diff --git a/query/query_example_v1_1.bkr b/query/query_example_v1_1.bkr deleted file mode 100644 index da4ed650a841492ee8ef99ac1e805bf8a557a710..0000000000000000000000000000000000000000 --- a/query/query_example_v1_1.bkr +++ /dev/null @@ -1,1597 +0,0 @@ -{ - "beaker": "2", - "evaluators": [ - { - "name": "HTML", - "plugin": "HTML", - "view": { - "cm": { - "mode": "htmlmixed" - } - } - }, - { - "name": "JavaScript", - "plugin": "JavaScript", - "view": { - "cm": { - "mode": "javascript", - "background": "#FFE0F0" - } - }, - "languageVersion": "ES2015" - }, - { - "name": "IPython", - "plugin": "IPython", - "setup": "%matplotlib inline\nimport numpy\nimport matplotlib\nfrom matplotlib import pylab, mlab, pyplot\nnp = numpy\nplt = pyplot\nfrom IPython.display import display\nfrom IPython.core.pylabtools import figsize, getfigs\nfrom pylab import *\nfrom numpy import *\n", - "view": { - "cm": { - "mode": "python" - } - } - } - ], - "cells": [ - { - "id": "markdownT9rKmh", - "type": "markdown", - "body": [ - "Warning: running the first cell of this notebook can take a long time(hours).", - "", - "One can run safely the second and third cell, that will analyze a precomputed result of the first cell (queryMgO.out).", - "", - "A more documented version of this notebook will be uploaded soon." - ], - "evaluatorReader": false - }, - { - "id": "sectionQ3dZWh", - "type": "section", - "title": "Query Example", - "level": 1, - "evaluatorReader": false, - "collapsed": false - }, - { - "id": "markdownKIKNEj", - "type": "markdown", - "body": [ - "*Warning*: running the first cell of this notebook can take a long time(hours), we are working to integrate the (much faster) flink query.", - "", - "One can run safely the second and third cell, that will analyze a precomputed result of the first cell (queryMgO.out).", - "", - "A more documented version of this notebook will be uploaded soon." - ], - "evaluatorReader": false - }, - { - "id": "sectionzKgCDS", - "type": "section", - "title": "Scan the whole NOMAD Archive", - "level": 2, - "evaluatorReader": false, - "collapsed": false - }, - { - "id": "codecJWYiO", - "type": "code", - "evaluator": "IPython", - "input": { - "body": [ - "from nomad_structure_tools import query", - "percentageOfArchives = 5", - "", - "def hasMgO(stats):", - " els = set(stats.get(\"elements\",{}).keys())", - " return stats and \"Mg\" in els and \"O\" in els and ((stats.get(\"nEigenvalues\", 0) > 0) or stats.get(\"nBands\", 0) > 0)", - "", - "def getVolumeAndGap(calc):", - " data = calc.json()", - " if not data:", - " print(calc,\"has no json\")", - " return None", - " gaps = []", - " volumes = {}", - " for sectionName1, section1 in data.get('sections', {}).items():", - " if sectionName1.startswith('section_run-'):", - " for sectionName2, section2 in section1.get('sections', {}).items():", - " if sectionName2.startswith('section_single_configuration_calculation-'):", - " gaps.append(gap.gapOfSingleConf(section2))", - " elif sectionName2.startswith('section_system-'):", - " cell = section2.get('simulation_cell')", - " atomLabels = section2.get('atom_labels')", - " atomPos = section2.get('atom_positions')", - " if atomLabels and cell:", - " hasMg = any(map(lambda x: stats.toSymbol(x) == 'Mg', atomLabels))", - " hasO = any(map(lambda x: stats.toSymbol(x) == 'O', atomLabels))", - " nAtoms = len(atomLabels)", - " if hasMg and hasO:", - " volume = abs(np.linalg.det(np.asarray(cell)))", - " volumes[section2['gIndex']] = volume/nAtoms", - " newGaps=[]", - " for g in gaps:", - " systemGIndex = g.get('systemGIndex')", - " if systemGIndex is not None:", - " vol = volumes.get(systemGIndex)", - " if vol:", - " g['volumePerAtom'] = vol", - " newGaps.append(g)", - " if newGaps:", - " return {\"calculationGid\": calc.gid, \"gaps\": newGaps }", - " else:", - " return None", - "", - "def methodToFile(outPath, archiveGroupIndexRange = query.archiveSplit(percentageOfArchives,101), calculationGroupIndexRange = query.calculationSplit(0,1)):", - " \"\"\"perform query, stores result to file\"\"\"", - " with open(outPath, \"w\", encoding=\"utf-8\") as outF:", - " nomadArch.queryCalcs(", - " statsFiltering = hasMgO,", - " calculationOp = getVolumeAndGap,", - " reduceOp = None,", - " f0 = outF,", - " closeOp = None,", - " archiveGroupIndexRange = archiveGroupIndexRange,", - " calculationGroupIndexRange = calculationGroupIndexRange,", - " parserFilter = None)", - "", - "methodToFile(\"/home/beaker/notebooks/data/querMg0.out\")", - "" - ] - }, - "output": { - "state": {} - }, - "evaluatorReader": true, - "lineCount": 58 - }, - { - "id": "sectionLNKIwc", - "type": "section", - "title": "Put data in a table", - "level": 2, - "evaluatorReader": false, - "collapsed": false - }, - { - "id": "markdownqcviza", - "type": "markdown", - "body": [ - "Organize results in a table, convert units (from SI)" - ], - "evaluatorReader": false - }, - { - "id": "codeCSQ8CP", - "type": "code", - "evaluator": "IPython", - "input": { - "body": [ - "import json", - "import pandas as pd", - "#from nomadcore.unit_conversion.unit_conversion import convert_unit_function", - "def convert_unit_function(sourceUnits, targetUnits):", - " if sourceUnits == \"m^3\":", - " return lambda x: x*1.0e30", - " else:", - " return lambda x: x*6.242e+18", - "", - "with open(\"/home/beaker/notebooks/data/queryMgO.out\") as f:", - " d=json.load(f)", - "volumePerAtom=[]", - "calculationGid=[]", - "singleConfCalcGIndex=[]", - "gapAtGamma=[]", - "for v in d:", - " cId=v[\"calculationGid\"]", - " for el in v[\"gaps\"]:", - " gap=el.get(\"gapAtGamma\")", - " if gap:", - " volumePerAtom.append(el[\"volumePerAtom\"])", - " calculationGid.append(cId)", - " gapAtGamma.append(gap)", - " singleConfCalcGIndex.append(el[\"singleConfCalcGIndex\"])", - "df = pd.DataFrame({", - " \"volumePerAtom\": map(convert_unit_function(\"m^3\",\"angstrom^3\"),volumePerAtom),", - " \"calculationGid\": calculationGid,", - " \"singleConfCalcGIndex\":singleConfCalcGIndex,", - " \"gapAtGamma\":map(convert_unit_function(\"J\",\"eV\"),gapAtGamma)", - " })", - "df" - ] - }, - "output": { - "state": { - "datatablestate": { - 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