Skip to main content

databpy

codecov pypi tests deployment

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

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

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

# store and retrieve attributes from a mesh, point cloud, or curves object
db.store_named_attribute()
db.named_attribute()

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([[0.8928602 , 0.3319798 , 0.8212291 ],
       [0.04169663, 0.10765668, 0.59505206],
       [0.52981734, 0.41880742, 0.33540785],
       [0.62251943, 0.43814144, 0.7358821 ],
       [0.5180364 , 0.5788586 , 0.6453551 ],
       [0.99022424, 0.8198582 , 0.41320094],
       [0.8762677 , 0.82375944, 0.05447451],
       [0.7186372 , 0.8021706 , 0.7364066 ],
       [0.7091318 , 0.5409368 , 0.12482417],
       [0.9576473 , 0.4032563 , 0.21695116]], dtype=float32)

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.

# wrap an existing object or create a new one
bob = db.BlenderObject(obj)
bob = db.create_bob(random_verts)

# these two are identical
bob.named_attribute("position")
bob.position
AttributeArray(name='position', object='NewObject', mesh='NewObject', domain=POINT, type=FLOAT_VECTOR, shape=(10, 3), dtype=float32)
array([[0.8928602 , 0.3319798 , 0.8212291 ],
       [0.04169663, 0.10765668, 0.59505206],
       [0.52981734, 0.41880742, 0.33540785],
       [0.62251943, 0.43814144, 0.7358821 ],
       [0.5180364 , 0.5788586 , 0.6453551 ],
       [0.99022424, 0.8198582 , 0.41320094],
       [0.8762677 , 0.82375944, 0.05447451],
       [0.7186372 , 0.8021706 , 0.7364066 ],
       [0.7091318 , 0.5409368 , 0.12482417],
       [0.9576473 , 0.4032563 , 0.21695116]], dtype=float32)

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
AttributeArray(name='position', object='NewObject', mesh='NewObject', domain=POINT, type=FLOAT_VECTOR, shape=(5, 3), dtype=float32)
array([[ 0.82465386, -1.1764315 ,  1.5644896 ],
       [ 0.7127051 , -0.1810066 ,  0.53419954],
       [-0.58661294, -1.4818532 ,  0.8572476 ],
       [ 0.94309896,  0.11444143, -0.02195668],
       [-2.1271446 , -0.83440745, -0.4655083 ]], dtype=float32)

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, empty_as_null=True)

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([[55.3846, 97.1795,  0.    ],
       [51.5385, 96.0256,  0.    ]], dtype=float32)
bob.named_attribute("Star")
array([[58.2136, 91.8819,  0.    ],
       [58.1961, 92.215 ,  0.    ]], dtype=float32)

Metadata

Release files for databpy 0.10.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for databpy 0.10.0
File Size Uploaded
databpy-0.10.0.tar.gz 35.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for databpy 0.10.0
File Interpreter ABI Platform
databpy-0.10.0-py3-none-any.whl Python 3 none any Details

Total release size: 77.2 kB

Release files / databpy-0.10.0.tar.gz

Download URL databpy-0.10.0.tar.gz
Size 35.2 kB
Tags Source
SHA-256 checksum
How to use checksums
21935f6e6a47ee3c8e2d5b9aba9738256e069a28940e7d01982c6f74274271fb
BLAKE2b-256 checksum
How to use checksums
7be5c0119b5fa3e066363589b0021ef64ab632512cb3998f103248ad44bc4be3
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 27, 2026.

Transparency log

Release files / databpy-0.10.0-py3-none-any.whl

Download URL databpy-0.10.0-py3-none-any.whl
Size 42.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
f89d82ba437ffcd2e08cf3b69facfd2ab568f6f7d67a759079d94011ee38a796
BLAKE2b-256 checksum
How to use checksums
11a775405d8632f7d0c3cb4e7e9342adf267637991af72ed3306b1ae6e6e3b89
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 27, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.10.0 This release

2 release files

0.9.0

2 release files

0.8.0

2 release files

0.7.0

2 release files

0.6.2

2 release files

0.6.0

2 release files

0.5.1

2 release files

0.5.0

2 release files

0.4.2

2 release files

0.4.1

2 release files

0.4.0

2 release files

0.3.1

2 release files

0.3.0

2 release files

0.2.0

2 release files

0.1.0

2 release files

0.0.19

2 release files

0.0.18

2 release files

0.0.17

2 release files

0.0.15

2 release files

0.0.14

2 release files

0.0.11

2 release files

0.0.10

2 release files

0.0.9

2 release files

0.0.8

2 release files

0.0.7

2 release files

0.0.6

2 release files

0.0.5

2 release files

0.0.4

2 release files

0.0.3

2 release files

0.0.2

2 release files

0.0.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page