from six.moves import xrange
import numpy as np
from numba import jit, float64, int64
from .base import MovementModel
[docs]class Model(MovementModel):
name = 'Constant Brownian Model'
default_parameters = {
'movement': {},
"density": {
"alpha": 4.950773510732457,
"density_exp": 0.8501718469622543,
"hr_exp": 0.8441724458551622,
"niche_size_exp": 0.8760285191282491
},
"home_range": {
"alpha": 59.02095500748234,
"exponent": 1.903072119815655
},
"velocity": {
"alpha": 0.002223569915673946,
"beta": 1.013044346939526
}
}
[docs] def generate_movement(
self,
initial_positions,
site,
steps,
velocity):
range_ = site.range
mov = self._movement(
initial_positions,
velocity,
range_,
steps)
return mov
@staticmethod
@jit(
float64[:, :, :](
float64[:, :],
float64,
float64[:],
int64),
nopython=True)
def _movement(
random_positions,
velocity,
range_,
steps):
num, _ = random_positions.shape
movement = np.zeros((num, steps, 2), dtype=float64)
sigma = velocity / 1.2533141373155003
rangex, rangey = range_
directions = np.random.normal(
0, sigma, size=(steps, num, 2))
for k in xrange(steps):
movement[:, k, :] = random_positions
for j in xrange(num):
direction = directions[k, j]
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
