| name | apt-profile-1d |
| description | Compute and plot a 1D concentration profile (composition vs. distance) from ranged/allocated atom probe data by voxelising along one axis. Use when the user wants a concentration profile, line profile, or composition-along-z plot. |
| argument-hint | [bin-nm] |
Build a 1D concentration profile by slicing the reconstructed tip into bins along one distance axis and computing the composition of each slice. Use the MATLAB MCP server.
Prerequisites
The MATLAB workspace must contain:
pos — ranged/allocated atom probe data with an ion/atom column (from /apt-composition)colorScheme — ion color table (from /apt-setup)
If pos has no ion column, tell the user to run /apt-composition first (ranging + posAllocateRange). If colorScheme is missing, run /apt-setup.
Steps
1. Choose the distance axis and create bin vectors
dist = pos.z; % distance variable to bin along (z is the default profile direction)
bin = 1; % bin width in nm
mode = 'distance';
[binCenters, binEdges] = binVectorsFromDistance(dist, bin, mode);
bin is the slice thickness in nm. Default 1 nm; use 0.25 nm near interfaces, 0.5 nm for a typical profile. Parse $ARGUMENTS for a bin width if the user supplied one. To profile along a different direction use pos.x or pos.y, or supply an ROI/interface distance from /apt-roi. There is also a 'count' mode (equal atoms per bin, e.g. bin = 390000) used mainly for proxigrams — only use it if the user explicitly asks.
2. Voxelise the data
vox = posNdBin(pos, dist, binEdges); % cell array: one pos subset per slice
The last slice is usually thinner / has fewer atoms than the rest — this is expected.
3. Define the concentration kernel (set detection efficiency)
detEff = 37; % detector efficiency, percent or decimal both accepted
excludeListConc = {'unranged'}; % species ignored in the composition, optional
volumeName = ''; % volume label, optional
concentrationKernel = @(pos) posCalculateConcentrationSimple(pos, detEff, excludeListConc, volumeName);
The detection efficiency is critical for correct composition and must match the instrument: 37 (LEAP 4000X HR), 52 (LEAP 5000 XR), 80 (LEAP 5000 XS), 50 (EIKOS). If the user has not confirmed their instrument, ask before proceeding. Read it from config if available: cfg = APTConfig.getInstance(); detEff = cfg.reconstruction.detectionEfficiency*100;.
4. Apply the kernel to every slice
conc1D = binApplyConcentrationKernel(vox, binCenters, concentrationKernel, {'nm'});
This returns one table holding the concentration, counts and variance per slice, tagged with the bin-center distance in nm. Composition is reported with counting-based variance, so each point carries a statistical uncertainty (see concentrationUncertainty below).
5. Plot the profile
excludeListPlot = {'unranged'}; % species hidden from the plot, optional
[p, ax, f] = concentrationProfilePlot(conc1D(conc1D.format=='concentration', :), excludeListPlot, colorScheme);
This plots concentration vs. distance for all ranged species (up to 4 volumes can be overlaid). ax is left in the workspace for further tweaking.
6. (Optional) Extract values for one species
conc = cellfun(concentrationKernel, vox, 'UniformOutput', false);
concWantedSpecies = cellfun(@(x) x.Fe(x.format == 'concentration'), conc); % replace Fe with element/complex ion
concWantedSpecies is a column vector of the fraction of that species per slice, aligned with binCenters{1}.
7. (Optional) Counting uncertainty
unc = concentrationUncertainty(conc1D.counts);
Reports the statistical (counting) uncertainty on the profile values.
Report
Tell the user the bin width used, the distance axis, and the detection efficiency applied, and that a 1D profile figure was created. Mention that each point has counting uncertainty (smaller bins = noisier profiles). Suggest:
/apt-roi to define a profile direction or sub-region (e.g. across an interface) before re-running/apt-profile-2d for a 2D concentration map- Export:
writetable(conc1D, 'profile_1d.csv') or save the figure with exportgraphics(ax, 'profile_1d.png')
