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apt-analyze

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Actualizado17 de marzo de 2026 a las 18:24

Run a full atom probe analysis pipeline on a data file. Loads data, creates mass spectrum, and guides through ion identification, ranging, and composition calculation. Use for end-to-end APT analysis.

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Fuente

peterfelfer

peterfelfer/Atom-Probe-Toolbox

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SKILL.md

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name	apt-analyze
description	Run a full atom probe analysis pipeline on a data file. Loads data, creates mass spectrum, and guides through ion identification, ranging, and composition calculation. Use for end-to-end APT analysis.
argument-hint	<file.pos\|file.epos>

Run a complete atom probe analysis pipeline. Use the MATLAB MCP server.

The file to analyze is: $ARGUMENTS

If no file is provided, ask the user for the file path.

Full Pipeline

Step 1: Setup (if not already done)

setupToolbox;
load('isotopeTable_naturalAbundances.mat');
load('colorScheme_default.mat');
cfg = APTConfig.getInstance();

Step 2: Ask about the instrument

Before proceeding, ask the user which instrument acquired this data:

LEAP 4000X HR (detEff = 0.37)
LEAP 5000 XR (detEff = 0.52)
LEAP 5000 XS (detEff = 0.80)
EIKOS (detEff = 0.50)

Apply the preset:

cfg.applyInstrumentPreset('LEAP5000XR');  % adjust based on answer

Step 3: Load data

pos = posLoad('$ARGUMENTS');
fprintf('Loaded %d atoms\n', height(pos));

Step 4: Mass spectrum

spec = massSpecPlot(pos, 0.01, 'normalised');

Step 5: Ask about expected elements

Ask the user what elements they expect in their sample (e.g., "Fe, C, Mn" for steel). Then add ion markers:

ionAdd(spec, 'Fe', 2, isotopeTable, colorScheme);
ionAdd(spec, 'C', 1, isotopeTable, colorScheme);
% ... for each element at likely charge states

Step 6: Define ranges

Tell the user to check the spectrum in MATLAB and confirm the ion identification looks correct. Then:

rangeAddAll(spec, colorScheme, 0.04, true);

Step 7: Extract ranges and allocate

rangeTable = rangesExtractFromMassSpec(spec);
pos = posAllocateRange(pos, rangeTable, 'decomposed');

Step 8: Composition

detEff = cfg.reconstruction.detectionEfficiency;
conc = posCalculateConcentrationSimple(pos, detEff, {'unranged'}, '', 'mode', 'atomic');
unc = concentrationUncertainty(conc.counts);
disp(conc);

Step 9: Visualization

scatterPlotPosWidget(pos, colorScheme);

Step 10: Report

Present a summary:

Dataset: file name, atom count
Instrument: name and detection efficiency used
Composition table with uncertainties
Suggest next steps: ROI analysis, proxigram, cluster analysis

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name	apt-analyze
description	Run a full atom probe analysis pipeline on a data file. Loads data, creates mass spectrum, and guides through ion identification, ranging, and composition calculation. Use for end-to-end APT analysis.
argument-hint	<file.pos\|file.epos>

Run a complete atom probe analysis pipeline. Use the MATLAB MCP server.

The file to analyze is: $ARGUMENTS

If no file is provided, ask the user for the file path.

Full Pipeline

Step 1: Setup (if not already done)

setupToolbox;
load('isotopeTable_naturalAbundances.mat');
load('colorScheme_default.mat');
cfg = APTConfig.getInstance();

Step 2: Ask about the instrument

Before proceeding, ask the user which instrument acquired this data:

LEAP 4000X HR (detEff = 0.37)
LEAP 5000 XR (detEff = 0.52)
LEAP 5000 XS (detEff = 0.80)
EIKOS (detEff = 0.50)

Apply the preset:

cfg.applyInstrumentPreset('LEAP5000XR');  % adjust based on answer

Step 3: Load data

pos = posLoad('$ARGUMENTS');
fprintf('Loaded %d atoms\n', height(pos));

Step 4: Mass spectrum

spec = massSpecPlot(pos, 0.01, 'normalised');

Step 5: Ask about expected elements

Ask the user what elements they expect in their sample (e.g., "Fe, C, Mn" for steel). Then add ion markers:

ionAdd(spec, 'Fe', 2, isotopeTable, colorScheme);
ionAdd(spec, 'C', 1, isotopeTable, colorScheme);
% ... for each element at likely charge states

Step 6: Define ranges

Tell the user to check the spectrum in MATLAB and confirm the ion identification looks correct. Then:

rangeAddAll(spec, colorScheme, 0.04, true);

Step 7: Extract ranges and allocate

rangeTable = rangesExtractFromMassSpec(spec);
pos = posAllocateRange(pos, rangeTable, 'decomposed');

Step 8: Composition

detEff = cfg.reconstruction.detectionEfficiency;
conc = posCalculateConcentrationSimple(pos, detEff, {'unranged'}, '', 'mode', 'atomic');
unc = concentrationUncertainty(conc.counts);
disp(conc);

Step 9: Visualization

scatterPlotPosWidget(pos, colorScheme);

Step 10: Report

Present a summary:

Dataset: file name, atom count
Instrument: name and detection efficiency used
Composition table with uncertainties
Suggest next steps: ROI analysis, proxigram, cluster analysis