Collection: Bulk Write, Visualization & Output

Bulk Write

.SetParam(name, value, unit)

Sets a parameter on every element in the collection inside a single transaction. Can optionally take a unit parameter to convert numeric values from the specified unit before setting. Returns the collection (chainable).

// Mark all unreviewed doors
GetElements("Doors")
    .WhereParam("Comments", "")
    .SetParam("Comments", "Pending Review")

// Set top constraint and offset with unit conversion in a single chain
GetElements<Wall>()
    .WhereParam("Base Constraint", "Level 01")
    .SetParam("Top Constraint", "Level 02")
    .SetParam("Top Offset", -150, "cm")

.SetParam(name, valueFactory) — Dynamic

Sets a parameter on every element using a function. The function receives the element and returns the value.

// Sequential renumbering
int i = 1;
GetElements("Doors")
    .WhereParam("Level", "Level 2")
    .OrderByParam("Mark")
    .SetParam("Mark", e => $"D2-{i++:000}")

How Transactions Work

Unlike a manual foreach loop (where each SetVal creates its own transaction), collection methods like .SetParam, .Delete, .Hide, .Unhide, and .Isolate already wrap all elements in a single Tx.Transact call. Each individual write inside sees the active transaction (IsModifiable == true) and skips creating its own. Result: one undo step, no Transact() wrapper needed for fluent chains.

For a full breakdown of all four transaction scenarios (including when you DO need Transact), see Transaction Behavior.

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Data Science & Analytics

.ToNotebook(string notebookName)

The ultimate bridge to Pandas and AI analysis. Takes any collection (elements or anonymous objects), serializes it to a highly compressed JSON file, and automatically generates and opens a Jupyter Notebook in VS Code to analyze the data.

// Export complex scheduling data straight to a new Jupyter Notebook
GetElements<Room>()
    .Select(r => new {
        Number = r.GetStr("Number"),
        Name = r.Name,
        Level = r.GetStr("Level"),
        Area = r.Area.OutputUnit("m2", 2)
    })
    .ToNotebook("Room_Analysis");

How it works (Scratch & Save):

1. Generates data.json and <notebookName>.ipynb in a temporary scratch folder.
2. The notebook is pre-populated with Python code specifically mapped to load your data.json straight into a pandas.DataFrame.
3. Auto-launches VS Code. Click "Save As" in VS Code to keep the analysis permanently.

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Visualization

These work on any IEnumerable<T>.

Method	Description
.Table()	Renders as an interactive data grid. Automatically extracts both Instance and Type parameters into columns.
.BarChart() / .BarGraph()	Bar chart (needs name + value properties)
.PieChart() / .PieGraph()	Pie chart
.LineChart() / .LineGraph()	Line chart
.Show()	Pro Output: Smart data grid + automated 3D geometric focus

// Count doors per level as pie chart
GetElements("Doors")
    .GroupByParam("Level")
    .Select(g => new { name = ((dynamic)g).Group, value = ((dynamic)g).Count })
    .PieChart()

// Any projection works directly
GetElements("Walls")
    .GroupByParam("Base Constraint", "Length", "m")
    .Table()

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Collection: Revit UI Actions

All return IEnumerable<T> (chainable). All batch into a single transaction automatically.

Method	Description
.Select()	Selects all elements in the Revit UI
.Zoom()	Zooms the active view to fit the elements
.Isolate()	Temporarily isolates in the active view
.Hide()	Hides all elements in the active view
.Unhide()	Unhides all elements in the active view
.Delete()	BIM-Smart Delete (skips Pinned, Curtain Panels, Curtain-hosted doors)
.Peek()	Forensic audit of every element in the collection

// Find and isolate all walls without a Mark
GetElements<Wall>()
    .WhereParam("Mark", "")
    .Isolate()

// Find and delete temporary elements
GetElements("Generic Models")
    .WhereMatches("TEMP")
    .Delete()

BIM-Smart Delete

The .Delete() extension method automatically skips Pinned elements, Curtain Wall Panels, and hosted Curtain Doors to prevent Revit exceptions and model corruption.