Friday, March 31, 2017

Exporting Revit Revision Cloud Data to Excel with Dynamo & Python

From the andrewking(me) website:

Extract revision cloud data from Revit using Dynamo and Python.

OOTB Revit Revision Schedule Limitations

Revit can schedule the following revision parameters in a title block family. However, instance parameters (Mark and Comments), sheet numbers, and sheet names are not supported out-of-the-box.
  • Revision Sequence
  • Revision Number
  • Revision Date
  • Revision Description
  • Issued to
  • Issued by

Expanding on OOTB Functionality

Dynamo and Python can expand on OOTB functionality to generate an itemized list of revisions including previously unavailable instance parameters, sheet numbers, and sheet names. The definition and code in this post will extract the following.
  • Sheet Number
  • Sheet Name
  • Revision Number
  • Revision Date
  • Revision Description
  • Mark (For example, RFI 001) [Instance Parameter]
  • Comments [Instance Parameter]

Dynamo Definition: Part 1

Part 1 of the definition collects all revision clouds and sheets within the active document and sends that information to a Python node.

# Python Code for Dynamo
# Input: Revision Clouds, Sheets
# Output: Matching Sheets, Matching Revision Clouds, Referencing Views
# Version 0.6
# Coded by Andrew King
# 2016-02-06 Version 0.1
# Hello World
# 2016-02-08 Version 0.2
# Output matchingrevisionclouds from Python node to avoid hidden element mismatch.
# 2016-02-14 Version 0.4
# Expanded Python code to output every instance of a revision cloud in every instance of a legend.
# 2016-02-16 Version 0.5
# Restructured code to reduce number of cycles.
# Eliminated the need for a separate legend/dependency path.
# Boolean selector for Revisions on Sheets/Revisions in Views on Sheets.
# Revit 2014 compatibility.
# 2016-02-25 Version 0.6
# Added category filter to improve FilteredElementCollector performace.

import clr
from Autodesk.DesignScript.Geometry import *

# Import RevitAPI
import Autodesk
from Autodesk.Revit.DB import *

# Import DocumentManager and TransactionManager
import RevitServices
from RevitServices.Persistence import DocumentManager
from RevitServices.Transactions import TransactionManager

# Assign input to the IN variables.
revisioncloudinput = UnwrapElement(IN[0])
sheetinput = UnwrapElement(IN[1])
revisionsonsheets = IN[2]
revisionsinviewsonsheets = IN[3]

matchingsheets = []
matchingrevisionclouds = []
referencingviews = []

# Look for revision clouds on sheets.
if revisionsonsheets == True:
  for sheet in sheetinput:
    for sheetelement in FilteredElementCollector(DocumentManager.Instance.CurrentDBDocument, sheet.Id).OfCategory(BuiltInCategory.OST_RevisionClouds):
      for revisioncloud in revisioncloudinput:
        if sheetelement.Id == revisioncloud.Id:

# Look for revision clouds in views on sheets.
if revisionsinviewsonsheets == True:
  for sheet in sheetinput:
    if DocumentManager.Instance.CurrentUIApplication.Application.VersionName == "Autodesk Revit 2014":
      for viewport in sheet.GetAllViewports():
        for view in [DocumentManager.Instance.CurrentDBDocument.GetElement(viewport)]:
          for viewid in [DocumentManager.Instance.CurrentDBDocument.GetElement(view.ViewId)]:
            for viewelement in FilteredElementCollector(DocumentManager.Instance.CurrentDBDocument, viewid.Id).OfCategory(BuiltInCategory.OST_RevisionClouds):
              for revisioncloud in revisioncloudinput:
                if viewelement.Id == revisioncloud.Id:
      for viewport in sheet.GetAllPlacedViews():
        for view in [DocumentManager.Instance.CurrentDBDocument.GetElement(viewport)]:
          for viewelement in FilteredElementCollector(DocumentManager.Instance.CurrentDBDocument, view.Id).OfCategory(BuiltInCategory.OST_RevisionClouds):
            for revisioncloud in revisioncloudinput:
              if viewelement.Id == revisioncloud.Id:

# Assign output to the OUT variable.
OUT = matchingsheets, matchingrevisionclouds, referencingviews

Notes on the Python code:
  1. Element collection was optimized to run as fast as possible using .OfCategory(). On a large benchmark project (1400+ revision clouds), the process currently takes around 4 minutes to complete.
  2. Revisions on Sheets and Revisions in Views on Sheets can be enabled separately on IN[2] and IN[3]. Consider having your team place all revisions on sheets and turn off Revisions in Views on Sheets to increase speed.
  3. The code above was designed to collect only visible revisions. If the revision cloud is not visible on the sheet (printed set), it will not be collected.
  4. The Sheet Issues/Revisions dialog in Revit can pre-filter your output by revision sequence (delta). Sequences set to Cloud and Tag or Tag will export. Sequences set to None are not visible and will not export.

Dynamo Definition: Part 2

Part 2 of the definition extracts the relevant parameter values, builds an itemized list, sorts the list, and writes it to Excel.

Complete Graph

Simple, efficient output of Revit revision cloud data.

There's more information available on the andrewking(me) website.

1 comment:

  1. This looks like a great tool and one of the more indepth tutorials! Thanks!

    One issue... I'm testing this out for a current project and it seems to be taking way too long (or possibly crashing) the RVT is 550MB and about 500 sheets with 500 revisions. Let it ran for about 20 minutes and no bueno yet. I tried adjusting the Python to only search for sheet revisions (which we dont have many) and still no luck. Any suggestions?