Skip to main content

A data-oriented wrapper library for the Blender Python API

Project description

databpy

codecov pypi tests deployment

A set of data-oriented wrappers around the python API of Blender.

CleanShot 2025-04-13 at 13 17 32@2x

This was originally used internally inside of Molecular Nodes but was broken out into a separate python module for re-use in other projects.

Installation

Available on PyPI, install with pip:

pip install databpy

[!CAUTION]

bpy (Blender as a python module) is listed as an optional dependency, so that if you install databpy inside of Blender you won’t install a redundant version of bpy. If you are using this outside of Blender, you will need to specifically request bpy with either of these methods:

# install wtih bpy dependency
pip install 'databpy[bpy]'

# install both packages
pip install databpy bpy

# install with all optional dependencies
pip install 'databpy[all]'

Usage

The main use cases are to create objects, store and retrieve attributes from them. The functions are named around nodes in Geometry Nodes Store Named Attribute and Named Attribute

import databpy as db

db.store_named_attribute() # store a named attribute on a mesh object
db.named_attribute()       # retrieve a named attribute from a mesh object

Here's an example on how to store an attribute:

import numpy as np
import databpy as db

coords = np.array([
    [0, 0, 0],
    [0, 5, 0],
    [5, 0, 0],
    [5, 5, 0]
])

obj = db.create_object(coords, name="Box")
db.store_named_attribute(obj, np.array([10, 20, 31, 42]), "vals")

image

This module is mainly used to create mesh objects and work with their attributes. It is built to store and retrieve data using NumPy arrays:

import numpy as np
import databpy as db
np.random.seed(6)

# Create a mesh object
random_verts = np.random.rand(10, 3)

obj = db.create_object(random_verts, name="RandomMesh")

obj.name
'RandomMesh'

Access attributes from the object’s mesh.

db.named_attribute(obj, 'position')
array([[0.89286017, 0.33197981, 0.8212291 ],
       [0.04169663, 0.10765668, 0.59505206],
       [0.52981734, 0.41880742, 0.33540785],
       [0.62251943, 0.43814144, 0.7358821 ],
       [0.51803643, 0.57885861, 0.64535511],
       [0.99022424, 0.81985819, 0.41320094],
       [0.87626767, 0.82375944, 0.05447451],
       [0.71863723, 0.80217057, 0.73640662],
       [0.70913178, 0.54093683, 0.12482417],
       [0.95764732, 0.4032563 , 0.21695116]])

BlenderObject class (bob)

This is a convenience class that wraps around the bpy.types.Object, and provides access to all of the useful functions. We can wrap an existing Object or return one when creating a new object.

This just gives us access to the named_attribute() and store_named_attribute() functions on the object class, but also provides a more intuitive way to access the object’s attributes.

bob = db.BlenderObject(obj)       # wraps the existing object 
bob = db.create_bob(random_verts) # creates a new object and returns it already wrapped

# these two are identical
bob.named_attribute('position')
bob.position
array([[0.89286017, 0.33197981, 0.8212291 ],
       [0.04169663, 0.10765668, 0.59505206],
       [0.52981734, 0.41880742, 0.33540785],
       [0.62251943, 0.43814144, 0.7358821 ],
       [0.51803643, 0.57885861, 0.64535511],
       [0.99022424, 0.81985819, 0.41320094],
       [0.87626767, 0.82375944, 0.05447451],
       [0.71863723, 0.80217057, 0.73640662],
       [0.70913178, 0.54093683, 0.12482417],
       [0.95764732, 0.4032563 , 0.21695116]])

We can clear all of the data from the object and initialise a new mesh underneath:

bob.new_from_pydata(np.random.randn(5, 3))
bob.position
array([[ 0.82465386, -1.17643154,  1.5644896 ],
       [ 0.71270508, -0.1810066 ,  0.53419954],
       [-0.58661294, -1.48185325,  0.85724759],
       [ 0.94309896,  0.11444143, -0.02195668],
       [-2.12714458, -0.83440745, -0.46550831]])

Example with Polars data

import polars as pl
import databpy as db
from io import StringIO

json_file = StringIO("""
{
  "Dino": [
    [55.3846, 97.1795, 0.0],
    [51.5385, 96.0256, 0.0]
  ],
  "Star": [
    [58.2136, 91.8819, 0.0],
    [58.1961, 92.215, 0.0]
  ]
}
""")

df = pl.read_json(json_file)
columns_to_explode = [col for col in df.columns if df[col].dtype == pl.List(pl.List)]
df = df.explode(columns_to_explode)

vertices = np.zeros((len(df), 3), dtype=np.float32)
bob = db.create_bob(vertices, name="DinoStar")

for col in df.columns:
    data = np.vstack(df.get_column(col).to_numpy())
    bob.store_named_attribute(data, col)

bob.named_attribute("Dino")
array([[55.38460159, 97.17949677,  0.        ],
       [51.53850174, 96.02559662,  0.        ]])
bob.named_attribute("Star")
array([[58.21360016, 91.88189697,  0.        ],
       [58.19609833, 92.21499634,  0.        ]])

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

databpy-0.4.1.tar.gz (49.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

databpy-0.4.1-py3-none-any.whl (36.7 kB view details)

Uploaded Python 3

File details

Details for the file databpy-0.4.1.tar.gz.

File metadata

  • Download URL: databpy-0.4.1.tar.gz
  • Upload date:
  • Size: 49.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for databpy-0.4.1.tar.gz
Algorithm Hash digest
SHA256 fccce81df6c15136508dd4e6bb3011d11cd602dc1808cec5aec5b7555f6fceef
MD5 84102094a1bb96e542c6262384ad8f78
BLAKE2b-256 8055a2a6b7f17ef16f33f572e0614f526909d51228dd901dc87674f66c2ab022

See more details on using hashes here.

Provenance

The following attestation bundles were made for databpy-0.4.1.tar.gz:

Publisher: ci-cd.yml on BradyAJohnston/databpy

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file databpy-0.4.1-py3-none-any.whl.

File metadata

  • Download URL: databpy-0.4.1-py3-none-any.whl
  • Upload date:
  • Size: 36.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for databpy-0.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 ba0cb35f52d77e6a0c3d39b161ed9bef2adf7e9106d56c1cad3ef5e159334113
MD5 e7dd12a2bd8e9bff78b9ca0873471727
BLAKE2b-256 dec2bedc19d7000982a495aacf5936ccf028b5b84a12833f82a69bfa51e5faa8

See more details on using hashes here.

Provenance

The following attestation bundles were made for databpy-0.4.1-py3-none-any.whl:

Publisher: ci-cd.yml on BradyAJohnston/databpy

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page