from six.moves import xrange
import numpy as np
import math
from numba import jit, float64, int64
from .base import MovementModel
[docs]class Model(MovementModel):
name = 'Gradient Levy Model'
default_parameters = {
"density": {
"alpha": 5.5687635749135715,
"density_exp": 0.9094048898627347,
"hr_exp": 0.9442292138564827,
"niche_size_exp": 0.5609397724240494},
"home_range": {
"alpha": 102.95793964057235,
"exponent": 1.8355253220927104},
"velocity": {
"alpha": -1.2919142788970375,
"beta": 2.3185432601405385},
'movement': {
'min_pareto': 1.1,
'max_pareto': 1.9,
'grad_weight': 8.0},
}
[docs] def generate_movement(
self,
initial_positions,
site,
steps,
velocity):
min_exponent = self.parameters['movement']['min_pareto']
max_exponent = self.parameters['movement']['max_pareto']
grad_weight = self.parameters['movement']['grad_weight']
heatmap = site.niche
range_ = site.range
resolution = site.resolution
gradient = np.stack(np.gradient(heatmap), -1)
mov = self._movement(
gradient,
heatmap,
initial_positions,
resolution,
velocity,
range_,
steps,
min_exponent,
max_exponent,
grad_weight)
return mov
@staticmethod
@jit(
float64[:, :, :](
float64[:, :, :],
float64[:, :],
float64[:, :],
float64,
float64,
float64[:],
int64,
float64,
float64,
float64),
nopython=True)
def _movement(
gradient,
heatmap,
random_positions,
resolution,
velocity,
range_,
steps,
min_exponent,
max_exponent,
grad_weight):
num, _ = random_positions.shape
movement = np.zeros((num, steps, 2), dtype=float64)
rangex, rangey = range_
directions = np.random.uniform(0, 1, size=(steps, num))
magnitudes = np.random.random((steps, num))
exponent_var = max_exponent - min_exponent
gradient = gradient[:, :, 0] + 1j * gradient[:, :, 1]
for k in xrange(steps):
movement[:, k, :] = random_positions
for j in xrange(num):
direction = directions[k, j]
magnitude = magnitudes[k, j]
index = (
random_positions[j, 0] // resolution,
random_positions[j, 1] // resolution)
grad = gradient[int(index[0]), int(index[1])]
value = heatmap[int(index[0]), int(index[1])]
exponent = min_exponent + exponent_var * value
magnitude = (velocity * (exponent - 1)) / \
(math.pow((1 - magnitude), 1/exponent) * exponent)
new_angle = (np.angle(grad) +
direction / (grad_weight * np.abs(grad) + 1e-10))
new_direction = (
magnitude * math.cos(new_angle),
magnitude * math.sin(new_angle))
tmp1 = (
random_positions[j, 0] + new_direction[0],
random_positions[j, 1] + new_direction[1])
tmp2 = (tmp1[0] % (2 * rangex), tmp1[1] % (2 * rangey))
if tmp2[0] < rangex:
random_positions[j, 0] = tmp2[0] % rangex
else:
random_positions[j, 0] = (-tmp2[0]) % rangex
if tmp2[1] < rangey:
random_positions[j, 1] = tmp2[1] % rangey
else:
random_positions[j, 1] = (-tmp2[1]) % rangey
return movement
