polyframe 0.1.5

A modern python library for 3D homogenous transforms and coordinate system utilities.

Polyframe

A fast, flexible, zero-cost Python library for 3D homogeneous transforms and arbitrary coordinate-system conventions.

---

🚀 Features

• Arbitrary Cartesian frames
  Define any right- or left-handed (x,y,z) basis via a simple enum.
  Zero-cost: all “bring your own conventions” work happens at import time.

• Flexible spherical conventions
  Compute azimuth/elevation, φ/θ or latitude/longitude with pluggable:

  • signed vs. unsigned angles
  • CW vs. CCW positive rotation
  • polar vs. elevation definitions
  • custom “up”/“forward”/“lateral” axes

• Effortless re-framing
  Change the basis of a Transform in one call:

  from polyframe import FrameRegistry, Direction, Transform
  
  tr_world = Transform.from_values(translation=[1,2,3])
  cs_robot = FrameRegistry.from_directions(
      Direction.RIGHT, Direction.FORWARD, Direction.UP
  )
  tr_robot = tr_world.change_coordinate_system(cs_robot)
  

• Quaternion & Euler support
  Apply quaternion or Euler rotations directly:

  tr = Transform()
  tr_q = tr.apply_quaternion([0,0,0,1])           # [x,y,z,w]
  tr_e = tr.apply_euler_rotation(roll, pitch, yaw)
  

• Distance & direction utilities

  • distance_to(), vector_to(), direction_to()

• Numba-accelerated core routines
  Under the hood, heavy linear-algebra paths (look_at, angle computations, quaternion ↔ matrix) are JIT-compiled for max throughput.

• Memory-efficient
  Coordinate frames are static types; no per-instance overhead for storing conventions.

---

📦 Installation

pip install polyframe

---

🎬 Quickstart

from polyframe import FrameRegistry, Direction, Transform

# 1) identity at origin, facing +X
tr = Transform()

# 2) translate to (1,2,3)
tr = tr.apply_translation([1,2,3])

# 3) rotate via Euler angles
tr = tr.apply_euler_rotation(roll=0, pitch=45, yaw=90)

# 4) apply a quaternion
tr = tr.apply_quaternion([0,0,0,1])  # x,y,z,w

# 5) make it look at a point
tr = tr.look_at([4,5,6])

# 6) compute spherical angles
az, el, rng = tr.az_el_range_to([7,8,9])

# 7) compute φ/θ
phi, theta = tr.phi_theta_to([1,1,0])

# 8) distance/direction helpers
dist = tr.distance_to([2,2,2])
vec  = tr.vector_to([2,2,2])
dirn = tr.direction_to([2,2,2])

# 9) re-frame into a robot basis
robot_cs = FrameRegistry.from_directions(
    Direction.FORWARD, Direction.DOWN, Direction.LEFT
)
tr_robot = tr.change_coordinate_system(robot_cs)

---

🔍 Coordinate-System Conventions

Cartesian frames

Choose from any of 48 valid (x_dir,y_dir,z_dir) triples:

from polyframe import Direction, FrameRegistry

# World: X forward, Y left, Z up
world_cs = FrameRegistry.from_directions(
    Direction.FORWARD,
    Direction.LEFT,
    Direction.UP,
)

# Robot: X right, Y forward, Z down
robot_cs = FrameRegistry.from_directions(
    Direction.RIGHT,
    Direction.FORWARD,
    Direction.DOWN,
)

Each frame type exposes .forward / .backward / .left / .right / .up / .down unit vectors.

Spherical coordinates

• Azimuth/Elevation (az_el_range_to)
• Longitude/Latitude (lat_lon_to)
• φ/θ (polar) (phi_theta_to)

All accept flags:

Flag	Meaning
degrees	degrees (default) or radians
signed_*	signed (±180°/π) vs. unsigned (0…360°/2π)
counterclockwise_*	CCW-positive vs. CW-positive
polar (φ/θ only)	θ as polar (0…π) vs. elevation (±90°)
flip_*	flip sign of elevation/latitude/θ

---

⚙️ API Highlights

from polyframe import Transform

# Construction
tr = Transform()                             # identity
tr2 = Transform.from_values(
    translation=[1,2,3],
    rotation=[[...]],    # 3×3
    scale=[2,2,2],
)

# Translation / Rotation / Scale
tr_t = tr.apply_translation([1,0,0])
tr_r = tr.apply_rotation(R)                  # 3×3
tr_q = tr.apply_quaternion(q)                # 4-vector [x,y,z,w]
tr_e = tr.apply_euler_rotation(roll,pitch,yaw)
tr_s = tr.apply_scale([2,3,4])

# Inverse / Combine
inv    = tr.inverse()
inv_ip = tr.inverse(inplace=True)
C      = tr @ tr2                            # compose

# Point / Vector
p2 = tr.transform_point([1,2,3])
v2 = tr.transform_vector([1,0,0])

# Re-frame
tr_new = tr.change_coordinate_system(other_frame)

# Look-at
tr_look = tr.look_at([x,y,z])

# Distance & Direction
d = tr.distance_to([x,y,z])
v = tr.vector_to([x,y,z])
dir = tr.direction_to([x,y,z])

# Angles
az, el, rng = tr.az_el_range_to([x,y,z])
phi, th     = tr.phi_theta_to([x,y,z])
lat, lon    = tr.lat_lon_to([x,y,z])

See the full docs.

---

🤝 Contributing

1. Fork
2. Add a branch
3. Write tests under tests/
4. PR — must pass CI (including Numba builds)

---

📄 License

Apache 2.0 — see LICENSE.