xmipp3.protocols.protocol_classes_2d_mapping module

class xmipp3.protocols.protocol_classes_2d_mapping.ScatterImageMarker(img_paths, pos, ids, occupancy=None, prevsel=None)[source]

Bases: object

imScatter(image, x, y, imid, edge_color=None)[source]
initializePlot()[source]
is_window_closed(event)[source]
readImages(img_paths)[source]
setPickCallback()[source]
setRectangleSelector()[source]
setSelectionButtons()[source]
setSliderCallback()[source]
updatePatch(ind)[source]
class xmipp3.protocols.protocol_classes_2d_mapping.XmippProtCL2DMap(**args)[source]

Bases: ProtAnalysis2D

Create a low dimensional mapping from a SetOfClasses2D with interactive selection of classes. Use mouse left-click to select/deselect classes individually or mouse right-click to select/deselect several classes.

AI Generated:

This protocol helps you explore and curate your 2D classes visually. It takes a set of 2D class averages and places them in a 2D map where similar classes appear close to each other. You can then interactively select the classes you want to keep and export them as a new set.

Think of it as a visual “landscape” of your 2D classes that helps you:

  • Identify groups of similar views

  • Detect outliers or junk classes

  • Separate different conformations or compositions

  • Select coherent subsets for downstream processing

When should you use this?

You typically use this protocol after running 2D classification (e.g., CL2D, Relion 2D, etc.) when:

  • You have many classes and it’s hard to interpret them one by one.

  • You suspect the presence of heterogeneous conformations.

  • You want to quickly identify junk clusters or rare views.

  • You want to define a clean subset of classes for 3D refinement.

  • Instead of manually browsing dozens or hundreds of class averages in a

grid, this protocol shows them in a meaningful spatial organization.

What does the map represent?

Each point in the map corresponds to one 2D class average.

Classes that look similar (same view, similar signal) tend to appear near each other.

Classes that are very different (different view, contamination, broken particles) tend to be far apart.

The map can optionally color classes according to their occupancy (how many particles they contain).

This makes it much easier to see structure in your classification results.

How to use the interactive selection

If interactive mode is enabled (default):

  • A window opens showing the 2D map.

  • Each class average appears as a small image at its position.

You can: - Left-click on a class to select or deselect it. - Right-click and drag to select/deselect multiple classes at once. - Use the zoom slider to change thumbnail size. - Use Select all / Select none buttons. - Selected classes are highlighted in green. - When you close the window, you will be asked whether you want to save the selection. If you confirm, a new set of 2D classes is created containing only the selected classes.

If interactive selection is disabled, all classes are selected automatically.

Choosing the mapping method

You can choose different dimensionality reduction methods (PCA, Diffusion Maps, etc.) and distance metrics (Euclidean or Correlation).

For most users:

PCA + Correlation is a good default.

If the map looks messy or unstructured, you can try another method (e.g., Diffusion Maps) to see if separation improves.

You don’t need to understand the mathematics to use this effectively—just check whether the map produces meaningful grouping.

What do you get as output?

You get a new SetOfClasses2D containing only the classes you selected.

This new set can be used for:

  • Exporting selected classes

  • Creating a clean subset of particles

  • Feeding into 3D reconstruction

  • Further classification steps

The original classes remain untouched.

Practical workflow example

Run 2D classification.

Use this protocol to map the classes.

In the map:

  • Select the large, coherent cluster of good-looking classes.

  • Deselect obvious junk or tiny, isolated classes.

  • Export the selection.

  • Use the selected classes (or their particles) for 3D refinement.

This approach is especially powerful when your dataset contains subtle heterogeneity that is not obvious from simple grid browsing.

Why this is useful

Manual selection of classes can be subjective and tedious. The 2D map:

  • Gives a global overview of your classification.

  • Makes outliers immediately visible.

  • Helps detect continuous variability.

  • Speeds up dataset cleaning.

In short, it turns class inspection from a linear browsing task into a structured, intuitive visual decision process.

CLASSES_MD = 'classes.xmd'
computeMappingStep()[source]
convertStep()[source]
distances = ['Euclidean', 'Correlation']
interactiveSelStep()[source]
isSelected(cls)[source]
red_methods = ['PCA', 'LTSA', 'DM', 'LLTSA', 'LPP', 'kPCA', 'pPCA', 'LE', 'HLLE', 'SPE', 'NPE']