Data Traversal - Speckle Docs

Overview

Data traversal is the systematic process of navigating Speckle’s object graphs to extract data, find specific objects, and preserve hierarchical context. Understanding traversal is fundamental to working with Speckle data effectively.

Prerequisites: This guide assumes familiarity with the Base class and data types. If you’re new to Speckle objects, start with those concepts first.

Speckle objects form hierarchical graphs - tree-like structures where objects contain other objects, arrays of objects, and references to objects stored elsewhere. Unlike simple flat lists, these graphs require strategic navigation to:

Extract all objects of a specific type (e.g., all walls, all meshes)
Preserve parent-child relationships and organizational context
Filter data based on properties or structure
Build analysis pipelines that understand object hierarchies

Key Concepts:

Graph Structure: Objects are connected through properties (parent → child relationships)
Traversal: The process of visiting every object in a graph systematically
Filtering: Selecting only objects that match specific criteria during traversal
Flattening: Converting a hierarchical graph into a flat list of objects

from specklepy.api import operations
from specklepy.objects import Base

# Receive an object graph
obj = operations.receive(object_id, remote_transport=transport)

# Navigate the graph
for name in obj.get_member_names():
    value = getattr(obj, name)
    print(f"{name}: {type(value).__name__}")

Understanding Object Graphs

Speckle data comes in different structural complexities depending on the source and use case. Understanding these patterns helps you choose the right traversal strategy.

Simple Graph (Custom Data)

# Linear structure
project
├── name: "Building A"
├── date: "2024-01-15"
└── measurements: [Point, Point, Point]

Medium Graph (Simple Model)

# 2-3 levels deep
collection
├── name: "Walls"
├── items: [
│   ├── Mesh (displayValue)
│   │   ├── vertices: [...]
│   │   └── properties: {...}
│   └── Mesh
│       ├── vertices: [...]
│       └── properties: {...}
└── count: 2

Complex Graph (BIM Data)

# 3+ levels with references (v3 DataObject)
DataObject
├── name: "Basic Wall - 200mm"
├── speckle_type: "Objects.Data.DataObject"
├── properties
│   ├── category: "Walls"
│   ├── family: "Basic Wall"
│   ├── type: "200mm"
│   ├── volume: 15.5
│   ├── area: 45.0
│   ├── level: "Level 1"
│   └── materials
│       ├── Concrete: {volume: 10.2, area: 30.5}
│       └── Steel: {volume: 0.5, area: 2.1}
├── displayValue: [Mesh, Mesh]  # List of geometry
└── applicationId: "guid-123..."

Why Traversal Matters

Different data structures require different traversal strategies:

Data Type	Structure	Best Strategy	Why
Custom Data	Flat or simple	Direct access	Properties are known
Geometry Collections	Medium depth	Type-based traversal	Find specific geometry types
BIM Models	Deep hierarchy	Property/displayValue filtering	BIM semantics in properties
Large Models	Very deep	elements[] traversal	Follows organizational structure

Common Traversal Goals

Find specific objects - “Get all walls” or “Find all meshes”
Extract properties - “List all volumes” or “Get material quantities”
Flatten hierarchy - “Convert nested structure to flat list”
Group objects - “Organize by level” or “Group by category”
Analyze relationships - “Which objects are on Level 1?”

Traversal Strategies Explained

For v3 Data: The most effective strategies are Property-Filtered, displayValue-Based, and elements[] Hierarchy traversal. Type-filtered traversal using speckle_type is primarily useful for geometry primitives (Mesh, Point, Line) but not for BIM objects, which are now generic DataObject instances with semantics in their properties.

Strategy 1: Property-Filtered Traversal ⭐ Recommended for v3

When to use: Working with BIM data from connectors (Revit, Rhino, ArchiCAD, etc.). How it works:

Traverse recursively
Check if object has a properties dictionary
Filter based on property values (e.g., category == "Walls")
Collect matching objects

Best for:

v3 BIM objects (DataObject)
Finding objects by category, family, type
Filtering by quantities or metadata
Most common pattern for modern Speckle data

Example use cases:

“Find all walls” → filter by properties.category == "Walls"
“Get steel beams” → filter by properties.category == "Structural Framing" AND properties.material == "Steel"
“Objects on Level 1” → filter by properties.level == "Level 1"

Strategy 2: displayValue-Based Traversal ⭐ Recommended for v3

When to use: You only want atomic, viewer-selectable objects. How it works:

Traverse recursively
Collect only objects with a displayValue property
These are the objects that appear as selectable items in the viewer

Best for:

Getting viewer-clickable objects
Counting actual BIM elements (not containers/groups)
Extracting geometry for visualization
Distinguishing atomic objects from organizational containers

Key insight: Objects without displayValue are typically containers, not atomic elements.

Strategy 3: elements[] Hierarchy Traversal

When to use: Objects are organized in a logical hierarchy using elements[] arrays.

The elements property is Speckle’s standardized convention for organizing hierarchical data. See The elements Convention for a detailed explanation of this pattern.

How it works:

Check if object has an elements property (list)
Recursively traverse only the elements array
Optionally filter during traversal
Ignores other properties

Best for:

Collections and Groups
Organized BIM hierarchies (Level → Room → Elements)
When you only care about the logical organization, not nested geometry

Advantage: Faster than full traversal, follows intentional structure

Strategy 4: Full Recursive Traversal

When to use: You need to visit EVERY object in the graph, regardless of type or depth. How it works:

Start at root object
Visit each property on the object
For each property that’s a Base object, list, or dict, recurse into it
Continue until all branches are explored

Best for:

Finding rare objects that could be anywhere
Building complete inventories
Ensuring nothing is missed

Cost: Slowest, visits everything

Strategy 5: Type-Filtered Traversal (Limited Use in v3)

When to use: Looking for geometry primitives (Mesh, Point, Line, etc.). How it works:

Traverse recursively
Check each object’s speckle_type property
Collect only objects matching the target type
Continue traversing to find all instances

Best for:

Finding geometry objects (Mesh, Line, Point, Arc, Circle, etc.)
Pure geometry workflows
Legacy v2 objects with typed BIM classes

Limited Use in v3 BIM Data: Most BIM objects from connectors are now DataObject instances with speckle_type = "Objects.Data.DataObject". Filtering by this type returns ALL BIM objects without differentiation. For BIM data, use Property-Filtered Traversal (Strategy 1) instead to filter by category, family, type, etc.

Choosing the Right Pattern

Use this decision tree for v3 data:

Working with BIM data from connectors?
├─ YES → Use Property-Filtered Traversal (Strategy 1)
│        Filter by: category, family, type, level, material, etc.
└─ NO → Continue...

Need only viewer-visible objects?
├─ YES → Use displayValue-Based Traversal (Strategy 2)
│        Gets atomic, selectable objects
└─ NO → Continue...

Following organizational hierarchy (Collections, Groups)?
├─ YES → Use elements[] Hierarchy Traversal (Strategy 3)
│        Faster, follows intentional structure
└─ NO → Continue...

Looking for geometry primitives (Mesh, Point, Line)?
├─ YES → Use type-filtered traversal (Pattern 1)
└─ NO → Continue...

Are you looking for v3 BIM objects (walls, columns, etc.)?
├─ YES → Use property-filtered traversal (Pattern 2)
│        or displayValue filtering (Pattern 3b)
└─ NO → Continue...

Is data organized in elements[] arrays?
├─ YES → Use elements[] traversal (Pattern 3a)
└─ NO → Use full recursive traversal

Basic Traversal Techniques

Technique 1: Direct Property Access

When to use: You know the exact structure and property names. Advantages: Fastest, most efficient, no unnecessary traversal.

from specklepy.api import operations

obj = operations.receive(object_id, remote_transport=transport)

# Direct access (if you know the structure)
if hasattr(obj, "name"):
    print(f"Name: {obj.name}")

if hasattr(obj, "measurements"):
    for measurement in obj.measurements:
        print(f"Point: ({measurement.x}, {measurement.y}, {measurement.z})")

Technique 2: Iterate All Members

When you want to explore:

from specklepy.objects import Base

def print_members(obj, indent=0):
    """Print all members of an object."""
    prefix = "  " * indent

    if isinstance(obj, Base):
        print(f"{prefix}{obj.speckle_type}")

        for name in obj.get_member_names():
            if name.startswith("_"):
                continue

            value = getattr(obj, name, None)
            print(f"{prefix}  {name}:")
            print_members(value, indent + 2)

    elif isinstance(obj, list):
        print(f"{prefix}[List: {len(obj)} items]")
        if obj:
            print_members(obj[0], indent + 1)  # Show first item

    elif isinstance(obj, dict):
        print(f"{prefix}{{Dict: {len(obj)} keys}}")
        for key, value in list(obj.items())[:3]:  # Show first 3
            print(f"{prefix}  {key}:")
            print_members(value, indent + 2)

    else:
        # Primitive value
        value_str = str(obj)[:50]  # Limit length
        print(f"{prefix}{value_str}")

# Use it
print_members(obj)

Technique 3: Recursive Search

Find all objects matching criteria:

from specklepy.objects import Base

def find_all(root, predicate):
    """Find all objects matching a predicate function."""
    results = []

    def traverse(obj):
        if isinstance(obj, Base):
            # Check if this object matches
            if predicate(obj):
                results.append(obj)

            # Traverse children
            for name in obj.get_member_names():
                if not name.startswith("_"):
                    value = getattr(obj, name, None)
                    traverse(value)

        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

        elif isinstance(obj, dict):
            for value in obj.values():
                traverse(value)

    traverse(root)
    return results

# Use it: find all meshes
from specklepy.objects.geometry import Mesh

meshes = find_all(obj, lambda x: isinstance(x, Mesh))
print(f"Found {len(meshes)} meshes")

# Find all walls by checking properties (v3 pattern)
from specklepy.objects.data_objects import DataObject
walls = find_all(obj, lambda x: isinstance(x, DataObject) and x.properties.get("category") == "Walls")
print(f"Found {len(walls)} walls")

Pattern 1: Find by Property ⭐ Primary Pattern for v3

Purpose: Search for objects based on their properties (the standard v3 pattern). When to use:

Working with BIM data from any connector (Revit, Rhino, ArchiCAD, etc.)
Need to find objects by category, family, type, level, material
Filtering by quantities, parameters, or metadata
This is the primary pattern for modern Speckle data

How it works:

Recursively traverse all objects
Check if object has properties dictionary
Filter based on property values
Collect matches

Purpose: Find objects based on their properties dictionary values (v3 BIM pattern). When to use:

Working with v3 BIM data (DataObject)
Need to find objects by category (“Walls”, “Columns”, “Floors”)
Filtering by BIM properties (loadBearing, fireRating, family, type)
Need to find objects with specific quantities or metadata

Advantages:

Works with v3 object model
Can filter on any property value
Can combine multiple property conditions
Reflects how BIM data is actually organized

How it works:

Traverse all objects
Check if object is a DataObject
Access the properties dictionary
Filter based on property values
Collect matches

from specklepy.objects import Base

def find_by_property(root, property_name, property_value=None):
    """Find objects with a specific property (optionally with value)."""
    results = []

    def traverse(obj):
        if isinstance(obj, Base):
            # Check if property exists
            if hasattr(obj, property_name):
                value = getattr(obj, property_name)

                # If checking value too
                if property_value is None or value == property_value:
                    results.append(obj)

            # Check properties dictionary
            if hasattr(obj, "properties") and isinstance(obj.properties, dict):
                if property_name in obj.properties:
                    value = obj.properties[property_name]
                    if property_value is None or value == property_value:
                        results.append(obj)

            # Traverse children
            for name in obj.get_member_names():
                if not name.startswith("_"):
                    child = getattr(obj, name, None)
                    traverse(child)

        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

    traverse(root)
    return results

# Use it
steel_elements = find_by_property(obj, "Material", "Steel")
exterior_walls = find_by_property(obj, "Function", "Exterior")
all_with_volume = find_by_property(obj, "volume")  # Any object with volume

print(f"Steel elements: {len(steel_elements)}")
print(f"Exterior walls: {len(exterior_walls)}")
print(f"Objects with volume: {len(all_with_volume)}")

Pattern 2: Find by Type (Geometry Only)

Purpose: Search for geometry primitives by speckle_type. When to use:

Looking for geometry objects (Mesh, Point, Line, Arc, Circle, etc.)
Pure geometry workflows without BIM semantics
Need to find all instances of a specific geometry class

When NOT to use:

❌ BIM data from connectors (use Pattern 1 - Find by Property instead)
❌ Need to differentiate walls from columns (use properties)
❌ Any data where semantics are in the properties dictionary

Limited Use for BIM Data: In v3, BIM objects are DataObject instances with speckle_type = "Objects.Data.DataObject". All walls, columns, beams, etc. have the same type. To find specific BIM elements, use Pattern 1 (Find by Property) to filter by category, family, type, etc.

from specklepy.objects import Base

def find_by_type(root, target_type):
    """Find all objects with a specific speckle_type (for geometry objects)."""
    results = []

    def traverse(obj):
        if isinstance(obj, Base):
            if obj.speckle_type == target_type:
                results.append(obj)

            for name in obj.get_member_names():
                if not name.startswith("_"):
                    value = getattr(obj, name, None)
                    traverse(value)

        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

        elif isinstance(obj, dict):
            for value in obj.values():
                traverse(value)

    traverse(root)
    return results

# Good use cases - geometry primitives
points = find_by_type(obj, "Objects.Geometry.Point")
meshes = find_by_type(obj, "Objects.Geometry.Mesh")
lines = find_by_type(obj, "Objects.Geometry.Line")

print(f"Found {len(points)} points, {len(meshes)} meshes, {len(lines)} lines")

# ❌ Won't work for BIM data:
# data_objects = find_by_type(obj, "Objects.Data.DataObject")
# # Returns ALL BIM objects - walls, columns, beams, everything!
# # Use Pattern 1 (find_by_property) instead

Pattern 3: Build Flat List

Purpose: Convert hierarchical object graphs into flat lists for easier processing. When to use:

Need to process all objects of a certain type
Want to use list operations (filter, map, sort)
Building dataframes or CSV exports
Running aggregate calculations (sum, average, count)

Advantages:

Easier to work with than nested structures
Can use standard Python list operations
Simple to convert to pandas DataFrame
Good for batch processing

Disadvantages:

Loses hierarchical relationships
Can be memory-intensive for large models
May include objects you don’t need

How it works:

Start with empty results list
Traverse entire object graph
Collect objects matching criteria
Return flat list

from specklepy.objects import Base
from specklepy.objects.geometry import Mesh
from specklepy.objects.data_objects import DataObject

def flatten_by_category(root, category_name):
    """Build a flat list of BIM objects by category (v3 pattern)."""
    results = []

    def traverse(obj):
        if isinstance(obj, Base):
            # Check properties for category (v3 pattern)
            if hasattr(obj, "properties") and isinstance(obj.properties, dict):
                if obj.properties.get("category") == category_name:
                    results.append(obj)

            # Continue traversing even after match
            for name in obj.get_member_names():
                if not name.startswith("_"):
                    value = getattr(obj, name, None)
                    traverse(value)

        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

        elif isinstance(obj, dict):
            for value in obj.values():
                traverse(value)

    traverse(root)
    return results

# For geometry primitives, use isinstance checks
def flatten_geometry(root, geometry_class):
    """Build a flat list of geometry objects by class."""
    results = []

    def traverse(obj):
        if isinstance(obj, geometry_class):
            results.append(obj)
        elif isinstance(obj, Base):
            for name in obj.get_member_names():
                if not name.startswith("_"):
                    value = getattr(obj, name, None)
                    traverse(value)
        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

    traverse(root)
    return results

# Use it
from specklepy.objects.geometry import Mesh, Point
meshes = flatten_geometry(obj, Mesh)
points = flatten_geometry(obj, Point)

Alternative: Property-based flattening (most flexible):

def flatten_by_properties(root, **filters):
    """Build a flat list of DataObjects by category (for v3 BIM objects)."""
    results = []

    def traverse(obj):
        if isinstance(obj, DataObject):
            obj_category = obj.properties.get("category", "")
            if obj_category == category:
                results.append(obj)

        if isinstance(obj, Base):
            for name in obj.get_member_names():
                if not name.startswith("_"):
                    value = getattr(obj, name, None)
                    traverse(value)
        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

    traverse(root)
    return results

# Use it for geometry
all_meshes = flatten_by_type(obj, "Objects.Geometry.Mesh")
print(f"Flattened to {len(all_meshes)} meshes")

# Use it for BIM objects (v3 pattern)
all_walls = flatten_data_objects_by_category(obj, "Walls")
all_columns = flatten_data_objects_by_category(obj, "Columns")
print(f"Found {len(all_walls)} walls, {len(all_columns)} columns")

Pattern 3a: Traverse elements[] Arrays

Purpose: Navigate object hierarchies by following the organizational structure defined by elements[] arrays. When to use:

Data is organized in Collections or Groups
Objects have a logical hierarchy (Building → Level → Room → Elements)
Want to respect the intentional organization
Need faster traversal by ignoring non-structural properties

How it works:

Check if object has elements property (list)
Recursively traverse only through elements arrays
Optionally filter during traversal
Ignore other properties like displayValue, geometry, etc.

Key concept: Many Speckle objects use elements[] to define parent-child relationships:

Collection objects have elements containing child objects
Group objects organize related objects in elements
Level hierarchies nest objects in elements

Advantages:

Faster than full traversal
Follows logical organization
Respects data structure intent
Avoids traversing geometry details

from specklepy.objects import Base
from specklepy.objects.data_objects import DataObject

def flatten_via_elements(root, filter_func=None):
    """
    Flatten object hierarchy by following elements[] arrays.
    Collections and groups typically have an elements property containing child objects.
    """
    results = []

    def traverse(obj):
        if isinstance(obj, Base):
            # Check if this object matches filter
            if filter_func is None or filter_func(obj):
                results.append(obj)

            # Recursively traverse elements array
            if hasattr(obj, "elements") and isinstance(obj.elements, list):
                for element in obj.elements:
                    traverse(element)

        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

    traverse(root)
    return results

# Use it - get all objects in elements hierarchy
all_objects = flatten_via_elements(obj)
print(f"Total objects in elements tree: {len(all_objects)}")

# Filter by type while traversing
walls = flatten_via_elements(
    obj,
    lambda o: isinstance(o, DataObject) and o.properties.get("category") == "Walls"
)
print(f"Walls in elements tree: {len(walls)}")

# Filter by property
load_bearing = flatten_via_elements(
    obj,
    lambda o: isinstance(o, DataObject) and o.properties.get("loadBearing") == True
)
print(f"Load bearing elements: {len(load_bearing)}")

Pattern 3b: Find Atomic/Displayable Objects

Purpose: Find only atomic, viewer-selectable objects by filtering for displayValue presence. When to use:

Need to count actual BIM elements (not containers or groups)
Want objects that appear as selectable items in the viewer
Building element lists for UI selection
Extracting objects that have visual representation
Need to match what users see in the 3D viewer

Key concept: Not all objects in the graph are “real” elements:

Containers (Collections, Groups) organize but aren’t selectable
Proxies (LevelProxy, ColorProxy) reference but aren’t rendered
Atomic objects have displayValue and ARE selectable in the viewer

Why displayValue matters: Objects with a displayValue property represent atomic, selectable items in the Speckle viewer. Each object with displayValue can be clicked, selected, and queried independently in the 3D view. The displayValue typically contains a list of Mesh objects that define the visual representation.

How it works:

Traverse the entire graph
Check if object has displayValue property
Verify displayValue is not None
Collect these objects
Optionally filter further by properties

Result: A list of objects that exactly matches what users can select in the viewer.

from specklepy.objects import Base
from specklepy.objects.data_objects import DataObject

def find_displayable_objects(root):
    """
    Find all objects with displayValue property.
    These are atomic objects that can be clicked/selected in the Speckle viewer.
    """
    displayable = []

    def traverse(obj):
        if isinstance(obj, Base):
            # Check if this object has displayValue
            if hasattr(obj, "displayValue") and obj.displayValue is not None:
                displayable.append(obj)

            # Continue traversing to find nested displayable objects
            if hasattr(obj, "elements") and isinstance(obj.elements, list):
                for element in obj.elements:
                    traverse(element)

            # Also traverse other properties
            for name in obj.get_member_names():
                if not name.startswith("_") and name != "displayValue":
                    value = getattr(obj, name, None)
                    traverse(value)

        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

    traverse(root)
    return displayable

# Find all atomic objects
atomic_objects = find_displayable_objects(obj)
print(f"Found {len(atomic_objects)} displayable objects")

# These are the objects that appear as selectable items in the viewer
for atomic_obj in atomic_objects[:5]:  # First 5
    if isinstance(atomic_obj, DataObject):
        category = atomic_obj.properties.get("category", "Unknown")
        print(f"  {atomic_obj.name} ({category})")
        print(f"    Display meshes: {len(atomic_obj.displayValue)}")

Combined Pattern: Elements + DisplayValue

Purpose: Efficiently find atomic BIM objects by combining structural traversal with displayValue filtering. When to use:

Large BIM models where full traversal is slow
Data is organized in elements[] hierarchy
Only want viewer-selectable objects
Need to skip containers and organizational objects

Advantages:

Faster than full traversal (follows elements[] only)
More precise than elements[] alone (filters to atomic objects)
Gets you exactly what users see in the viewer
Skips geometry details and nested references

Key Optimization: Objects with displayValue are typically leaf nodes - they don’t have children with displayValues. This means you can stop traversing deeper once you find a displayValue, making traversal much more efficient. Strategy:

Traverse through elements[] arrays only (ignore other properties)
At each object, check for displayValue
Collect objects that have displayValue and STOP traversing deeper
Skip containers and organizational objects automatically

def find_displayable_in_elements(root):
    """
    Traverse elements[] hierarchy and collect objects with displayValue.
    This gives you the atomic BIM objects that are viewer-selectable.
    """
    displayable = []

    def traverse(obj):
        if isinstance(obj, Base):
            # If it has displayValue, it's an atomic object (leaf node)
            if hasattr(obj, "displayValue") and obj.displayValue is not None:
                displayable.append(obj)
                return  # STOP - leaf nodes rarely have children with displayValue

            # Container node - continue down elements hierarchy
            if hasattr(obj, "elements") and isinstance(obj.elements, list):
                for element in obj.elements:
                    traverse(element)

    traverse(root)
    return displayable

# Get all viewer-selectable BIM objects
selectable = find_displayable_in_elements(obj)
print(f"Selectable objects: {len(selectable)}")

# Analyze by category
from collections import defaultdict
by_category = defaultdict(int)

for obj in selectable:
    if isinstance(obj, DataObject):
        category = obj.properties.get("category", "Other")
        by_category[category] += 1

print("\nBy category:")
for category, count in sorted(by_category.items()):
    print(f"  {category}: {count}")

Pattern 4: Group by Property

Purpose: Organize flat lists of objects into groups based on shared property values. When to use:

Need to analyze objects by category, type, or level
Building summaries (“count by type”)
Preparing data for reports or charts
Want to process each group separately
Need to find relationships between objects

Common groupings:

By BIM category (Walls, Columns, Floors)
By level/storey (“Level 1”, “Level 2”)
By family or type
By material
By any property value

How it works:

First, flatten objects (Pattern 3)
Iterate through flat list
Extract grouping property from each object
Build dictionary with property value as key
Append objects to appropriate group

Result: Dictionary where keys are property values and values are lists of objects.

from collections import defaultdict
from specklepy.objects import Base

def group_by_property(root, property_name, type_filter=None):
    """Group objects by a property value."""
    groups = defaultdict(list)

    def traverse(obj):
        if isinstance(obj, Base):
            # Apply type filter if specified
            if type_filter and type_filter not in obj.speckle_type:
                # Still traverse children
                for name in obj.get_member_names():
                    if not name.startswith("_"):
                        value = getattr(obj, name, None)
                        traverse(value)
                return

            # Try direct property
            if hasattr(obj, property_name):
                key = getattr(obj, property_name)
                groups[key].append(obj)

            # Try properties dict
            elif hasattr(obj, "properties") and isinstance(obj.properties, dict):
                if property_name in obj.properties:
                    key = obj.properties[property_name]
                    groups[key].append(obj)

            # Traverse children
            for name in obj.get_member_names():
                if not name.startswith("_"):
                    value = getattr(obj, name, None)
                    traverse(value)

        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

    traverse(root)
    return dict(groups)

# Use it
by_material = group_by_property(obj, "Material")
for material, elements in by_material.items():
    print(f"{material}: {len(elements)} elements")

by_level = group_by_property(obj, "level", type_filter="Revit")
for level, elements in by_level.items():
    print(f"{level}: {len(elements)} elements")

Pattern 5: Extract to DataFrame

Convert graph to tabular data:

import pandas as pd
from specklepy.objects import Base

def extract_to_dataframe(root, type_filter=None, properties=None):
    """Extract objects to pandas DataFrame."""
    rows = []

    def traverse(obj):
        if isinstance(obj, Base):
            # Apply type filter
            if type_filter and type_filter not in obj.speckle_type:
                # Still traverse children
                for name in obj.get_member_names():
                    if not name.startswith("_"):
                        value = getattr(obj, name, None)
                        traverse(value)
                return

            # Build row
            row = {
                "id": obj.id,
                "type": obj.speckle_type,
            }

            # Add specified properties
            if properties:
                for prop in properties:
                    # Direct property
                    if hasattr(obj, prop):
                        row[prop] = getattr(obj, prop)
                    # Properties dict
                    elif hasattr(obj, "properties") and isinstance(obj.properties, dict):
                        row[prop] = obj.properties.get(prop)
                    else:
                        row[prop] = None
            else:
                # Include all direct properties (not nested)
                for name in obj.get_typed_member_names():
                    value = getattr(obj, name, None)
                    if not isinstance(value, (Base, list, dict)):
                        row[name] = value

                # Include properties dict
                if hasattr(obj, "properties") and isinstance(obj.properties, dict):
                    for key, value in obj.properties.items():
                        if not isinstance(value, (Base, list, dict)):
                            row[key] = value

            rows.append(row)

            # Traverse children
            for name in obj.get_member_names():
                if not name.startswith("_"):
                    value = getattr(obj, name, None)
                    traverse(value)

        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

    traverse(root)
    return pd.DataFrame(rows)

# Use it - filter by DataObject with specific category
df = extract_to_dataframe(obj, type_filter="Objects.Data.DataObject")
# Then filter by properties
walls_df = df[df["properties"].apply(lambda p: p.get("category") == "Walls" if isinstance(p, dict) else False)]
print(walls_df.head())

# With specific properties
df = extract_to_dataframe(
    obj,
    type_filter="Wall",
    properties=["family", "type", "Volume", "Area", "Material"]
)
print(df[["family", "type", "Volume", "Area"]].head())

Pattern 6: Count by Type

Quick statistics:

from collections import Counter
from specklepy.objects import Base

def count_by_type(root):
    """Count objects by speckle_type."""
    counts = Counter()

    def traverse(obj):
        if isinstance(obj, Base):
            counts[obj.speckle_type] += 1

            for name in obj.get_member_names():
                if not name.startswith("_"):
                    value = getattr(obj, name, None)
                    traverse(value)

        elif isinstance(obj, list):
            for item in obj:
                traverse(item)

        elif isinstance(obj, dict):
            for value in obj.values():
                traverse(value)

    traverse(root)
    return counts

# Use it
counts = count_by_type(obj)

print("Object counts:")
for speckle_type, count in counts.most_common():
    print(f"  {speckle_type}: {count}")

# Summary by category
geometry_count = sum(count for type_name, count in counts.items() if "Geometry" in type_name)
revit_count = sum(count for type_name, count in counts.items() if "Revit" in type_name)

print(f"\nGeometry objects: {geometry_count}")
print(f"Revit objects: {revit_count}")

Handling Unknown Structures

When you don’t know the structure ahead of time:

from specklepy.objects import Base

def safe_traverse(obj, callback, max_depth=10, _depth=0):
    """Safely traverse unknown structures with depth limit."""
    if _depth > max_depth:
        return

    try:
        if isinstance(obj, Base):
            # Call callback with object
            callback(obj, _depth)

            # Traverse members safely
            for name in obj.get_member_names():
                if name.startswith("_"):
                    continue

                try:
                    value = getattr(obj, name, None)
                    safe_traverse(value, callback, max_depth, _depth + 1)
                except Exception as e:
                    print(f"Warning: Error accessing {name}: {e}")

        elif isinstance(obj, list):
            for i, item in enumerate(obj):
                try:
                    safe_traverse(item, callback, max_depth, _depth + 1)
                except Exception as e:
                    print(f"Warning: Error at list index {i}: {e}")

        elif isinstance(obj, dict):
            for key, value in obj.items():
                try:
                    safe_traverse(value, callback, max_depth, _depth + 1)
                except Exception as e:
                    print(f"Warning: Error at dict key {key}: {e}")

    except Exception as e:
        print(f"Warning: Traversal error at depth {_depth}: {e}")

# Use it
found_objects = []

def collect_meshes(obj, depth):
    if "Mesh" in obj.speckle_type:
        found_objects.append(obj)

safe_traverse(obj, collect_meshes)
print(f"Safely found {len(found_objects)} meshes")

Performance Optimization

Memoization

Cache traversal results:

from specklepy.objects import Base

class CachedTraverser:
    """Traverser with result caching."""

    def __init__(self):
        self.cache = {}

    def find_by_type(self, root, speckle_type):
        """Find objects with caching."""
        cache_key = (id(root), speckle_type)

        if cache_key in self.cache:
            return self.cache[cache_key]

        results = []

        def traverse(obj):
            if isinstance(obj, Base):
                if obj.speckle_type == speckle_type:
                    results.append(obj)

                for name in obj.get_member_names():
                    if not name.startswith("_"):
                        value = getattr(obj, name, None)
                        traverse(value)

            elif isinstance(obj, list):
                for item in obj:
                    traverse(item)

        traverse(root)
        self.cache[cache_key] = results
        return results

# Use it
traverser = CachedTraverser()

# First call - does traversal
meshes = traverser.find_by_type(obj, "Objects.Geometry.Mesh")

# Second call - uses cache
meshes_again = traverser.find_by_type(obj, "Objects.Geometry.Mesh")  # Fast!

Early Termination

Stop when found:

from specklepy.objects import Base

def find_first(root, predicate):
    """Find first object matching predicate (stops early)."""
    result = [None]  # Use list to allow modification in nested function

    def traverse(obj):
        if result[0] is not None:
            return  # Already found

        if isinstance(obj, Base):
            if predicate(obj):
                result[0] = obj
                return

            for name in obj.get_member_names():
                if result[0] is not None:
                    return
                if not name.startswith("_"):
                    value = getattr(obj, name, None)
                    traverse(value)

        elif isinstance(obj, list):
            for item in obj:
                if result[0] is not None:
                    return
                traverse(item)

    traverse(root)
    return result[0]

# Use it
first_wall = find_first(obj, lambda x: "Wall" in x.speckle_type)
if first_wall:
    print(f"Found wall: {first_wall.speckle_type}")

Complete Example: Building Analysis

from specklepy.api import operations
from specklepy.api.client import SpeckleClient
from specklepy.transports.server import ServerTransport
from collections import defaultdict
import pandas as pd

# 1. Receive building data
client = SpeckleClient(host="app.speckle.systems")
client.authenticate_with_token(token)

version = client.version.get(project_id, version_id)
transport = ServerTransport(stream_id=project_id, client=client)
building = operations.receive(version.referencedObject, remote_transport=transport)

print(f"Received: {building.speckle_type}")

# 2. Count object types
counts = count_by_type(building)
print("\n=== Object Inventory ===")
for obj_type, count in counts.most_common(10):
    print(f"{obj_type}: {count}")

# 3. Find all walls (v3 pattern - by property)
walls = find_by_property(building, "category", "Walls")
print(f"\n=== Walls ===")
print(f"Total walls: {len(walls)}")

# 4. Group walls by material
by_material = group_by_property(building, "Material", type_filter="Wall")
print("\nBy material:")
for material, wall_list in by_material.items():
    print(f"  {material}: {len(wall_list)}")

# 5. Extract to DataFrame for analysis
df = extract_to_dataframe(
    building,
    type_filter="Wall",
    properties=["family", "type", "Volume", "Area", "Material"]
)

print("\n=== Statistical Analysis ===")
print(f"Total volume: {df['Volume'].sum():.2f} m³")
print(f"Total area: {df['Area'].sum():.2f} m²")
print(f"Average wall volume: {df['Volume'].mean():.2f} m³")

# 6. Export results
df.to_csv("building_analysis.csv", index=False)
print("\n✓ Analysis exported to building_analysis.csv")

Best Practices

Always check property existence

Use hasattr() before accessing:

python # Good if hasattr(obj, "displayValue"): mesh =         obj.displayValue # Bad - can crash mesh = obj.displayValue

Skip private members

Don’t traverse _ prefixed properties:

python # Good for name in obj.get_member_names(): if       not name.startswith("_"): value = getattr(obj, name) # Bad - includes internals for name in       dir(obj): value = getattr(obj, name)

Handle both lists and single values

Properties can be lists or single objects:

python # Good display = obj.displayValue meshes =       display if isinstance(display, list) else [display] # Bad - assumes always list for mesh in       obj.displayValue: # Crashes if not list pass

Use type filters to reduce traversal

Limit traversal scope when possible:

python # Good - focused property search (v3 pattern)         walls = find_by_property(obj, "category", "Walls") # Less good - searches everything then         filters all_objects = find_all(obj, lambda x: True) walls = [o for o in all_objects if         hasattr(o, "properties") and o.properties.get("category") == "Walls"]

Summary

Effective traversal requires:

✅ Understanding the graph - Know if it’s simple, medium, or complex
✅ Choosing the right pattern - Type, property, flatten, group
✅ Defensive coding - Check before accessing
✅ Performance awareness - Cache results, terminate early
✅ Flexibility - Handle unknown structures gracefully

These patterns work with any Speckle data structure!

Next Steps

BIM Data Patterns

Apply these traversal techniques to complex BIM data

Simple Data Patterns

Working with custom and simple model data

Data Types

Understanding the three types of data

API Reference

Client and operations API documentation

​Overview

​Understanding Object Graphs

​Simple Graph (Custom Data)

​Medium Graph (Simple Model)

​Complex Graph (BIM Data)

​Why Traversal Matters

​Common Traversal Goals

​Traversal Strategies Explained

​Strategy 1: Property-Filtered Traversal ⭐ Recommended for v3

​Strategy 2: displayValue-Based Traversal ⭐ Recommended for v3

​Strategy 3: elements[] Hierarchy Traversal

​Strategy 4: Full Recursive Traversal

​Strategy 5: Type-Filtered Traversal (Limited Use in v3)

​Choosing the Right Pattern

​Basic Traversal Techniques

​Technique 1: Direct Property Access

​Technique 2: Iterate All Members

​Technique 3: Recursive Search

​Pattern 1: Find by Property ⭐ Primary Pattern for v3

​Pattern 2: Find by Type (Geometry Only)

​Pattern 3: Build Flat List

​Pattern 3a: Traverse elements[] Arrays

​Pattern 3b: Find Atomic/Displayable Objects

​Combined Pattern: Elements + DisplayValue

​Pattern 4: Group by Property

​Pattern 5: Extract to DataFrame

​Pattern 6: Count by Type

​Handling Unknown Structures

​Performance Optimization

​Memoization

​Early Termination

​Complete Example: Building Analysis

​Best Practices

​Summary

​Next Steps

BIM Data Patterns

Simple Data Patterns

Data Types

API Reference

Overview

Understanding Object Graphs

Simple Graph (Custom Data)

Medium Graph (Simple Model)

Complex Graph (BIM Data)

Why Traversal Matters

Common Traversal Goals

Traversal Strategies Explained

Strategy 1: Property-Filtered Traversal ⭐ Recommended for v3

Strategy 2: displayValue-Based Traversal ⭐ Recommended for v3

Strategy 3: elements[] Hierarchy Traversal

Strategy 4: Full Recursive Traversal

Strategy 5: Type-Filtered Traversal (Limited Use in v3)

Choosing the Right Pattern

Basic Traversal Techniques

Technique 1: Direct Property Access

Technique 2: Iterate All Members

Technique 3: Recursive Search

Pattern 1: Find by Property ⭐ Primary Pattern for v3

Pattern 2: Find by Type (Geometry Only)

Pattern 3: Build Flat List

Pattern 3a: Traverse elements[] Arrays

Pattern 3b: Find Atomic/Displayable Objects

Combined Pattern: Elements + DisplayValue

Pattern 4: Group by Property

Pattern 5: Extract to DataFrame

Pattern 6: Count by Type

Handling Unknown Structures

Performance Optimization

Memoization

Early Termination

Complete Example: Building Analysis

Best Practices

Summary

Next Steps