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 = 'Constant Levy Model'
default_parameters = {
'movement': {
'pareto': 1.8},
"density": {
"alpha": 5.139654979615388,
"density_exp": 0.8673231302202313,
"hr_exp": 0.8709250205414867,
"niche_size_exp": 0.8105129468063047
},
"home_range": {
"alpha": 82.0335925804581,
"exponent": 1.9018678531419373
},
"velocity": {
"alpha": 0.0005826019701355142,
"beta": 1.0525954033363216
}
}
[docs] def generate_movement(
self,
initial_positions,
site,
steps,
velocity):
exponent = self.parameters['movement']['pareto']
range_ = site.range
mov = self._movement(
initial_positions,
velocity,
range_,
steps,
exponent)
return mov
@staticmethod
@jit(
float64[:, :, :](
float64[:, :],
float64,
float64[:],
int64,
float64),
nopython=True)
def _movement(
random_positions,
velocity,
range_,
steps,
exponent):
num, _ = random_positions.shape
movement = np.zeros((num, steps, 2), dtype=float64)
random_angles = np.random.uniform(0.0, 2 * np.pi, size=(steps, num))
rangex, rangey = range_
for k in xrange(steps):
movement[:, k, :] = random_positions
for j in xrange(num):
angle = random_angles[k, j]
heading = (math.cos(angle), math.sin(angle))
magnitude = (velocity * (exponent - 1)) / \
(math.pow((1 - np.random.rand()), 1/exponent) * exponent)
direction = (magnitude * heading[0], magnitude * heading[1])
tmp1 = (
random_positions[j, 0] + direction[0],
random_positions[j, 1] + 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
