[out3Plot]: add functionality for assets

[awirb]

PR Checklist

Please confirm before requesting review:

• I ran pytest locally without fails
• I added/updated tests where needed
• I updated documentation where needed

Confirm before the final merge/rebase into master

• Commits are sensibly squashed and rebased onto latest master
• Standardtest run without difference (with recompiled cython code)

[qltysh]

Analysis for project AvaFrame

❌ 1 blocking issue (1 total)

Tool	Category	Rule	Count
black	Style	Incorrect formatting, autoformat by running qlty fmt.	1	❌

@qltysh one-click actions:

• Auto-fix formatting (qlty fmt && git push)

[qltysh]

Coverage Impact

⬆️ Merging this pull request will increase total coverage on master by 0.11%.

Modified Components (1)

Rating	Component	% Diff
	com1DFA	94.4%

Modified Files with Diff Coverage (5)

Rating	File	% Diff	Uncovered Line #s
	avaframe/in1Data/getInput.py	76.0%	1300-1302, 1304-1306
	avaframe/com1DFA/com1DFA.py	100.0%
	avaframe/com1DFA/particleTools.py	96.9%	1054, 1152
	avaframe/com1DFA/deriveParameterSet.py	87.0%	1090-1095
	avaframe/in3Utils/geoTrans.py	56.5%	264, 268, 384...
	Total	80.6%

🤖 Increase coverage with AI coding...

In the `addInfra` branch, add test coverage for this new code:

- `avaframe/com1DFA/deriveParameterSet.py` -- Line 1090-1095
- `avaframe/com1DFA/particleTools.py` -- Lines 1054 and 1152
- `avaframe/in1Data/getInput.py` -- Lines 1300-1302 and 1304-1306
- `avaframe/in3Utils/geoTrans.py` -- Lines 264, 268, 384, and 2200-2226

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• Diff Coverage: Coverage for added or modified lines of code (excludes deleted files). Learn more.

• Total Coverage: Coverage for the whole repository, calculated as the sum of all File Coverage. Learn more.

• File Coverage: Covered Lines divided by Covered Lines plus Missed Lines. (Excludes non-executable lines including blank lines and comments.)

  • Indirect Changes: Changes to File Coverage for files that were not modified in this PR. Learn more.

[fso42]

Wrong PR, sorry

[fso42]

  def createAssetsRasterFromParticleLocations(particlesTimeArrays, dem, uniqueAssets,   
assetsValues):                                                                          
      """create a raster indicating particle trajectories colorcoded with assets        
classes,                                                                                
      highest overrides lower classes"""                                                
                                                                                        
      nTime, nPart = particlesTimeArrays["ID"].shape                                    
      particlesTimeArrays["assetsValue"] = np.full((nTime, nPart), np.nan)              
      particleAssets = np.full((dem["header"]["nrows"], dem["header"]["ncols"]),        
np.nan)                                                                                 
                                                                                        
      # Process classes from low to high so higher classes naturally override lower     
ones                                                                                    
      for l in uniqueAssets:                                                            
          assetCells = np.asarray(assetsValues["value_%d" % l])                         
          inAsset = np.isin(particlesTimeArrays["inCellDEM"], assetCells)               
                                                                                        
          for pId in range(nPart):                                                      
              hitTimes = np.where(inAsset[:, pId])[0]                                   
              if len(hitTimes) == 0:                                                    
                  continue                                                              
                                                                                        
              # Mark particle trajectory up to the last time it hit this asset class    
              mMax = hitTimes[-1]                                                       
              particlesTimeArrays["assetsValue"][:mMax, pId] = l                        
                                                                                        
              indX = particlesTimeArrays["indXDEM"][: mMax + 1, pId].astype(int)        
              indY = particlesTimeArrays["indYDEM"][: mMax + 1, pId].astype(int)        
              # Only overwrite cells not already set to a higher class                  
              particleAssets[indY, indX] = np.where(                                    
                  particleAssets[indY, indX] >= l, particleAssets[indY, indX], l        
              )                                                                         
                                                                                        
      return particleAssets, particlesTimeArrays                                        

[ahuber-bfw]

Schaut gut aus und läuft für ein paar Testbeispiele bei mir.

Hier noch ein paar Kommentare zu Details, die aber auch noch in einem separaten, kleineren PR noch angepasst/angeschaut werden könnten.