Embedding.bkr 79.9 KB
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{
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    "evaluators": [
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                    "<label style=\"text-align: left; color: #20335d; font-weight: 900; font-size: 18pt; padding-top: 2em;\">",
                    "  Visualizing material-similarity:</label><br/><label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> Octet-binary zincblende vs. rocksalt semiconductors</label>",
                    " </p>",
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                    " <p style=\"font-size: 15px;\">Angelo Ziletti, Ankit Kariryaa, Emre Ahmetcik, Fawzi Mohamed, Luca Ghiringhelli, and Matthias Scheffler <span style=\"font-size: smaller;\">[version 2017-01-30]</span></p>",
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                    " ",
                    "<div style=\"padding-top: 1em;\">"
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                    "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<label style=\"text-align: left; color: #20335d; font-weight: 900; font-size: 18pt; padding-top: 2em;\">\n  Visualizing material-similarity:</label><br><label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> Octet-binary zincblende vs. rocksalt semiconductors</label>\n <p></p>\n <p style=\"font-size: 15px;\">Angelo Ziletti, Ankit Kariryaa, Emre Ahmetcik, Fawzi Mohamed, Luca Ghiringhelli, and Matthias Scheffler <span style=\"font-size: smaller;\">[version 2017-01-30]</span></p>\n \n<div style=\"padding-top: 1em;\"></div>"
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                    "</style>",
                    "<!-- Button trigger modal -->",
                    "<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-motivation-modal\">",
                    " Introduction and motivation",
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                    "",
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                    "      <div class=\"modal-header\">",
                    "        <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>",
                    "        <h4 class=\"modal-title\" id=\"lasso-motivation-modal-label\">Introduction and motivation</h4>",
                    "      </div>",
                    "      <div class=\"modal-body lasso_instructions\">",
                    "        <p> In this tutorial, we present a tool that produces two-dimensional structure maps for octet binary compounds, by starting from a high-dimensional set of <i>features</i> (coordinates) that identify each data point (material), based on free-atom data of the atomic species constituting the binary material. </p>",
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                    "          ",
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                    "        <p> The low-dimensional embedding methods (here, two-dimensional for the sake of visualization) are <i>unsupervised</i> machine-learning algorithms; so, in our example, the algorithm processes only the spatial arrangement of the points in the high-dimensional representation that is determined by the user. </p>",
                    "        ",
                    "        <p> In the linear method, <b>principal component analysis (<a href=\"https://en.wikipedia.org/wiki/Feature_scaling\" target=\"_blank\">PCA</a>)</b>, the direction (linear combination of the input coordinates) with the maximum variance is identified as the first principal component (PC). The direction perpendicular to the first PC with the largest variance is the second PC.",
                    "          The process can be iterated up to as many dimensions as the initial dimensionality of the data, but here we stop at the second dimension and give the amount of total variance recovered by the first two principal components. </p>",
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                    "      <p> In the two popular non-linear method we chose, <b>multidimensional scaling (<a href=\"https://en.wikipedia.org/wiki/Multidimensional_scaling\" target=\"_blank\">MDS</a>) </b> tries to preserve the distances from the given high-dimensional to the two-dimensional representation, ",
                    "        and the <b>t-Distributed Stochastic Neighbor Embedding (<a href=\"https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding\" target=\"_blank\">t-SNE</a>) </b> tries to preserve the local shape of groups of neighboring points. Both methods use a notion of distance that in our example is the Euclidean norm, even if in principle it could be any proper norm. </p>",
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                    "",
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                    "        <p> In the results, we show the data points colored according to the difference in energy between the Rocksalt (RS) and Zincblende (ZB) crystal structures (both relaxed to their local minima) of the material they represent. The labeling and consequent coloring are independent of the embedding method used, therefore the labeling is an <i>a posteriori</i>",
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                    "          check that the high-dimensional representation could contain information about the labeling itself. In practice, if the coloring identifies clearly distinct areas, then the two dimensional representation is a map for the prediction of the labels, so that a new data point of unknown labeling, that lands in the 2D map in a area of points with known labeling, is expected to belong to that same labeling. </p>",
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                    "        ",
                    "<p>The merit of the embedding methods is to provide relatively inexpensive tools to visually test whether a given set of features contains information about an investigated property (label). For this reason, they are widely used as preliminary tools for discovering structures in the data. </p>",
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                    "<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. </p>",
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                    "",
                    "        <p>References:</p>",
                    "        <ol>",
                    "          <li>J. A. van Vechten, Phys. Rev. 182, 891 (1969).</li>",
                    "          <li>J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970).</li>",
                    "          <li>J. St. John and A.N. Bloch, Phys. Rev. Lett. 33, 1095 (1974).</li>",
                    "          <li>J. R. Chelikowsky and J. C. Phillips, Phys. Rev. B 17, 2453 (1978).</li>",
                    "          <li>A. Zunger, Phys. Rev. B 22, 5839 (1980).</li>",
                    "          <li>D. G. Pettifor, Solid State Commun. 51, 31 (1984).</li>",
                    "          <li>Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).</li>",
                    "        </ol>",
                    "      </div>",
                    "      <div class=\"modal-footer\">",
                    "        <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>",
                    "<!--         <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->",
                    "      </div>",
                    "    </div>",
                    "  </div>",
                    "</div>"
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                    "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\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 Introduction and motivation\n</button>\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\">Introduction and motivation</h4>\n      </div>\n      <div class=\"modal-body lasso_instructions\">\n        <p> In this tutorial, we present a tool that produces two-dimensional structure maps for octet binary compounds, by starting from a high-dimensional set of <i>features</i> (coordinates) that identify each data point (material), based on free-atom data of the atomic species constituting the binary material. </p>\n          \n        <p> The low-dimensional embedding methods (here, two-dimensional for the sake of visualization) are <i>unsupervised</i> machine-learning algorithms; so, in our example, the algorithm processes only the spatial arrangement of the points in the high-dimensional representation that is determined by the user. </p>\n        \n        <p> In the linear method, <b>principal component analysis (<a href=\"https://en.wikipedia.org/wiki/Feature_scaling\" target=\"_blank\">PCA</a>)</b>, the direction (linear combination of the input coordinates) with the maximum variance is identified as the first principal component (PC). The direction perpendicular to the first PC with the largest variance is the second PC.\n          The process can be iterated up to as many dimensions as the initial dimensionality of the data, but here we stop at the second dimension and give the amount of total variance recovered by the first two principal components. </p>\n      <p> In the two popular non-linear method we chose, <b>multidimensional scaling (<a href=\"https://en.wikipedia.org/wiki/Multidimensional_scaling\" target=\"_blank\">MDS</a>) </b> tries to preserve the distances from the given high-dimensional to the two-dimensional representation, \n        and the <b>t-Distributed Stochastic Neighbor Embedding (<a href=\"https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding\" target=\"_blank\">t-SNE</a>) </b> tries to preserve the local shape of groups of neighboring points. Both methods use a notion of distance that in our example is the Euclidean norm, even if in principle it could be any proper norm. </p>\n\n        <p> In the results, we show the data points colored according to the difference in energy between the Rocksalt (RS) and Zincblende (ZB) crystal structures (both relaxed to their local minima) of the material they represent. The labeling and consequent coloring are independent of the embedding method used, therefore the labeling is an <i>a posteriori</i>\n          check that the high-dimensional representation could contain information about the labeling itself. In practice, if the coloring identifies clearly distinct areas, then the two dimensional representation is a map for the prediction of the labels, so that a new data point of unknown labeling, that lands in the 2D map in a area of points with known labeling, is expected to belong to that same labeling. </p>\n        \n<p>The merit of the embedding methods is to provide relatively inexpensive tools to visually test whether a given set of features contains information about an investigated property (label). For this reason, they are widely used as preliminary tools for discovering structures in the data. </p>\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. </p>\n\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>"
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                },
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        },
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            "id": "markdowntnAHej",
            "type": "markdown",
            "body": [
                "<p style=\"font-size: 15px;\"> <b> Machine learning methods: </b> <br>",
                "Multi- to 2-dimensional embedding, i.e. Principal Component Analysis (PCA), and a selection of non-linear embedding methods."
            ],
            "evaluatorReader": false
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            "id": "codeZ2DSp3",
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            "evaluator": "HTML",
            "input": {
                "body": [
                    "<style type=\"text/css\">",
                    " .lasso_instructions{",
                    "    font-size: 15px;",
                    "  } ",
                    "</style>",
                    "<!-- Button trigger modal -->",
                    "<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#lasso-instructions-modal\">",
                    " Instructions",
                    "</button>",
                    "",
                    "<!-- Modal -->",
                    "<div class=\"modal fade\" id=\"lasso-instructions-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"lasso-instructions-modal-label\">",
                    "  <div class=\"modal-dialog\" role=\"document\">",
                    "    <div class=\"modal-content\">",
                    "      <div class=\"modal-header\">",
                    "        <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>",
                    "        <h4 class=\"modal-title\" id=\"lasso-instructions-modal-label\">Instructions</h4>",
                    "      </div>",
                    "      <div class=\"modal-body lasso_instructions\">",
                    "<p> In this example, you can run the linear low-dimensional embedding method, <b>principal component analysis (<a href=\"https://en.wikipedia.org/wiki/Feature_scaling\" target=\"_blank\">PCA</a>)</b> and two selected non-linear methods, <b>multidimensional scaling (<a href=\"https://en.wikipedia.org/wiki/Multidimensional_scaling\" target=\"_blank\">MDS</a>) </b>",
                    "        and <b>t-Distributed Stochastic Neighbor Embedding (<a href=\"https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding\" target=\"_blank\">t-SNE</a>) </b>. </p>",
                    "      ",
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                    "<p> The input features, that can be selected in the checklist below (any number of features larger than 2 is allowed), represent chemical elements constituting binary octet materials, that crystallize typically into rocksalt or zincblende crystal structure. </p>",
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                    "<p> The next step is to select the embedding method (exclusive selection) and whether each feature is pre-processed by dividing it by the standard deviation of the whole population (all data points). Note that the feature are anyhow centered around their mean value as pre-processing.</p>      ",
                    "        ",
                    "<p> After selecting the list of features, the method, and the normalization criterion, click <b>“Run two-dimensional embedding”</b> to apply the selected method. </p>",
                    "  ",
                    "<p> During and at the end of the run, a brief summary is printed out below the <b>“Run two-dimensional embedding”</b> button. After the end of the run, click on <b>“View interactive 2D scatter plot”</b> (it is unlocked  at the end of the run) to open a new tab where the two-dimensional map is shown as an interactive scatter plot. </p>",
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                    "<p> Note1: 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>",
                    "        <!--- <p> Note2: with the following selection of features:<br> ",
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                    "        ['rs(A)', 'rs(B)', 'rp(A)', 'rp(B)', 'Es(A)/sqrt(Zval(A))', 'Es(B)/sqrt(Zval(B))', 'Ep(A)/sqrt(Zval(A))', 'Ep(B)/sqrt(Zval(B))']<br>",
                    "        and PCA method, one obtains a result similar to Fig. 4 in Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).",
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                    "          The plot may appear mirrored because the sign of the principal component is immaterial. Besides, the input data are slightly different (here, everything is calculated at the converged LDA level).",
                    "        </p> -->",
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                    "      </div>",
                    "      <div class=\"modal-footer\">",
                    "        <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>",
                    "<!--         <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->",
                    "      </div>",
                    "    </div>",
                    "  </div>",
                    "</div>"
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                    "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\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-instructions-modal\">\n Instructions\n</button>\n\n<!-- Modal -->\n<div style=\"display: none;\" 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 the linear low-dimensional embedding method, <b>principal component analysis (<a href=\"https://en.wikipedia.org/wiki/Feature_scaling\" target=\"_blank\">PCA</a>)</b> and two selected non-linear methods, <b>multidimensional scaling (<a href=\"https://en.wikipedia.org/wiki/Multidimensional_scaling\" target=\"_blank\">MDS</a>) </b>\n        and <b>t-Distributed Stochastic Neighbor Embedding (<a href=\"https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding\" target=\"_blank\">t-SNE</a>) </b>. </p>\n      \n<p> The input features, that can be selected in the checklist below (any number of features larger than 2 is allowed), represent chemical elements constituting binary octet materials, that crystallize typically into rocksalt or zincblende crystal structure. </p>\n<p> The next step is to select the embedding method (exclusive selection) and whether each feature is pre-processed by dividing it by the standard deviation of the whole population (all data points). Note that the feature are anyhow centered around their mean value as pre-processing.</p>      \n        \n<p> After selecting the list of features, the method, and the normalization criterion, click <b>“Run two-dimensional embedding”</b> to apply the selected method. </p>\n  \n<p> During and at the end of the run, a brief summary is printed out below the <b>“Run two-dimensional embedding”</b> button. After the end of the run, click on <b>“View interactive 2D scatter plot”</b> (it is unlocked  at the end of the run) to open a new tab where the two-dimensional map is shown as an interactive scatter plot. </p>\n<p> Note1: 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        <!--- <p> Note2: with the following selection of features:<br> \n        ['rs(A)', 'rs(B)', 'rp(A)', 'rp(B)', 'Es(A)/sqrt(Zval(A))', 'Es(B)/sqrt(Zval(B))', 'Ep(A)/sqrt(Zval(A))', 'Ep(B)/sqrt(Zval(B))']<br>\n        and PCA method, one obtains a result similar to Fig. 4 in Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, Phys. Rev. B 85, 104104 (2012).\n          The plot may appear mirrored because the sign of the principal component is immaterial. Besides, the input data are slightly different (here, everything is calculated at the converged LDA level).\n        </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>"
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                },
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                    "<script>",
                    "var run_lasso = function() {",
                    "  $(\"#lasso_result_button\").removeClass(\"active\").addClass(\"disabled\");",
                    "  getFeatures();",
                    "  getEmbedMethod();",
                    "  getStandardize();",
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                    "  getUnits();",
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                    "  beaker.evaluate(\"lasso_cell\"); // evaluate cells with tag \"lasso_cell\"",
                    " // view_result()",
                    "};",
                    "var reset_lasso = function(){",
                    "  beaker.evaluate(\"lasso_gui\");",
                    "};",
                    "var getFeatures = function() {",
                    "    beaker.selected_feature_list = [];",
                    "    $('#lasso_features_select input:checkbox').each(function () {",
                    "        if(this.checked )",
                    "          beaker.selected_feature_list.push(this.value);",
                    "    });",
                    "};",
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                    "  ",
                    "var getUnits = function() {",
                    "   beaker.units = $(\"#units_select\").val();",
                    "};",
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                    "  ",
                    "var getEmbedMethod = function() {",
                    "   beaker.embed_method = \"pca\";",
                    "   $('#embed_method_selector input:radio').each(function () {",
                    "     if(this.checked )",
                    "       beaker.embed_method = this.value;",
                    "   });",
                    "};",
                    "  ",
                    "var getStandardize = function() {",
                    "   beaker.standardize = \"yes\";",
                    "   $('#standardize input:radio').each(function () {",
                    "     if(this.checked )",
                    "       beaker.standardize = this.value;",
                    "   });",
                    "};",
                    "  ",
                    "beaker.view_result = function(result_link) {",
                    "//   beaker.evaluate(\"lasso_viewer_result\").then(function(x) {",
                    "    $(\"#lasso_result_button\").attr(\"href\", result_link);",
                    "//   }); ",
                    "  $(\"#lasso_result_button\").removeClass(\"disabled\").addClass(\"active\");",
                    "}",
                    "</script>",
                    "<style type=\"text/css\">",
                    "  label {",
                    "    font-size: 18px;",
                    "  }",
                    " .lasso_control{",
                    "    font-size: 18px;",
                    "  }   ",
                    ".lasso_form_group input {",
                    "    width: 15px;",
                    "    height: 15px;",
                    "    padding: 0;",
                    "    margin:0;",
                    "    padding-right:5px; ",
                    "    vertical-align: bottom;",
                    "    top: -1px;",
                    "} ",
                    " .lasso_selection_description{",
                    "        padding: 10px 15px;",
                    "  }",
                    "</style>",
                    "<div class=\"lasso_control\">",
                    "  <div class=\"row\">",
                    "    <p class=\"lasso_selection_description\"><b>Primary features </b>",
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                    "  (hover the mouse pointer over the feature names to see a full description):</p>",
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                    "    <form id=\"lasso_features_select\">",
                    "      <div class=\"lasso_form_group\">",
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                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_ionization_potential\" CHECKED > <span title=\"Atomic ionization potential\"><i>IP</i> </span></label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_electron_affinity\" CHECKED > <span title=\"Atomic electron affinity\"> <i>EA</i></span></label>",
                    "          <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_homo\"  > <span title=\"Energy of highest occupied molecular orbital\"><i>E</i> <sub>HOMO</sub></span> </label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-2\"> <input type=\"checkbox\" value=\"atomic_lumo\"  > <span title=\"Energy of lowest unoccupied molecular orbital\"> <i>E</i> <sub>LUMO</sub>  </span> </label>",
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                    "        ",
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                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_rs_max\" CHECKED > <span title=\"Radius at which the radial probability density of the valence s orbital is maximum\"> <i>r</i><sub>s</sub>  </span> </label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_rp_max\" CHECKED > <span title=\"Radius at which the radial probability density of the valence p orbital is maximum\"> <i>r</i><sub>p</sub>  </span> </label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_rd_max\" CHECKED > <span title=\"Radius at which the radial probability density of the valence d orbital is maximum\"> <i>r</i><sub>d</sub>  </span> </label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_number\" > <span title=\"Atomic number\"> <i>Z</i>  </span> </label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_number_valence_electrons\" > <span title=\"Number of valence electrons\"> <i>Z</i><sub>val</sub>  </span> </label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"period\" > <span title=\"Period (in the periodic table)\"> <i>n</i> <sub>period</sub>  </span> </label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_r_by_2_dimer\" > <span title=\"Bond length of the dimer\"> <i>d</i> <sub>dimer</sub> </span> </label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_electronic_binding_energy_dimer\" > <span title=\"Binding energy of the dimer\"> <i>E</i> <sub>b</sub> </span> </label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"atomic_homo_lumo_diff\" > <span title=\"HOMO-LUMO gap of the dimer\"> Δ<i>E</i><sub>HL</sub>  </span> </label>",
                    "        ",
                    "<!---              <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"Es/sqrt(Zval)\"  > ",
                    "         <span title=\"Energy of the valence s orbital(s) divided by the square root of the number of valence electrons. [Phys. Rev. B 85, 104104 (2012)]\"> <i>E</i><sub>s</sub>/sqrt(<i>Z</i> <sub>val</sub>) </span> </label>",
                    "         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"Ep/sqrt(Zval)\"  > ",
                    "         <span title=\"Energy of the valence p orbital(s) divided by the square root of the number of valence electrons. [Phys. Rev. B 85, 104104 (2012)]\"> <i>E</i><sub>p</sub>/sqrt(<i>Z</i> <sub>val</sub>) </span> </label>",
                    "-->      ",
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                    "      </div>",
                    "    </form>",
                    "  </div>  <!-- End of row-->",
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                    "    ",
                    "  <br>",
                    "    <div class=\"row\"> <!-- Start of row-->",
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                    "  <p class=\"lasso_selection_description\"><b>Units of measurement: </b> ",
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                    "  <select id='units_select'>",
                    "    <option value=\"eV_angstrom\" > [energy]=eV;&nbsp;&nbsp;[length]=angstrom</option>",
                    "    <option value=\"J_m\" > [energy]=J;&nbsp;&nbsp;[length]=m</option>",
                    "    <option value=\"kcal/mol_angstrom\" > [energy]=kcal/mol;&nbsp;&nbsp;[length]=angstrom</option>",
                    "  </select> </p>",
                    "  </div><!-- End of row-->",
                    "  ",
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                    "  <br>",
                    "  <div class=\"row\"> <!-- Start of second row-->",
                    "      <div class=\"lasso_form_group\">",
                    "        <p class=\"lasso_selection_description\"><b>Embedding methods:</b> </p>",
                    "        <div id='embed_method_selector'>",
                    "          <label class =\"col-xs-4 col-md-4 col-lg-4\"><input type=\"radio\" name=\"inlineRadioOptions\" id=\"inlineRadio1\" value=\"pca\" CHECKED>  Principal Compenent Analysis (PCA) [<a href=\"https://en.wikipedia.org/wiki/Principal_component_analysis\" target=\"_blank\">more info</a>]</label>",
                    "          <label class =\"col-xs-4 col-md-4 col-lg-4\"><input type=\"radio\" name=\"inlineRadioOptions\" id=\"inlineRadio2\" value=\"mds\"> Multidimensional scaling (MDS) [<a href=\"https://en.wikipedia.org/wiki/Multidimensional_scaling\" target=\"_blank\">more info</a>]</label>",
                    "          <label class =\"col-xs-4 col-md-4 col-lg-4\"><input type=\"radio\" name=\"inlineRadioOptions\" id=\"inlineRadio3\" value=\"tsne_pca\"> t-Distributed Stochastic Neighbor Embedding (t-SNE) [<a href=\"https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding\" target=\"_blank\">more info</a>]</label>",
                    "        </div>         ",
                    "      </div>",
                    "  </div><!-- End of row-->  ",
                    "    <div class=\"row\"> <!-- Start of second row-->",
                    "      <div class=\"lasso_form_group\">",
                    "        <p class=\"lasso_selection_description\"><b>Scale data to unit-variance:</b>",
                    "        (data are centered around the mean in any case) [<a href=\"https://en.wikipedia.org/wiki/Feature_scaling\" target=\"_blank\">more info</a>]</p>",
                    "        <div id='standardize'>",
                    "          <label class =\"col-xs-4 col-md-4 col-lg-4\"><input type=\"radio\" name=\"inlineRadioOptionsStandardize\" id=\"inlineRadio4\" value=\"True\" CHECKED> yes </label>",
                    "          <label class =\"col-xs-4 col-md-4 col-lg-4\"><input type=\"radio\" name=\"inlineRadioOptionsStandardize\" id=\"inlineRadio5\" value=\"False\"> no </label>",
                    "        </div>         ",
                    "      </div>",
                    "  </div><!-- End of row-->  ",
                    "  <br>",
                    "",
                    "<!-- <span title=''> <img src=\"http://images.clipartpanda.com/question-purzen_Icon_with_question_mark_Vector_Clipart.png\" style=\"height: 30px; width: 30px;\"> </span> -->",
                    "  <button class=\"btn btn-default\" onclick='run_lasso()'>RUN TWO-DIMENSIONAL EMBEDDING</button>",
                    "  <button class=\"btn btn-default\" onclick='reset_lasso()'>RESET</button>",
                    "  <label title=\"This button becomes active when the run is finished. By clicking it, an interactive structural-similarity plot will be opened\"> <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\" >View interactive 2D scatter plot</a> </label>",
                    "</div> <!-- End of lasso_control -->",
                    ""
                ],
                "hidden": true
            },
            "output": {
                "selectedType": "BeakerDisplay",
                "outputArrived": true,
                "elapsedTime": 0,
                "state": {},
                "result": {
                    "type": "BeakerDisplay",
                    "innertype": "Html",
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                    "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  getEmbedMethod();\n  getStandardize();\n  getUnits();\n  beaker.evaluate(\"lasso_cell\"); // evaluate cells with tag \"lasso_cell\"\n // view_result()\n};\nvar reset_lasso = function(){\n  beaker.evaluate(\"lasso_gui\");\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};\n  \nvar getUnits = function() {\n   beaker.units = $(\"#units_select\").val();\n};\n  \nvar getEmbedMethod = function() {\n   beaker.embed_method = \"pca\";\n   $('#embed_method_selector input:radio').each(function () {\n     if(this.checked )\n       beaker.embed_method = this.value;\n   });\n};\n  \nvar getStandardize = function() {\n   beaker.standardize = \"yes\";\n   $('#standardize input:radio').each(function () {\n     if(this.checked )\n       beaker.standardize = this.value;\n   });\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  label {\n    font-size: 18px;\n  }\n .lasso_control{\n    font-size: 18px;\n  }   \n.lasso_form_group input {\n    width: 15px;\n    height: 15px;\n    padding: 0;\n    margin:0;\n    padding-right:5px; \n    vertical-align: bottom;\n    top: -1px;\n} \n .lasso_selection_description{\n        padding: 10px 15px;\n  }\n</style>\n<div class=\"lasso_control\">\n  <div class=\"row\">\n    <p class=\"lasso_selection_description\"><b>Primary features </b>\n  (hover the mouse pointer over the feature names to see a full description):</p>\n    <form id=\"lasso_features_select\">\n      <div class=\"lasso_form_group\">\n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_ionization_potential\" checked=\"\" type=\"checkbox\"> <span title=\"Atomic ionization potential\"><i>IP</i> </span></label>\n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_electron_affinity\" checked=\"\" type=\"checkbox\"> <span title=\"Atomic electron affinity\"> <i>EA</i></span></label>\n          <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_homo\" type=\"checkbox\"> <span title=\"Energy of highest occupied molecular orbital\"><i>E</i> <sub>HOMO</sub></span> </label>\n         <label class=\"col-xs-4 col-md-4 col-lg-2\"> <input value=\"atomic_lumo\" type=\"checkbox\"> <span title=\"Energy of lowest unoccupied molecular orbital\"> <i>E</i> <sub>LUMO</sub>  </span> </label>\n        \n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_rs_max\" checked=\"\" type=\"checkbox\"> <span title=\"Radius at which the radial probability density of the valence s orbital is maximum\"> <i>r</i><sub>s</sub>  </span> </label>\n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_rp_max\" checked=\"\" type=\"checkbox\"> <span title=\"Radius at which the radial probability density of the valence p orbital is maximum\"> <i>r</i><sub>p</sub>  </span> </label>\n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_rd_max\" checked=\"\" type=\"checkbox\"> <span title=\"Radius at which the radial probability density of the valence d orbital is maximum\"> <i>r</i><sub>d</sub>  </span> </label>\n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_number\" type=\"checkbox\"> <span title=\"Atomic number\"> <i>Z</i>  </span> </label>\n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_number_valence_electrons\" type=\"checkbox\"> <span title=\"Number of valence electrons\"> <i>Z</i><sub>val</sub>  </span> </label>\n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"period\" type=\"checkbox\"> <span title=\"Period (in the periodic table)\"> <i>n</i> <sub>period</sub>  </span> </label>\n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_r_by_2_dimer\" type=\"checkbox\"> <span title=\"Bond length of the dimer\"> <i>d</i> <sub>dimer</sub> </span> </label>\n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_electronic_binding_energy_dimer\" type=\"checkbox\"> <span title=\"Binding energy of the dimer\"> <i>E</i> <sub>b</sub> </span> </label>\n         <label class=\"col-xs-4 col-md-4 col-lg-3\"> <input value=\"atomic_homo_lumo_diff\" type=\"checkbox\"> <span title=\"HOMO-LUMO gap of the dimer\"> Δ<i>E</i><sub>HL</sub>  </span> </label>\n        \n<!---              <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"Es/sqrt(Zval)\"  > \n         <span title=\"Energy of the valence s orbital(s) divided by the square root of the number of valence electrons. [Phys. Rev. B 85, 104104 (2012)]\"> <i>E</i><sub>s</sub>/sqrt(<i>Z</i> <sub>val</sub>) </span> </label>\n         <label class =\"col-xs-4 col-md-4 col-lg-3\"> <input type=\"checkbox\" value=\"Ep/sqrt(Zval)\"  > \n         <span title=\"Energy of the valence p orbital(s) divided by the square root of the number of valence electrons. [Phys. Rev. B 85, 104104 (2012)]\"> <i>E</i><sub>p</sub>/sqrt(<i>Z</i> <sub>val</sub>) </span> </label>\n-->      \n      </div>\n    </form>\n  </div>  <!-- End of row-->\n    \n  <br>\n    <div class=\"row\"> <!-- Start of row-->\n  <p class=\"lasso_selection_description\"><b>Units of measurement: </b> \n  <select id=\"units_select\">\n    <option value=\"eV_angstrom\"> [energy]=eV;&nbsp;&nbsp;[length]=angstrom</option>\n    <option value=\"J_m\"> [energy]=J;&nbsp;&nbsp;[length]=m</option>\n    <option value=\"kcal/mol_angstrom\"> [energy]=kcal/mol;&nbsp;&nbsp;[length]=angstrom</option>\n  </select> </p>\n  </div><!-- End of row-->\n  \n  <br>\n  <div class=\"row\"> <!-- Start of second row-->\n      <div class=\"lasso_form_group\">\n        <p class=\"lasso_selection_description\"><b>Embedding methods:</b> </p>\n        <div id=\"embed_method_selector\">\n          <label class=\"col-xs-4 col-md-4 col-lg-4\"><input name=\"inlineRadioOptions\" id=\"inlineRadio1\" value=\"pca\" checked=\"\" type=\"radio\">  Principal Compenent Analysis (PCA) [<a href=\"https://en.wikipedia.org/wiki/Principal_component_analysis\" target=\"_blank\">more info</a>]</label>\n          <label class=\"col-xs-4 col-md-4 col-lg-4\"><input name=\"inlineRadioOptions\" id=\"inlineRadio2\" value=\"mds\" type=\"radio\"> Multidimensional scaling (MDS) [<a href=\"https://en.wikipedia.org/wiki/Multidimensional_scaling\" target=\"_blank\">more info</a>]</label>\n          <label class=\"col-xs-4 col-md-4 col-lg-4\"><input name=\"inlineRadioOptions\" id=\"inlineRadio3\" value=\"tsne_pca\" type=\"radio\"> t-Distributed Stochastic Neighbor Embedding (t-SNE) [<a href=\"https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding\" target=\"_blank\">more info</a>]</label>\n        </div>         \n      </div>\n  </div><!-- End of row-->  \n    <div class=\"row\"> <!-- Start of second row-->\n      <div class=\"lasso_form_group\">\n        <p class=\"lasso_selection_description\"><b>Scale data to unit-variance:</b>\n        (data are centered around the mean in any case) [<a href=\"https://en.wikipedia.org/wiki/Feature_scaling\" target=\"_blank\">more info</a>]</p>\n        <div id=\"standardize\">\n          <label class=\"col-xs-4 col-md-4 col-lg-4\"><input name=\"inlineRadioOptionsStandardize\" id=\"inlineRadio4\" value=\"True\" checked=\"\" type=\"radio\"> yes </label>\n          <label class=\"col-xs-4 col-md-4 col-lg-4\"><input name=\"inlineRadioOptionsStandardize\" id=\"inlineRadio5\" value=\"False\" type=\"radio\"> no </label>\n        </div>         \n      </div>\n  </div><!-- End of row-->  \n  <br>\n\n<!-- <span title=''> <img src=\"http://images.clipartpanda.com/question-purzen_Icon_with_question_mark_Vector_Clipart.png\" style=\"height: 30px; width: 30px;\"> </span> -->\n  <button class=\"btn btn-default\" onclick=\"run_lasso()\">RUN TWO-DIMENSIONAL EMBEDDING</button>\n  <button class=\"btn btn-default\" onclick=\"reset_lasso()\">RESET</button>\n  <label title=\"This button becomes active when the run is finished. By clicking it, an interactive structural-similarity plot will be opened\"> <a href=\"#\" target=\"_blank\" class=\"btn btn-primary disabled\" id=\"lasso_result_button\">View interactive 2D scatter plot</a> </label>\n</div> <!-- End of lasso_control -->\n"
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                },
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                "height": 553
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            },
            "evaluatorReader": true,
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            "lineCount": 137
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        },
        {
            "id": "code2uVtKX",
            "type": "code",
            "evaluator": "IPython",
            "input": {
                "body": [
                    "from IPython.core.display import HTML ",
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                    "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_crystals import create_supercell",
                    "from nomad_sim.utils_crystals import create_vacancies",
                    "from nomad_sim.utils_crystals import random_displace_atoms",
                    "from nomad_sim.utils_crystals import substitute_atoms",
                    "from nomad_sim.descriptors import XrayDiffraction",
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                    "from nomad_sim.utils_crystals import create_supercell",
                    "",
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                    "# hack to change to local/Beaker mode in all files in the packages",
                    "# DEPRECATED",
                    "import __builtin__",
                    "__builtin__.isBeaker = True",
                    "",
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                    "import hashlib",
                    "",
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                    "import sys, os",
                    "import pandas as pd",
                    "import numpy as np",
                    "import json",
                    "",
                    "",
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                    "# define paths",
                    "tmp_folder = '/home/beaker/.beaker/v1/web/tmp/'",
                    "control_file = '/home/beaker/.beaker/v1/web/tmp/control.json'",
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                    "data_folder='/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/'",
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                    "lookup_file = '/home/beaker/.beaker/v1/web/tmp/lookup.dat'",
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                    "collection_path = '/home/beaker/test/nomad_sim/data_zcrs/ExtendedBinaries_Dimers_Atoms_new.json'",
                    "path_to_collection = '/home/beaker/test/nomad_sim/data_zcrs/ExtendedBinaries_Dimers_Atoms_new.json'",
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                    "",
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                    "# define units",
                    "if beaker.units == 'eV_angstrom':",
                    "    energy_unit = 'eV'",
                    "    length_unit = 'angstrom'",
                    "elif beaker.units == 'J_m':",
                    "    energy_unit = 'J'",
                    "    length_unit = 'm'",
                    "elif beaker.units == 'kcal/mol_angstrom':",
                    "    energy_unit = 'kcal/mol'",
                    "    length_unit = 'angstrom'"
                ],
                "hidden": true
            },
            "output": {
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                "selectedType": "Results",
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                "state": {},
                "pluginName": "IPython",
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                "shellId": "3A80290908F64F10A4B82404FBACE0BA",
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                "height": 103,
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                "elapsedTime": 3244
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            },
            "evaluatorReader": true,
            "lineCount": 42,
            "tags": "lasso_cell"
        },
        {
            "id": "codelrvhSB",
            "type": "code",
            "evaluator": "IPython",
            "input": {
                "body": [
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                    "# get the json_list",
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                    "#json_list = get_json_list(method='folder', drop_duplicates=False, ",
                    "#    data_folder=data_folder, tmp_folder=tmp_folder)",
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                    "",
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                    "# pass only lowest energy structures to save time for demonstation purposes",
                    "json_list = [",
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                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/P-M1B6jU_t-kPPKkoFU9kZkEbx332.json', ",
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                    ""
                ],
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        {
            "id": "codeVePPlk",
            "type": "code",
            "evaluator": "IPython",
            "input": {
                "body": [
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                    "operations_on_structure = [(create_supercell, {'replicas': [3, 3, 3]})]",
                    "op_list = np.zeros(len(json_list))",
                    "",
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                    "kwargs = {'path_to_collection': path_to_collection,",
                    "          'feature_order_by': 'atomic_mulliken_electronegativity',",
                    "          'energy_unit': energy_unit,",
                    "          'length_unit': length_unit}",
                    "",
                    "dict_delta_e = {'SeZn': 4.2159179660687287e-20, 'InSb': 1.2506570609555687e-20, 'AgCl': -6.8568799955090609e-21, 'SZn': 4.4190167090138643e-20, 'BN': 2.7430550221687302e-19, 'GaSb': 2.4773702178144287e-20, 'BrRb': -2.6246943074069208e-20, 'BaTe': -6.0143598233041078e-20, 'BeSe': 7.9298202208796193e-20, 'MgS': -1.3890792272791661e-20, 'AsB': 1.4018696180162358e-19, 'AlAs': 3.416831556361176e-20, 'BP': 1.632978757533666e-19, 'TeZn': 3.9253535511316122e-20, 'MgSe': -8.8603255975880209e-21, 'ClLi': -6.149391549293258e-21, 'FK': -2.3456843288794608e-20, 'BrLi': -5.2465217764284492e-21, 'BSb': 9.3062289002001379e-20, 'ClRb': -2.5715504707788539e-20, 'GeSn': 1.3083912918128912e-20, 'CsI': -2.6017342042091155e-20, 'CaTe': -5.6149286826500206e-20, 'ClK': -2.6349506219210773e-20, 'Sn2': 2.7179163244033424e-21, 'BrCs': -2.4972695386489382e-20, 'CsF': -1.734569615637085e-20, 'BrCu': 2.442400384019518e-20, 'CaSe': -5.7806170659176063e-20, 'AgF': -2.4634695420313482e-20, 'MgTe': -7.3560522736479063e-22, 'FLi': -9.5310792412059186e-21, 'CuF': -2.7272687327072279e-21, 'FNa': -2.3357835066436331e-20, 'C2': 4.2114873809101575e-19, 'BaO': -1.4900011177054134e-20, 'AgBr': -4.8118839046830307e-21, 'MgO': -3.721451404088126e-20, 'FRb': -2.1724838814450727e-20, 'AlN': 1.1687730189874494e-20, 'Si2': 4.4727296163305501e-20, 'SiSn': 2.1646816357014748e-20, 'OSr': -3.5297012817210187e-20, 'ClNa': -2.1307665354820141e-20, 'AsIn': 2.14767895373523e-20, 'OZn': 1.633710321777262e-20, 'CGe': 1.3000748379902827e-19, 'CdO': -1.348413629348854e-20, 'InP': 2.8709930119109753e-20, 'SSr': -5.9029656118592692e-20, 'InN': 2.4628706405675984e-20, 'BaSe': -5.5025977545738119e-20, 'BrK': -2.6624325013472597e-20, 'BeTe': 7.5075740576200973e-20, 'CdS': 1.1643465692630618e-20, 'CdTe': 1.8351256432814928e-20, 'GeSi': 4.217091895225337e-20, 'GaP': 5.5876198809236085e-20, 'CdSe': 1.3389702567051265e-20, 'INa': -1.8399111846593045e-20, 'AlP': 3.5080994271602511e-20, 'BeO': 1.1084460139894976e-19, 'AsGa': 4.3944144343852047e-20, 'Ge2': 3.218012279776111e-20, 'SeSr': -6.0003270319365202e-20, 'CSi': 1.071894196156348e-19, 'BaS': -5.1231589897332471e-20, 'AgI': 5.9161045280535275e-21, 'GaN': 6.9445584247860156e-20, 'CaS': -5.9141658617526103e-20, 'AlSb': 2.5133142314028706e-20, 'IK': -2.6762621853286689e-20, 'ILi': -3.4704646494924847e-21, 'ClCs': -2.4088110334584613e-20, 'CaO': -4.2492775596486839e-20, 'CuI': 3.2792483878850995e-20, 'CSn': 7.2664795347721018e-20, 'BeS': 8.1122637897805144e-20, 'IRb': -2.6788624696652254e-20, 'BrNa': -2.0256115610545176e-20, 'SrTe': -6.0769715445352658e-20, 'ClCu': 2.5035406212316063e-20}",
                    "",
                    "#derived_features = []",
                    "#selected_feature_list = beaker.selected_feature_list",
                    "",
                    "#if 'Es/sqrt(Zval)' in selected_feature_list:",
                    "#    derived_features.append('Es/sqrt(Zval)')",
                    "#    selected_feature_list.remove('Es/sqrt(Zval)')",
                    "#    selected_feature_list.append('atomic_valence_s_orbital')",
                    "#    selected_feature_list.append('atomic_number_valence_electrons')",
                    "    ",
                    "#if 'Ep/sqrt(Zval)' in selected_feature_list:",
                    "#    derived_features.append('Ep/sqrt(Zval)')",
                    "#    selected_feature_list.remove('Ep/sqrt(Zval)')",
                    "#    selected_feature_list.append('atomic_valence_p_orbital')",
                    "#    selected_feature_list.append('atomic_number_valence_electrons')",
                    "   ",
                    "descriptor = calc_descriptor(",
                    "    desc_type='atomic_features',",
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                    "    selected_feature_list=beaker.selected_feature_list,",
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                    "    dict_delta_e=dict_delta_e,",
                    "    op_list=op_list,",
                    "    operations_on_structure=operations_on_structure,",
                    "    json_list=json_list, tmp_folder=tmp_folder,",
                    "    **kwargs)"
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                ],
                "hidden": true
            },
            "output": {
                "state": {},
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                "selectedType": "Results",
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                "pluginName": "IPython",
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                "shellId": "3A80290908F64F10A4B82404FBACE0BA",
                "elapsedTime": 13620,
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                "height": 166
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            },
            "evaluatorReader": true,
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            "lineCount": 33,
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            "tags": "lasso_cell"
        },
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        {
            "id": "codeZFE5kr",
            "type": "code",
            "evaluator": "IPython",
            "input": {
                "body": [
                    "# pass only lowest energy structures to save time for demonstation purposes",
                    "json_list = [",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pm0_fbKdKA2iyued6niH-AARk8hhM.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PzZe8HJ1RoiT6LBluiHmTN9IDP6vE.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/P6N-eaR5japcqjIGylr67mAGo9L-S.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PMbzdub7JONozp5LPWlPqLbGuLt3F.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PyBnwEdQ98isxcx9_miHJ2Tr82JrN.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PECMSMMNgxQUVLxlv5IWYEvWOatMh.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PK5EwBMnyyGjm5_lykmBBaMU7FzFl.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PezFp7D_Pzi-KwwYE9WlnFWtpTP_2.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PlQ7NfvecVk8-o2I_Fbz0hNtkAJAw.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PWBxopsGbXEANMPDUxcMi-PzKvqxH.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PUC2t35p9KOEdmaAyB7I91DoUyae7.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/P1RXpIJmIprXumBAD3Lk20-RwmC19.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PoXHWtsIc1BlQ7N2bsUiZ0PJnFa6O.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PVvk2rLGsl4Gd6Q3l0Cbnyi1bM4XO.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pb4Ku0TY7IkW9pjHBECQguVhvtd6Q.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pqky2IgyYljS01KXKFanIV11nbcCT.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PeXUCf_QcDwfIhLJTg61D3lsjvJQu.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PVH2AkTXt2QDVEfJdFkGPAMk1_dQO.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PLoQIJtvhgUQcXFhb0k_6mWOPV9NI.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pp-9DTkK5y5w7fFZOf-5JJc9SCPD1.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PN0pxdAiZpbbUV2jORc4LSy5MaYWe.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PGex81N1PxLHkRkSGopRqqLQ4tSkp.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PWNJV2eK0tIrw_AEg-EpXTggLH88h.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PbkJ-LOXCmwltwIWDXwnHWXpySRVi.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PB-GqTDr-DZ-j4OKjRNJEp1hnOGvG.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pb4jnAWzhAL1kkV-J0QHJTsWUtBCj.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PwXFVrN5zPsZq5W93S0r3XPR3O7kq.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PhF_jMdta8Ncok9i2JHC7G1ZM5KPP.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pt5thhX-pEWdlL6-DGsQe2r6Gr-lu.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PSvyfO0p4QEfhh7dUujLdUg8lCNs0.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PHQG6-EPlnROo0wmc11YFOLefErCO.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/P_Lx9ePtVOK5MyoQBlUWA_kePKF_J.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/P-WFXv4pg5JXNw8v86SVKW9_gFrbO.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PkP9vxbD_d5in7JZZd-W-Rv7yvYzJ.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pi1rNqBwWwWQBy5RoGVMcJaix-ISM.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pz-zWbbn5PNd-5CJdBVD60npmzSwn.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PTijdgDWu11E79tuyylukptiyCtv2.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pr3fuw6xCJS5vZiUf9B5tW2KT_LQW.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PGsPQn1h36VyBTthr3CnA6yAtlzs3.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Ph-6A75k6v-qJ-tgzcs-BoIWFGqQb.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/P4dSQn4GKPhaTawz0JtVOyotDaGom.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PbV7iF5sNHb5Y7MIF4vaxwqWLsHdh.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PVQgfnLx6_iwg7AoMC0GB0VQmBJ6g.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Ppn_-f3QxBgeCwkICEO4BL62GIBx6.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PJYTR_x-6ANZ-cAsi75D5h9Gvb1e-.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PJz4SOdD_cp-YPSfaJ0QWYOzYiZB7.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PcpD0axNY3fEO1jmggSHMCtnWuX2q.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pb0-IKoZYu3Pmbu323yUsGR8jQe_e.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/P-1SH_T1kd13-U3MEB7Xz-_eToBHT.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PUcFCGgEnxeFQIWTg8qeByla9jJJg.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pd23q1LJrA4DOKbForwH4cvHWRk6U.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PB8zc5-R1ZXaxutxBRsD247NMyR2N.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Phb63KR9BOj86coXGS0bKGD2xq5O2.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PZo5v3_KRI2CARrYsoiYNun-FaJCd.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PLlOCjkEQ61Se5wdc_H7h4MP65gOC.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PSFiXUNv75SzlqJDfVZ0BFKrSsAax.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PWoElPtI5U48PFUHK-yXJfn4JaasP.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/P0Zgt21TvKKb18vKKFKXYNI-bDofb.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pv4l5-nI7xyQQgnAULtpVXTuXvZo9.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PtPzGOtC64C9WwpqoUjnlET1liwRP.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PC4jwHwSTdNEilYtIDuuNIRBUH_Df.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PkUwSxY7ro62M6SUfGJGZJTa1z1G0.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/POHjGTnYd8JGgqzKrI5tTc_o8GPAy.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pg4Lo5RY8cWWojQUOg9ikurdCqPnb.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PC0ikq0ulkygT2Co59UBAl9YcYxbG.json',",
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                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PF8Zb1nzPb5YMugWmjUm0gAS0JySC.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PKCyiUMeNTeE2Qp-8ElDtGu3iDVh0.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PokNGy5MbvPoNIi4g95YgX_oF4AI6.json',",
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                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PavCzBt15bIH5NeKUXulmwe7uQyAM.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PRakENtX-ME-LrbIo19w0RDyRE6Bi.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pbb6tWhL8Cn4P0j4XcwS8O6oopygF.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PwTh1t979bFWSWD2gFWLF_rVtJKv8.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/P1cjW50CGsQC-wkw0ZfTGzpzrdXCQ.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pq0gpi99XHDz4L10rfZOe1YlMTo5R.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PFvJLQd4N-p0O9ytP6TRzvEqM94gJ.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/PUzjQT0patVJ9CyvEnKl_xQwoO7iX.json',",
                    "'/parsed/prod-017/FhiAimsParser2.0.0/RWApItBGtGUDsfMVlHKqrjUQ4rShT/Pj4jIPerqprBjHAT6ZKsbXGPsSmze.json']",
                    ""
                ],
                "hidden": true
            },
            "output": {
                "state": {},
                "selectedType": "Hidden",
                "pluginName": "IPython",
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                "shellId": "3A80290908F64F10A4B82404FBACE0BA",
                "elapsedTime": 398,
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                "height": 78
            },
            "evaluatorReader": true,
            "tags": "lasso_cell",
            "lineCount": 85
        },
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        {
            "id": "codeeWTtU4",
            "type": "code",
            "evaluator": "IPython",
            "input": {
                "body": [
                    "embed_params = {'learning_rate': 20}",
                    "",
                    "calc_embedding(embed_method=beaker.embed_method, embed_params=embed_params,",
                    "              desc_type='atomic_features',",
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                    "              energy_unit=energy_unit,",
                    "              length_unit=length_unit,",
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                    "              lookup_file=lookup_file, tmp_folder=tmp_folder,",
                    "              standardize=beaker.standardize)",
                    ""
                ],
                "hidden": true
            },
            "output": {
                "state": {},
                "selectedType": "Results",
                "pluginName": "IPython",
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                "shellId": "3A80290908F64F10A4B82404FBACE0BA",
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                "height": 78,
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                "elapsedTime": 385
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            },
            "evaluatorReader": true,
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            "lineCount": 9,
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            "tags": "lasso_cell"
        },
        {
            "id": "lasso_viewer_result",
            "type": "code",
            "evaluator": "IPython",
            "input": {
                "body": [
                    "parameter_list = beaker.selected_feature_list",
                    "parameter_list.append(beaker.embed_method)",
                    "",
                    "name_html_page = hashlib.sha224(str(parameter_list)).hexdigest()[:16]",
                    "",
                    "json_list, frame_list, x_list, y_list, target_list = get_json_list(method='file', data_folder=data_folder,",
                    "    path_to_file=lookup_file, drop_duplicates=True, displace_duplicates=True, predicted_value=False)",
                    "beaker.viewer_result = name_html_page",
                    "",
                    "plot_result = plot(name=name_html_page, json_list=json_list, frames='list', frame_list=frame_list, ",
                    "    file_format='NOMAD', clustering_x_list=x_list, clustering_y_list=y_list, target_list=target_list,",
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                    "    target_unit=energy_unit, energy_unit=energy_unit, legend_title='Reference E(RS)-E(ZB)', target_name='E(RS)-E(ZB)',",
                    "    plot_title='Two-dimensional embedding',",
                    "    clustering_point_size=12, tmp_folder=tmp_folder, control_file=control_file,",
                    "    op_list=op_list, operations_on_structure=operations_on_structure)",
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                    ""
                ],
                "hidden": true
            },
            "output": {
                "state": {},
                "selectedType": "Results",
                "pluginName": "IPython",
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                "shellId": "3A80290908F64F10A4B82404FBACE0BA",
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                "height": 78,
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                "elapsedTime": 7568
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            },
            "evaluatorReader": true,
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            "lineCount": 16,
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            "tags": "lasso_cell"
        },
        {
            "id": "markdownetkGD7",
            "type": "markdown",
            "body": [
                "<!-- <p style=\"font-size: 15px;\"> <b> Note </b>: the default selection will produce an interactive version of  </p>",
                "<div class=\"crop\"   style=\"overflow:hidden;height:346px;width:346px\"> ",
                "<a href=\"http://journals.aps.org/prl/article/10.1103/PhysRevLett.114.105503/figures/2/medium\" target=\"_blank\"> <img style=\"margin: -188px 0 0 0\" src=\"http://journals.aps.org/prl/article/10.1103/PhysRevLett.114.105503/figures/2/medium\"> </a>",
                "</div>",
                "<p  style=\"font-size: 15px;\"> from <a href=\"http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503\" target=\"_blank\"> Phys. Rev. Lett. 114, 105503 (2015) </a>. </p> -->"
            ],
            "evaluatorReader": false
        },
        {
            "id": "codeVFrr3c",
            "type": "code",
            "evaluator": "JavaScript",
            "input": {
                "body": [
                    "var result_link = '/user/tmp/' + beaker.viewer_result + '.html';",
                    "beaker.view_result(result_link);"
                ],
                "hidden": true
            },
            "output": {
                "selectedType": "BeakerDisplay",
                "pluginName": "JavaScript",
                "state": {},
                "hidden": true,
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                "elapsedTime": 48,
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                "height": 51
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            },
            "evaluatorReader": true,
            "lineCount": 2,
            "tags": "lasso_cell"
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