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This example shows how to initialize the Voxel Farm library in a report Lambda program. import voxelfarm as vf  

The following example shows how to read inputs from a report lambda program. mesh_entity = vf.input_entity("mesh_entity_id", "Entity", vf.type.voxel_mesh) string_input = vf.input_string("string_input", "String Input", "") numerical_input = vf.input("numeric...

This example shows how to create volumetric boolean operations between volumetric datasets.   # Get inputs, in this case the entities that will have they volumes computed entityA = vf.input_entity("entityA", "Select Entity A", vf.type.voxel_generator | vf.t...

This example shows how to create a Pandas dataframe from voxels inside a Report Lambda program and output a different dataframe as a result.   import pandas import voxelfarm as vf entity = vf.input_entity("entity", "Select Entity", vf.type.block_model) ...

VoxelSpace's spatial lambdas are serverless functions that execute custom computations on your volumetricdata. They run near your data on highly parallel infrastructure, allowing you to process trillions of voxels inminutes. This guide explains what spatial la...

Spatial lambdas are stateless cloud functions written in languages such as Python or .NET. They take voxel gridsas input, perform a computation, and return a result or a modified dataset. Examples include: Volume calculations and statistical summaries Filt...

Use a spatial lambda when you need to: Compute metrics such as total volume tonnage average density, or other summarystatistics Filter voxels based on property thresholds (e.g., density > 2.5) or remove empty voxels Resample data to a coarser or finer res...

Spatial lambdas are typically composed of three main components: 1. Input Definition Specify the dataset(s) and region of interest (bounding box or selection) to process. 2. Compute Function Write code that iterates through voxels and applies your logic. F...

Follow these steps to execute a spatial lambda: Step 1:Prepare Your Dataset Ensure your data is processed into an indexed dataset or voxel grid. Lambdas operate on indexed datasets ratherthan raw uploads. Step 2:Select or Upload a Lambda Navigate to your d...

Performance Optimization Limit the region: Restrict the lambda to the smallest area necessary to reduce compute costand time Efficient algorithms: Use vectorized operations where possible for better performance Memory management: Be mindful of memory usage ...

Here's a more sophisticated lambda that performs statistical analysis: def statistical_analysis_lambda(context, voxel_reader, voxel_writer): grades = [] volumes = [] for voxel in voxel_reader: grade = voxel.properties.get('grade',0.0) if...

Performance Problems Large memory usage: Process voxels in batches rather than loading all into memory Slow execution: Optimize algorithms and reduce unnecessary computations Timeout errors: Break large jobs into smaller regions    Data Issues Miss...