from six.moves import xrange
import numpy as np
from numba import jit, float64, int64
from .base import MovementModel
[docs]class Model(MovementModel):
name = 'Variable Brownian Model'
default_parameters = {
'movement': {
'niche_weight': 0.2},
"density": {
"alpha": 4.956627725964207,
"density_exp": 0.8509470680476414,
"hr_exp": 0.8449833387462566,
"niche_size_exp": 0.8752336080881222
},
"home_range": {
"alpha": 57.61382053222021,
"exponent": 1.9096337585646315
},
"velocity": {
"alpha": 0.14473388093667755,
"beta": 0.8918520334954713
}
}
[docs] def generate_movement(
self,
initial_positions,
site,
steps,
velocity):
niche_weight = self.parameters['movement']['niche_weight']
heatmap = site.niche
resolution = site.resolution
range_ = site.range
mov = self._movement(
heatmap,
initial_positions,
resolution,
velocity,
range_,
steps,
niche_weight)
return mov
@staticmethod
@jit(
float64[:, :, :](
float64[:, :],
float64[:, :],
float64,
float64,
float64[:],
int64,
float64),
nopython=True)
def _movement(
heatmap,
random_positions,
resolution,
velocity,
range_,
steps,
niche_weight):
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]
index = (
random_positions[j, 0] // resolution,
random_positions[j, 1] // resolution)
value = heatmap[int(index[0]), int(index[1])]
direction *= 1 + niche_weight * (0.5 - value)
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
