# -*- coding: utf-8 -*-
# Copyright (c) 2020 Stephen Wasilewski, HSLU and EPFL
# =======================================================================
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
# =======================================================================
import numpy as np
from raytools import translate
filterdict = {
'wav': (np.array([[-1, 2, -1]])/2, np.array([[-1], [2], [-1]])/2,
np.array([[-1, 0, 0], [0, 2, 0], [0, 0, -1]])/2),
'haar': (np.array([[1, -1]])/2, np.array([[1], [-1]])/2,
np.array([[1, 0], [0, -1]])/2)
}
[docs]
class Sensor(object):
"""for use as engine in area sampler, holds collection of multiple sensor
directions and offsets
Parameters
----------
engine: craytraverse.renderer.Rennderer
fully initialized renderer class instance
dirs: Sequence, optional
array like shape (N, 3) sensor directions
offsets: Sequence, optional
array like shape (N, 3) offsets from sample position to include
(for example mulitple z-heights)
sunview: bool, optional
NOT IMPLEMENTED
if True, dirs are treated as candidate reflection normals, a value
of (0, 0, 0) is prepended to hold the direct view.
"""
def __init__(self, engine, dirs=(0.0, 0.0, 1.0), offsets=(0.0, 0.0, 0.0),
name="sensor", sunview=False):
self.name = name
self.sunview = sunview
self.engine = engine
dirs = np.atleast_2d(dirs)
self.offsets = np.atleast_2d(offsets)
if dirs.shape[1] == 3:
self.dirs = translate.norm(np.atleast_2d(dirs))
# if sunview:
# self.dirs = np.concatenate((((0, 0, 0),), self.dirs))
d, o = np.broadcast_arrays(self.dirs[None], self.offsets[:, None])
self.sensors = np.hstack((o.reshape(-1, 3), d.reshape(-1, 3)))
elif dirs.shape[1] == 6:
dirs[:, 3:] = translate.norm(dirs[:, 3:])
self.dirs = dirs
self.sensors = dirs
else:
raise ValueError(f"bad shape for sensor dirs: {dirs.shape} should "
f"be (N,3) or (N,6)")
self.features = self.sensors.shape[0]
@property
def nproc(self):
return self.engine.nproc
def __call__(self, rays):
srays = np.copy(self.stack_rays(rays).reshape(-1, 6), 'C')
r = self.engine.run(srays)
return r.reshape(len(rays), len(self.sensors), *r.shape[1:])
[docs]
def run(self, *args, **kwargs):
"""alias for call, for consistency with SamplerPt classes for nested
dimensions of evaluation"""
return self(args[0])
[docs]
def stack_rays(self, r):
r = np.atleast_2d(r)
srays = np.hstack((r, np.zeros(r.shape)))
return srays[:, None] + self.sensors[None]
