The transformation matrix will be logged by the Logger, so I think you can extract the required information from the feature log if you sneak a peek at log.

# PythonCaller Script import fmeobjects def collectLog(sevirity, message): g_logger.append(message) class PreProcessor(object): def __init__(self): fmeobjects.FMELogFile().setCallBack(collectLog) def input(self,feature): global g_logger g_logger = [] self.pyoutput(feature)

# PythonCaller_2 Script class PostProcessor(object): def input(self, feature): feature.setAttribute("_log", "\n".join(g_logger)) self.pyoutput(feature)

The attribute named "_log" stores a text string like this.You can then parse this text to extract the transformation matrix.

Logger: Feature is: +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Feature Type: `Logger_LOGGED' Attribute(string): `fme_basename' has value `PointCloudCreator' Attribute(string): `fme_feature_type' has value `PointCloudCreator' Attribute(string): `fme_geometry' has value `fme_polygon' Attribute(string): `fme_type' has value `fme_point_cloud' Coordinate System: `' Geometry Type: IFMEPointCloud Transformation Matrix: |0.86602540378443904 -0.49999999999999928 0 30| |0.49999999999999928 0.86602540378443904 0 50| |0 0 1 0 | |0 0 0 1 | Global Extent Min: (-31.999999999999908,50,0) Global Extent Max: (137.38715006927043,219.38715006927035,20) Number of points: 15625 Components: x (Real64) y (Real64) z (Real64) ===========================================================================

[Update] This script for the PythonCaller_2 extracts individual elements of the transformation matrix from the log and stores them as feature attributes.

# PythonCaller_2 Script [Update] class PostProcessor(object): def input(self, feature): # Extract rows containing transformation matrix from the log.matrix, flag = [], False for r in [s.strip() for s in g_logger]: if not flag and r.startswith('Transformation Matrix:'): flag = True elif flag and len(matrix) < 4: matrix.append(r.strip('|')) # Extract every element of the matrix.if len(matrix) == 4: r = [s.split() for s in matrix] attrs = { "_A": r[0][0], "_B": r[0][1], "_C": r[0][2], "_D": r[0][3], "_E": r[1][0], "_F": r[1][1], "_G": r[1][2], "_H": r[1][3], "_I": r[2][0], "_J": r[2][1], "_K": r[2][2], "_L": r[2][3], "_M": r[3][0], "_N": r[3][1], "_O": r[3][2], "_P": r[3][3], } for k in attrs.keys(): feature.setAttribute(k, float(attrs[k])) feature.setAttribute("_log", "\n".join(g_logger)) # Optional feature.setAttribute("_matrix{}", matrix) # Optional self.pyoutput(feature)

Hi@ryanw

It might be something along the line of forcing the RevIt feature attributes above the meta-attribute line.
Please seeexpose custom attrs from RevItusing the AttributeExposer and i think there is maybe traits you can express using GeomPropertyExposer or to scratch at some of these.

and lastly there alot of clever features hiding in PointCloudExpressionEvaluator / PointCloudPropertyExtractor see if you can force the component into an attribute using one of these .

-S