </td>\n <td>\n <input type=\"number\" name=\"quantity\" id=\"max_nb_res\" min=\"1\" max=\"100\" value=\"25\" step=\"1\"> \n </td>\n </tr> \n \n \n \n</tbody></table>\n \n<br> <br>\n\n <p style=\"color:#20335d; font-weight:900; font-size:18pt;\"> Control options Analysis <br> </p><hr style=\"border-width:2px\"> <p></p>\n \n \n <table class=\"glosim_control\">\n \n <!-- ATOMIC DENSITY --> \n <tbody><tr style=\"line-height:40px\">\n <td><b>Atomic density</b> </td>\n <td>\n <select class=\"select_main\" id=\"atomic_density_type\">\n <option id=\"option_el_agno_dens\" value=\"number_density_generic\" selected=\"\"> Density type: element-agnostic density </option>\n <option id=\"option_el_spec_dens\" value=\"number_density\" disabled=\"\"> Density type: element-specific density </option> \n <option value=\"valence_charge_density\"> Density type: valence charge </option>\n <option value=\"z_density\"> Density type: core charge </option>\n <option value=\"elneg_density\"> Density type: electronegativity </option>\n </select> <!-- DATASETS -->\n \n <button id=\"toggle_detail_2\" type=\"button\" class=\"btn btn-xs\" onclick=\"show_hide("in_detail_2", "toggle_detail_2")\">Configure</button>\n <g id=\"in_detail_2\" hidden=\"\"> Use covalent radii <input id=\"atomic_density_use_covrad\" type=\"checkbox\" value=\"true\" unchecked=\"\">\n Constant atomic radius (Angstrom) <input id=\"atomic_density_atomic_radius\" type=\"number\" value=\"0.5\" min=\"0.0\" max=\"2.0\" step=\"0.1\" class=\"in11f\"> </g>\n </td>\n </tr>\n \n <!-- BASIS SET --> \n <tr style=\"line-height:40px\">\n <td><b>Basis set</b> </td>\n <td>\n <select class=\"select_main\" id=\"soap_radialbasis_mode\">\n <option value=\"adaptive\"> Radial basis: adaptive </option>\n <option value=\"equispaced\"> Radial basis: equispaced </option>\n </select>\n <button id=\"toggle_detail_3\" type=\"button\" class=\"btn btn-xs\" onclick=\"show_hide("in_detail_3", "toggle_detail_3")\">Configure</button>\n <g id=\"in_detail_3\" hidden=\"\">\n Radial functions N = <input id=\"soap_radialbasis_N\" type=\"number\" value=\"9\" min=\"1\" max=\"14\" step=\"1\" class=\"in11f\">\n Radial cutoff (equispaced only) <input id=\"soap_radialbasis_Rc\" type=\"number\" value=\"3.5\" min=\"0.1\" max=\"10.0\" step=\"0.1\" class=\"in11f\">\n Radial Gaussian width <input id=\"soap_radialbasis_sigma\" type=\"number\" value=\"0.5\" min=\"0.1\" max=\"3.0\" step=\"0.1\" class=\"in11f\">\n Angular functions L = <input id=\"soap_angularbasis_L\" type=\"number\" value=\"6\" min=\"1\" max=\"14\" step=\"1\" class=\"in11f\"> \n </g>\n </td>\n </tr> \n \n <!-- BASE KERNEL --> \n <tr style=\"line-height:40px\">\n <td><b>Base kernel</b> </td>\n <td>\n <select class=\"select_main\" id=\"basekernel_method\">\n <option value=\"dot\"> Dot-product kernel </option>\n <option value=\"laplacian\" disabled=\"\"> Laplacian kernel </option>\n <option value=\"gaussian\" disabled=\"\"> Gaussian kernel </option>\n </select>\n <button id=\"toggle_detail_4\" type=\"button\" class=\"btn btn-xs\" onclick=\"show_hide("in_detail_4", "toggle_detail_4")\">Configure</button>\n <g id=\"in_detail_4\" hidden=\"\">\n Exponent <input id=\"basekernel_kernel_xi\" type=\"number\" value=\"3.0\" min=\"0.1\" max=\"5.0\" step=\"0.1\" class=\"in11f\">\n </g>\n </td>\n </tr> \n \n <!-- TOP KERNEL -->\n <tr style=\"line-height:40px\">\n <td><b>Top kernel</b> </td>\n <td>\n <select class=\"select_main\" id=\"kernel_method\">\n <option value=\"global\" selected=\"\"> Global average (coherent)</option>\n <option value=\"average\"> Global average (incoherent)</option>\n <option value=\"rematch\"> Regularized-entropy match</option> \n <option value=\"laplacian\"> Laplacian Markov random field</option>\n </select>\n <button id=\"toggle_detail_5\" type=\"button\" class=\"btn btn-xs\" onclick=\"show_hide("in_detail_5", "toggle_detail_5")\">Configure</button>\n <g id=\"in_detail_5\" hidden=\"\">\n Mode \n <select id=\"kernel_hierarchical\">\n <option value=\"false\"> single-level</option>\n <option value=\"true\"> hierarchical</option>\n </select>\n Graph Laplacian \n <select id=\"opt_top_kernel_hierarchy\">\n <option value=\"invdist\"> inverse distance</option>\n <option value=\"coulomb\"> Coulomb matrix</option>\n </select>\n Levels <input id=\"graph:n_levels\" type=\"number\" value=\"1\" min=\"1\" max=\"5\" step=\"1\" class=\"in11f\">\n Base radius <input id=\"graph:r0\" type=\"number\" value=\"1.0\" min=\"0.1\" max=\"5.0\" step=\"0.1\" class=\"in11f\">\n Scale <input id=\"graph:alpha\" type=\"number\" value=\"2.0\" min=\"0.1\" max=\"5.0\" step=\"0.1\" class=\"in11f\">\n </g>\n </td>\n </tr> \n \n</tbody></table>\n \n<hr style=\"border-width:2px\">\n \n <button class=\"btn btn-default\" onclick=\"run_soap()\">RUN Search & Analysis</button> \n <button class=\"btn btn-default\" onclick=\"reset_soap()\">RESET </button> \n <label title=\"Activate once run is complete.\"> \n <a href=\"/user/tmp/1baed593093907b8.html\" target=\"_blank\" class=\"btn btn-primary\" id=\"plot_result_button\">View 2D similarity map</a> \n </label>\n"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\nvar run_soap = function() {\n $(\"#plot_result_button\").removeClass(\"active\").addClass(\"disabled\");\n get_options();\n beaker.evaluate(\"cell_soap_run\");\n};\n \nvar show_hide = function(tag, bt_tag) {\n $(\"#\"+tag).toggle();\n $(\"#\"+bt_tag).toggleClass(\"active\");\n}\n\nvar allow = function(yesno) {\n if (yesno) {\n $(\"#option_el_spec_dens\").removeAttr('disabled');\n $(\"#atomic_density_type\").val('number_density').change();\n }\n else {\n if ($(\"#atomic_density_type\").val() == \"number_density\") {\n $(\"#atomic_density_type\").val('number_density_generic').change();\n }\n $(\"#option_el_spec_dens\").attr('disabled', 'disabled');\n }\n}\n \nvar reset_soap = function() {\n beaker.evaluate(\"cell_soap_gui\");\n};\n \nvar get_options = function() {\n var query_name = $(\"#query_name\").val(); \n var max_nb_res = $(\"#max_nb_res\").val(); \n \n // Determine kernel adaptor function\n // [i.e., choose from specific/generic/global-specific/global-generic] \n var density_type = $(\"#atomic_density_type\").val(); \n var adaptor_type = \"?\";\n if (density_type == \"number_density\") {\n adaptor_type = \"specific\";\n }\n else { // element-agnostic, core charge, valence charge, electronegativity\n adaptor_type = \"generic\";\n }\n var kernel_method = $(\"#kernel_method\").val();\n if (kernel_method == \"global\") {\n adaptor_type = \"global-\" + adaptor_type;\n }\n \n var n_procs = parseInt($(\"#exe_n_procs\").val());\n if ($(\"#threading_mode\").val() == \"single_threaded\") n_procs = 1;\n \n beaker.options = {\n \"query\": {\n \"query_name\": $(\"#query_name\").val(),\n \"max_nb_res\": $(\"#max_nb_res\").val()\n },\n \"run\": {\n \"config_folder\": $(\"#dataset_example\").val(),\n \"n_configs\": -1,\n \"n_procs\": n_procs,\n \"n_blocksize\": parseInt($(\"#exe_batch_size\").val()),\n \"queue\": \"?\",\n \"walltime\": 12,\n \"out_folder\": \"out.kernel\",\n \"verbose\": false\n },\n \"atomic_density\": {\n \"density_type\": $(\"#atomic_density_type\").val(),\n \"atomic_radius\": parseFloat($(\"#atomic_density_atomic_radius\").val()),\n \"use_covrad\": $(\"#atomic_density_use_covrad:checked\").val() == \"true\"\n },\n \"kernel\": {\n \"method\": kernel_method\n },\n \n \"basekernel\": {\n \"type\": \"dot\",\n \"dot\": {\n \"xi\": parseFloat($(\"#basekernel_kernel_xi\").val()),\n \"delta\": 1.0\n }\n },\n \n \"topkernel\": {\n \"type\": \"rematch\",\n \"rematch\": {\n \"gamma\": 0.01\n },\n \"rematch-atomic\": {\n \"gamma\": 0.01\n },\n \"average\": {\n \"xi\": 1.0,\n \"delta\": 1.0\n }\n },\n \n \"laplacian\": {\n \"inverse_dist\": true,\n \"coulomb\": true,\n \"scale\": 0.01,\n \"eta\": 1.0,\n \"gamma\": 0.01,\n \"optimize_eta_gamma\": false\n },\n \"dimred\": {\n \"method\": $(\"#dimred_method\").val()\n },\n \"graph\": {\n \"descriptor\": \"soap\",\n \"pca\": \"none\",\n \"hierarchical\": $(\"#kernel_hierarchical\").val() == \"true\",\n \"r0\": 1.0,\n \"alpha\": 2.0,\n \"n_levels\": 3,\n \"optimize_hierarchy\": false\n },\n \"lamatch\": {\n \"gamma\": 0.3,\n \"optimize_rematch\": false\n },\n\n \"descriptor\": {\n \"type\": \"soap-atomic-dmap\",\n \"soap-atomic\": {\n \"spectrum.global\": false,\n \"spectrum.gradients\": false,\n \"spectrum.2l1_norm\": false,\n \"_spectrum.2l1_norm\": true,\n \"radialbasis.type\" : \"gaussian\",\n \"radialbasis.mode\" : $(\"#soap_radialbasis_mode\").val(),\n \"radialbasis.N\" : parseInt($(\"#soap_radialbasis_N\").val()),\n \"radialbasis.sigma\": parseFloat($(\"#soap_radialbasis_sigma\").val()),\n \"radialbasis.integration_steps\": 15,\n \"radialcutoff.Rc\": parseFloat($(\"#soap_radialbasis_Rc\").val()),\n \"radialcutoff.Rc_width\": parseFloat($(\"#soap_radialbasis_sigma\").val()),\n \"radialcutoff.type\": \"heaviside\",\n \"radialcutoff.center_weight\": 1.0,\n \"angularbasis.type\": \"spherical-harmonic\",\n \"angularbasis.L\": parseInt($(\"#soap_angularbasis_L\").val()),\n \"kernel.adaptor\": \"specific-unique\",\n \"exclude_centers\": [],\n \"exclude_targets\": [],\n \"type_list\": []\n },\n \"soap-atomic-dmap\": {\n \"spectrum.global\": false,\n \"spectrum.gradients\": false,\n \"spectrum.2l1_norm\": false,\n \"radialbasis.type\" : \"gaussian\",\n \"radialbasis.mode\" : \"adaptive\",\n \"radialbasis.N\" : 9,\n \"radialbasis.sigma\": 0.5,\n \"radialbasis.integration_steps\": 15,\n \"radialcutoff.Rc\": 3.5,\n \"radialcutoff.Rc_width\": 0.5,\n \"radialcutoff.type\": \"heaviside\",\n \"radialcutoff.center_weight\": 1.0,\n \"angularbasis.type\": \"spherical-harmonic\",\n \"angularbasis.L\": 6,\n \"kernel.adaptor\": \"specific-unique-dmap\",\n \"exclude_centers\": [],\n \"exclude_targets\": [],\n \"type_list\": []\n }\n }\n\n \n };\n\n}; \n \nbeaker.view_result = function(result_link) {\n $(\"#plot_result_button\").attr(\"href\", result_link);\n $(\"#plot_result_button\").removeClass(\"disabled\"); //.addClass(\"active\");\n}\n</script>\n<style type=\"text/css\">\n .in11f {\n width: 3.5em;\n }\n .in3d {\n width: 5em;\n }\n .select_main {\n width: 20em;\n }\n .btn.active, .btn:active {\n background: #cecece;\n text-decoration: none;\n }\n</style>\n\n <p style=\"color:#20335d; font-weight:900; font-size:18pt;\"> Control options Query <br> </p><hr style=\"border-width:2px\"> <p></p>\n\n <table class=\"query_control\">\n \n <!-- SYSTEM REWEIHGTED COMPOSITION --> \n <tbody><tr style=\"line-height:40px\">\n <td><b>Name of query to analyze</b> (if empty: load last query)</td>\n <td>\n <input type=\"text\" id=\"query_name\" value=\"\"> \n\n </td>\n </tr>\n \n \n <!-- BASE KERNEL --> \n <tr style=\"line-height:40px\">\n <td><b>Max number or results for each space group/point group</b> </td>\n <td>\n <input type=\"number\" name=\"quantity\" id=\"max_nb_res\" min=\"5\" max=\"100\" value=\"25\" step=\"1\"> \n </td>\n </tr> \n \n \n \n</tbody></table>\n \n<br> <br>\n\n <p style=\"color:#20335d; font-weight:900; font-size:18pt;\"> Control options Analysis <br> </p><hr style=\"border-width:2px\"> <p></p>\n \n \n <table class=\"glosim_control\">\n \n <!-- ATOMIC DENSITY --> \n <tbody><tr style=\"line-height:40px\">\n <td><b>Atomic density</b> </td>\n <td>\n <select class=\"select_main\" id=\"atomic_density_type\">\n <option id=\"option_el_agno_dens\" value=\"number_density_generic\" selected=\"\"> Density type: element-agnostic density </option>\n <option id=\"option_el_spec_dens\" value=\"number_density\" disabled=\"\"> Density type: element-specific density </option> \n <option value=\"valence_charge_density\"> Density type: valence charge </option>\n <option value=\"z_density\"> Density type: core charge </option>\n <option value=\"elneg_density\"> Density type: electronegativity </option>\n </select> <!-- DATASETS -->\n \n <button id=\"toggle_detail_2\" type=\"button\" class=\"btn btn-xs\" onclick=\"show_hide("in_detail_2", "toggle_detail_2")\">Configure</button>\n <g id=\"in_detail_2\" hidden=\"\"> Use covalent radii <input id=\"atomic_density_use_covrad\" type=\"checkbox\" value=\"true\" unchecked=\"\">\n Constant atomic radius (Angstrom) <input id=\"atomic_density_atomic_radius\" type=\"number\" value=\"0.5\" min=\"0.0\" max=\"2.0\" step=\"0.1\" class=\"in11f\"> </g>\n </td>\n </tr>\n \n <!-- BASIS SET --> \n <tr style=\"line-height:40px\">\n <td><b>Basis set</b> </td>\n <td>\n <select class=\"select_main\" id=\"soap_radialbasis_mode\">\n <option value=\"adaptive\"> Radial basis: adaptive </option>\n <option value=\"equispaced\"> Radial basis: equispaced </option>\n </select>\n <button id=\"toggle_detail_3\" type=\"button\" class=\"btn btn-xs\" onclick=\"show_hide("in_detail_3", "toggle_detail_3")\">Configure</button>\n <g id=\"in_detail_3\" hidden=\"\">\n Radial functions N = <input id=\"soap_radialbasis_N\" type=\"number\" value=\"9\" min=\"1\" max=\"14\" step=\"1\" class=\"in11f\">\n Radial cutoff (equispaced only) <input id=\"soap_radialbasis_Rc\" type=\"number\" value=\"3.5\" min=\"0.1\" max=\"10.0\" step=\"0.1\" class=\"in11f\">\n Radial Gaussian width <input id=\"soap_radialbasis_sigma\" type=\"number\" value=\"0.5\" min=\"0.1\" max=\"3.0\" step=\"0.1\" class=\"in11f\">\n Angular functions L = <input id=\"soap_angularbasis_L\" type=\"number\" value=\"6\" min=\"1\" max=\"14\" step=\"1\" class=\"in11f\"> \n </g>\n </td>\n </tr> \n \n <!-- BASE KERNEL --> \n <tr style=\"line-height:40px\">\n <td><b>Base kernel</b> </td>\n <td>\n <select class=\"select_main\" id=\"basekernel_method\">\n <option value=\"dot\"> Dot-product kernel </option>\n <option value=\"laplacian\" disabled=\"\"> Laplacian kernel </option>\n <option value=\"gaussian\" disabled=\"\"> Gaussian kernel </option>\n </select>\n <button id=\"toggle_detail_4\" type=\"button\" class=\"btn btn-xs\" onclick=\"show_hide("in_detail_4", "toggle_detail_4")\">Configure</button>\n <g id=\"in_detail_4\" hidden=\"\">\n Exponent <input id=\"basekernel_kernel_xi\" type=\"number\" value=\"3.0\" min=\"0.1\" max=\"5.0\" step=\"0.1\" class=\"in11f\">\n </g>\n </td>\n </tr> \n \n <!-- TOP KERNEL -->\n <tr style=\"line-height:40px\">\n <td><b>Top kernel</b> </td>\n <td>\n <select class=\"select_main\" id=\"kernel_method\">\n <option value=\"global\" selected=\"\"> Global average (coherent)</option>\n <option value=\"average\"> Global average (incoherent)</option>\n <option value=\"rematch\"> Regularized-entropy match</option> \n <option value=\"laplacian\"> Laplacian Markov random field</option>\n </select>\n <button id=\"toggle_detail_5\" type=\"button\" class=\"btn btn-xs\" onclick=\"show_hide("in_detail_5", "toggle_detail_5")\">Configure</button>\n <g id=\"in_detail_5\" hidden=\"\">\n Mode \n <select id=\"kernel_hierarchical\">\n <option value=\"false\"> single-level</option>\n <option value=\"true\"> hierarchical</option>\n </select>\n Graph Laplacian \n <select id=\"opt_top_kernel_hierarchy\">\n <option value=\"invdist\"> inverse distance</option>\n <option value=\"coulomb\"> Coulomb matrix</option>\n </select>\n Levels <input id=\"graph:n_levels\" type=\"number\" value=\"1\" min=\"1\" max=\"5\" step=\"1\" class=\"in11f\">\n Base radius <input id=\"graph:r0\" type=\"number\" value=\"1.0\" min=\"0.1\" max=\"5.0\" step=\"0.1\" class=\"in11f\">\n Scale <input id=\"graph:alpha\" type=\"number\" value=\"2.0\" min=\"0.1\" max=\"5.0\" step=\"0.1\" class=\"in11f\">\n </g>\n </td>\n </tr> \n \n</tbody></table>\n \n<hr style=\"border-width:2px\">\n \n <button class=\"btn btn-default\" onclick=\"run_soap()\">RUN Retrieval & Analysis</button> \n <button class=\"btn btn-default\" onclick=\"reset_soap()\">RESET </button> \n <label title=\"Activate once run is complete.\"> \n <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"plot_result_button\">View 2D similarity map</a> \n </label>\n"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
@@ -2836,26 +2726,30 @@
"open(configs[\"output_file\"], 'w').close()",
" ",
"tmp_folder = '/home/beaker/.beaker/v1/web/tmp/'",
- "",
"tmp_folder_json = os.path.abspath(os.path.normpath(os.path.join(tmp_folder, 'jsons_for_nomad_sim'))) ",
"control_file = os.path.abspath(os.path.normpath(os.path.join(tmp_folder, 'control.json'))) ",
"lookup_file = os.path.abspath(os.path.normpath(os.path.join(tmp_folder, 'lookup.dat'))) ",
"h5_filename = os.path.abspath(os.path.normpath(os.path.join(tmp_folder, 'configs.hdf5'))) ",
"",
+ "if not os.path.exists(tmp_folder_json):",
+ " os.makedirs(tmp_folder_json)",
+ " ",
"# visualize structrues with 3x3 supercells in the Viewer",
"operations_on_structure = [(create_supercell, {'replicas': [3, 3, 3]})]",
"",
"# HANDLE SPECIAL CASES",
"if options['graph']['hierarchical'] and kernel_method == 'laplacian':",
- " logger.warn(\"Hierarchical laplacian kernel requested, evaluation may take several minutes.\")"
- ]
+ " logger.warn(\"Hierarchical laplacian kernel requested, evaluation may take several minutes.\")",
+ " "
+ ],
+ "hidden": true
},
"output": {
"state": {},
"selectedType": "Results",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 232,
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 229,
"dataresult": {
"name": "Fe_query",
"timestamp": 1516012342,
@@ -12873,35 +12767,10 @@
"path": "data/2018-01-15 103222 Fe_query.json",
"data": {}
},
- "result": {
- "type": "Results",
- "outputdata": [
- {
- "type": "err",
- "value": "INFO: Started @ 2018-01-17 10:48\n"
- },
- {
- "type": "err",
- "value": "INFO: Kernel method: global\n"
- },
- {
- "type": "err",
- "value": "INFO: Kernel adaptor: specific-unique\n"
- },
- {
- "type": "err",
- "value": "INFO: Density type: number_density_generic\n"
- },
- {
- "type": "err",
- "value": "INFO: Dim.-red. method: kernelpca\n"
- }
- ]
- },
"height": 153
},
"evaluatorReader": true,
- "lineCount": 46,
+ "lineCount": 49,
"initialization": true,
"tags": "cell_soap_run"
},
@@ -12937,8 +12806,8 @@
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 401,
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 345,
"height": 88
},
"evaluatorReader": true,
@@ -12977,23 +12846,23 @@
" \"section_system-0\": ",
" \"\"\"",
" ) ",
- " ",
- " try:",
- " cell = (atoms.get_cell()*1.0E-10).tolist()",
- " except:",
- " cell = None",
" ",
" res={",
" \"name\": \"section_system\",",
" \"gIndex\" : 0,",
" \"label\": atoms.info[label_name],",
" \"chemical_formula\": atoms.get_chemical_formula(mode='hill'),",
- " \"simulation_cell\": cell,",
" \"atom_labels\": atoms.get_chemical_symbols(),",
" # nomad json files are in SU ",
" \"atom_positions\": map(lambda x: [x.x*1.0E-10,x.y*1.0E-10,x.z*1.0E-10], atoms)",
" }",
- " ",
+ "",
+ " if atoms.pbc.all():",
+ " # periodic structure",
+ " res['simulation_cell'] = (atoms.get_cell()*1.0E-10).tolist()",
+ " else:",
+ " pass",
+ " ",
" json.dump(res, outF, indent=2)",
" outF.write(\"\"\"",
" }}}}",
@@ -13010,8 +12879,8 @@
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 361,
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 332,
"height": 88
},
"evaluatorReader": true,
@@ -13105,7 +12974,7 @@
"#print tmp_json_folder",
"json_list = write_json_for_nomad_sim(output_folder=tmp_json_folder, ase_atom_list=ase_atoms_list, label_name='nmd_checksum')",
"",
- "threshold_nb_analysis = 2",
+ "threshold_nb_analysis = 3",
"if len(ase_atoms_list) < threshold_nb_analysis:",
" logger.warning(\"Your query+analysis yielded to less than {} structures. Please make another search.\".format(threshold_nb_analysis))",
" sys.exit()"
@@ -13113,19 +12982,10 @@
},
"output": {
"state": {},
- "result": {
- "type": "Results",
- "outputdata": [
- {
- "type": "err",
- "value": "INFO: Retrieving query data from the NOMAD Archive...\n"
- }
- ]
- },
"selectedType": "Results",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 7854,
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 9461,
"height": 61
},
"evaluatorReader": true,
@@ -13214,8 +13074,8 @@
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 326,
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 333,
"height": 88
},
"evaluatorReader": true,
@@ -13245,39 +13105,24 @@
"spgroups_ascii = [item.encode(\"ascii\") for item in targets_dict.keys()]",
"logger.debug(\"Spacegroups/pointgroups found: {0}\".format(targets_dict))",
"",
- "# case of NaCl",
- "#99 -> strange absorbed structure (optional, but it shows that we can also deal/identify crazy/wrong structures)",
- "#191 -> hexagonal structure",
- "#194 -> NiAs structure",
- "#216 -> Zincblende",
- "#225 -> Rocksalt",
- "",
"# sort spacegroups according to number of structures, and get top MAX_CATEGORIES",
"sorted_targets = sorted([(k, len(v)) for k, v in targets_dict.iteritems()], key=lambda x: x[1], reverse=True)",
"top_k_targets = [item[0] for item in sorted_targets][:MAX_CATEGORIES]",
"top_k_targets = [item.encode(\"ascii\") for item in top_k_targets]",
"logger.info(\"Top-{0} space groups (crystals) or point groups (molecules): {1}\".format(MAX_CATEGORIES, top_k_targets))"
- ]
+ ],
+ "hidden": true
},
"output": {
"state": {},
"selectedType": "Results",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 245,
- "height": 61,
- "result": {
- "type": "Results",
- "outputdata": [
- {
- "type": "err",
- "value": "INFO: Top-5 space groups (crystals) or point groups (molecules): ['229', '225', '194', '139', '227']\n"
- }
- ]
- }
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 254,
+ "height": 61
},
"evaluatorReader": true,
- "lineCount": 27,
+ "lineCount": 20,
"tags": "cell_soap_run",
"isError": false
},
@@ -13319,14 +13164,15 @@
" f.write(json.dumps(options, indent=4, sort_keys=True))",
" f.write(\"\\n\")",
""
- ]
+ ],
+ "hidden": true
},
"output": {
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 321,
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 327,
"height": 88
},
"evaluatorReader": true,
@@ -13335,2099 +13181,74 @@
"isError": false
},
{
- "id": "codea3mj4L",
+ "id": "codenaInta",
"type": "code",
"evaluator": "IPython",
"input": {
"body": [
- "kmat, dmat = calc_soap_similarity(ase_atoms_list_cut, tmp_folder, h5_filename=h5_filename, options=options)"
+ "kmat, dmat = calc_soap_similarity(ase_atoms_list_cut, tmp_folder, h5_filename=h5_filename, options=options)",
+ "",
+ "# DECOMPOSE KERNEL MATRIX",
+ "dimred_positions = soap.soapy.dimred_matrix(dimred_method, kmat=kmat, distmat=dmat)",
+ "#print dimred_positions[:,0:4]",
+ "x_list = list(dimred_positions[:,0])",
+ "y_list = list(dimred_positions[:,1])",
+ "",
+ "# write a lookup file for the viewer",
+ "lookup_out = []",
+ "for i in range(len(ase_atoms_list_cut)):",
+ " lookup_out.append((",
+ " json_list_cut[i],",
+ " '0',",
+ " # x_coord",
+ " str(x_list[i]),",
+ " # y_coord",
+ " str(y_list[i]),",
+ " # target value",
+ " str(target_list_cut[i]),",
+ " # chemical_formula",
+ " 'NaN',",
+ " 'NaN',",
+ " # predicted value (2D descriptor)",
+ " 'NaN'",
+ " ))",
+ "",
+ "# write log file with general info about the model",
+ "with open(lookup_file, \"w\") as f:",
+ " f.write(\"\\n\".join([\" \".join(x) for x in lookup_out]))",
+ "",
+ "with open(control_file, \"w\") as f:",
+ " f.write(\"\"\"",
+ " {",
+ " \"model_info\":[\"\"\")",
+ " writeColon = False",
+ "",
+ " model_info = {",
+ " \"x_axis_label\": 'x (kernel pca)',",
+ " \"y_axis_label\": 'y (kernel pca)',",
+ " }",
+ " if (writeColon):",
+ " f.write(\", \")",
+ " ",
+ " writeColon = True",
+ " json.dump(model_info, f, indent=2)",
+ " ",
+ " f.write(\"\"\"",
+ " ] }\"\"\")",
+ " f.flush()"
]
},
"output": {
"state": {},
- "result": {
- "type": "Results",
- "outputdata": [
- {
- "type": "out",
- "value": "[Kernel] 64 106 \n"
- },
- {
- "type": "out",
- "value": "[Kernel] 64 118 \n"
- },
- {
- "type": "out",
- "value": "[Kernel] 64 120 \n"
- },
- {
- "type": "out",
- "value": "[Kernel] 64 121 \n"
- },
- {
- "type": "out",
- "value": "[Kernel] 70 70 \n"
- },
- {
- "type": "out",
- "value": "[Kernel] 70 79 \n"
- },
- {
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- },
- {
- "type": "out",
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- },
- {
- "type": "out",
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- {
- "type": "out",
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- },
- {
- "type": "out",
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- },
- {
- "type": "out",
- "value": "[Kernel] 70 4 \n"
- },
- {
- "type": "out",
- "value": "[Kernel] 70 10 \n"
- },
- {
- "type": "out",
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- },
- {
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- },
- {
- "type": "out",
- "value": "[Kernel] 70 20 \n"
- },
- {
- "type": "out",
- "value": "[Kernel] 70 25 \n"
- },
- {
- "type": "out",
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- },
- {
- "type": "out",
- "value": "[Kernel] 70 30 \n"
- },
- {
- "type": "out",
- "value": "[Kernel] 70 33 \n"
- },
- {
- "type": "out",
- "value": "[Kernel] 70 36 \n"
- },
- {
- "type": "out",
- "value": "[Kernel] 70 44 \n"
- },
- {
- "type": "out",
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- },
- {
- "type": "out",
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- {
- "type": "out",
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- {
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- {
- "type": "out",
- "value": "[Kernel] 70 80 \n"
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- {
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- {
- "type": "out",
- "value": "[Kernel] 70 83 \n"
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- {
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- {
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- },
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- },
"selectedType": "Results",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 13339,
- "hidden": true,
- "height": 50
- },
- "evaluatorReader": true,
- "lineCount": 1,
- "tags": "cell_soap_run"
- },
- {
- "id": "codenaInta",
- "type": "code",
- "evaluator": "IPython",
- "input": {
- "body": [
- "# DECOMPOSE KERNEL MATRIX",
- "dimred_positions = soap.soapy.dimred_matrix(dimred_method, kmat=kmat, distmat=dmat)",
- "#print dimred_positions[:,0:4]",
- "x_list = list(dimred_positions[:,0])",
- "y_list = list(dimred_positions[:,1])",
- "",
- "# write a lookup file for the viewer",
- "lookup_out = []",
- "for i in range(len(ase_atoms_list_cut)):",
- " lookup_out.append((",
- " json_list_cut[i],",
- " '0',",
- " # x_coord",
- " str(x_list[i]),",
- " # y_coord",
- " str(y_list[i]),",
- " # target value",
- " str(target_list_cut[i]),",
- " # chemical_formula",
- " 'NaN',",
- " 'NaN',",
- " # predicted value (2D descriptor)",
- " 'NaN'",
- " ))",
- "",
- "# write log file with general info about the model",
- "with open(lookup_file, \"w\") as f:",
- " f.write(\"\\n\".join([\" \".join(x) for x in lookup_out]))",
- "",
- "with open(control_file, \"w\") as f:",
- " f.write(\"\"\"",
- " {",
- " \"model_info\":[\"\"\")",
- " writeColon = False",
- "",
- " model_info = {",
- " \"x_axis_label\": 'x (kernel pca)',",
- " \"y_axis_label\": 'y (kernel pca)',",
- " }",
- " if (writeColon):",
- " f.write(\", \")",
- " ",
- " writeColon = True",
- " json.dump(model_info, f, indent=2)",
- " ",
- " f.write(\"\"\"",
- " ] }\"\"\")",
- " f.flush()"
- ],
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 13570,
+ "height": 11618,
"hidden": true
},
- "output": {
- "state": {},
- "selectedType": "Hidden",
- "pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 783,
- "height": 88
- },
"evaluatorReader": true,
- "lineCount": 48,
+ "lineCount": 50,
"tags": "cell_soap_run",
"isError": true
},
@@ -15437,7 +13258,7 @@
"evaluator": "IPython",
"input": {
"body": [
- "operations_on_structure = [(create_supercell, {'replicas': [3, 3, 3]})]",
+ "operations_on_structure = [(create_supercell, {'replicas': [1, 1, 1]})]",
"",
"json_list, frame_list, x_list, y_list, target_list, target_pred_list = get_json_list(method='file', data_folder='',",
" path_to_file=lookup_file, drop_duplicates=False, displace_duplicates=True, predicted_value=True)",
@@ -15507,78 +13328,9 @@
"state": {},
"selectedType": "Results",
"pluginName": "IPython",
- "shellId": "99408C2784FB4F08A3E241D8DA6E06D9",
- "elapsedTime": 4525,
- "height": 429,
- "result": {
- "type": "Results",
- "outputdata": [
- {
- "type": "err",
- "value": "INFO: Same points in the plot have exactly the same X and Y coordinates.\n"
- },
- {
- "type": "err",
- "value": "INFO: The degenerate points in the plot are randomly displaced \n (by a very small amount) to allow actions on click.\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometry files.\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometries: file 1/86\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometries: file 10/86\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometries: file 19/86\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometries: file 28/86\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometries: file 37/86\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometries: file 46/86\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometries: file 55/86\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometries: file 64/86\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometries: file 73/86\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometries: file 82/86\n"
- },
- {
- "type": "err",
- "value": "INFO: Generating figures and geometry files: done.\n"
- },
- {
- "type": "err",
- "value": "INFO: Click on the button 'View interactive 2D scatter plot' to see the plot.\n"
- },
- {
- "type": "err",
- "value": "INFO: Finished @ 2018-01-17 10:48\n"
- }
- ]
- }
+ "shellId": "0A64306A72684EBB8E96FD0D3C1057DB",
+ "elapsedTime": 2497,
+ "height": 56
},
"evaluatorReader": true,
"lineCount": 64,
@@ -15600,8 +13352,8 @@
"state": {},
"selectedType": "BeakerDisplay",
"pluginName": "JavaScript",
- "elapsedTime": 19,
- "height": 88
+ "elapsedTime": 47,
+ "height": 56
},
"evaluatorReader": true,
"lineCount": 2,
@@ -15641,7 +13393,7 @@
"namespace": {
"options": {
"query": {
- "query_name": "",
+ "query_name": "Au",
"max_nb_res": "25"
},
"run": {
@@ -15751,7 +13503,7 @@
}
}
},
- "viewer_result": "1baed593093907b8",
+ "viewer_result": "d6c25bb00686ca68",
"last_query_name": "Crystal-C"
},
"locked": true