mlreco.post_processing.analysis.michel_reconstruction_2d module¶
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mlreco.post_processing.analysis.michel_reconstruction_2d.is_at_edge(cluster_coords, point_coords, one_pixel=1, radius=10.0)[source]¶ Determines whether the point with coordinates point_coords is at the edge of the cluster with coordinates cluster_coords. Removes a disc of radius radius around that point, runs DBSCAN and checks whether there is still only 1 cluster.
Assumes: that DBSCAN run on cluster_coords will only find 1 cluster.
cluster_coords: np.array (N, data_dim) point_coords: np.array (1, data_dim) or (data_dim,)
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mlreco.post_processing.analysis.michel_reconstruction_2d.michel_reconstruction_2d(cfg, data_blob, res, logdir, iteration)[source]¶ Very simple algorithm to reconstruct Michel clusters from UResNet semantic segmentation output.
- Parameters
data_blob (dict) – Input dictionary returned by iotools
res (dict) – Results from the network, dictionary using analysis_keys
cfg (dict) – Configuration
idx (int) – Iteration number
Notes
Assumes 2D
Requires the following analysis keys: - segmentation output of UResNet Requires the following input keys: - input_data - segment_label - particles_label to get detailed information such as energy. - clusters_label from cluster3d_mcst for true clusters informations
- Output
Writes 2 CSV files
- `michel_reconstruction-*`
- `michel_reconstruction2-*`