-
ankit kariryaa authoredankit kariryaa authored
query_example_v1_1.bkr 85.26 KiB
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"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."
],
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"type": "section",
"title": "Query Example",
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"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,
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"id": "codecJWYiO",
"type": "code",
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"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\")",
""
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"title": "Put data in a table",
"level": 2,
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"type": "markdown",
"body": [
"Organize results in a table, convert units (from SI)"
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"evaluatorReader": false
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"id": "codeCSQ8CP",
"type": "code",
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"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"
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