def agronomicplotwithdistributions(length, width, sowing_density, plant_density, inter_row, mu=0.0, kappa=3.0, noise = 0,convunit=100):
"""
Returns the
- number of plants
- the positions
- azimuthal orientation of each plant
- the domain
- the simulated density
of a micro-plot specified with agronomical variables
Inputs:
- length (m) is plot dimension along row direction
- width (m) is plot dimension perpendicular to row direction
- sowing density is the density of seeds sawn
- plant_density is the density of plants that are present (after loss due to bad emergence, early death...)
- inter_row (m) is for the distance between rows
- mu, kappa are the von Mises parameters of the azimuthal distribution
- noise (%), indicates the precision of the sowing for the inter plant spacing
- unit (m or cm) is for the unit of the position and domain
"""
inter_plant = 1. / inter_row / sowing_density
nrow = max(1, int(float(width) / inter_row))
plant_per_row = max(1,int(float(length) / inter_plant))
nplants = nrow * plant_per_row
positions, domain = regular(nplants, nrow, inter_plant * convunit, inter_row * convunit)
n_emerged = int(nplants * plant_density / sowing_density)
positions = sample(positions, n_emerged)
density = int(n_emerged / ( abs(domain[1][0] - domain[0][0]) / convunit * abs(domain[1][1] - domain[0][1]) / convunit))
azimuths = vonmises(mu, kappa, nplants)
return n_emerged, positions, azimuths, domain, density
def agronomicplotwithdistributions(length, width, sowing_density, plant_density, inter_row, mu=0.0, kappa=3.0, noise = 0,convunit=100):
"""
Returns the
- number of plants
- the positions
- azimuthal orientation of each plant
- the domain
- the simulated density
of a micro-plot specified with agronomical variables
Inputs:
- length (m) is plot dimension along row direction
- width (m) is plot dimension perpendicular to row direction
- sowing density is the density of seeds sawn
- plant_density is the density of plants that are present (after loss due to bad emergence, early death...)
- inter_row (m) is for the distance between rows
- mu, kappa are the von Mises parameters of the azimuthal distribution
- noise (%), indicates the precision of the sowing for the inter plant spacing
- unit (m or cm) is for the unit of the position and domain
"""
inter_plant = 1. / inter_row / sowing_density
nrow = max(1, int(float(width) / inter_row))
plant_per_row = max(1,int(float(length) / inter_plant))
nplants = nrow * plant_per_row
positions, domain = regular(nplants, nrow, inter_plant * convunit, inter_row * convunit)
n_emerged = int(nplants * plant_density / sowing_density)
positions = sample(positions, n_emerged)
density = int(n_emerged / ( abs(domain[1][0] - domain[0][0]) / convunit * abs(domain[1][1] - domain[0][1]) / convunit))
azimuths = vonmises(mu, kappa, nplants)
return n_emerged, positions, azimuths, domain, density
def get_landmark_measurements(self, X_WB_xy):
"""
:param X_WB_xy: robot pose ([x, y]]) in world frame.
:return:
(1) idx_visible_l: indices into self.l_xy whose distance to t_xy is
smaller than self.r_sensor.
(2) noisy measurements of distances to self.l_xy[idx_visible_l]
"""
X_WB = meshcat.transformations.rotation_matrix(0, np.array([0, 0, 1.]))
X_WB[:2, 3] = X_WB_xy
l_xyz_W = np.zeros((self.nl, 3))
l_xyz_W[:, :2] = self.l_xy
X_BW = np.linalg.inv(X_WB)
l_xyz_B = (X_BW[:3, :3].dot(l_xyz_W.T)).T + X_BW[:3, 3]
d_l = np.linalg.norm(l_xyz_B, axis=1)
theta_l_B = np.arctan2(l_xyz_B[:, 1], l_xyz_B[:, 0])
is_visible = np.all(
[d_l > self.r_range_min, d_l < self.r_range_max], axis=0)
idx_visible_l = np.where(is_visible)[0]
d_l_noisy = d_l[idx_visible_l] + random.normal(
scale=self.sigma_range, size=idx_visible_l.size)
bearings_noisy = random.vonmises(
theta_l_B[idx_visible_l], self.kappa_bearing)
return idx_visible_l, d_l_noisy, bearings_noisy
def _sample_nu(self):
ang_bar = self._calc_ang()
nu0 = np.array([np.cos(self.nu0), np.sin(self.nu0)])
for r in self.nu:
tmp = self.kappa[r] * ang_bar[r] + nu0 * self.kappa0
kappa = linalg.norm(tmp)
nu = tmp / kappa
nu = np.arctan2(nu[1], nu[0])
self.nu[r] = rd.vonmises(nu, kappa)
def von_mises(num_bins: int,
num_points: int,
kappa: float = 0.5,
mean_loc: float = 0.,
visualize: bool = False) -> np.ndarray:
'''
Generates von mises distributed data on the unit circle.
'''
samples = npr.vonmises(mean_loc, kappa=kappa, size=num_points)
result = np.histogram(samples, bins=num_bins, range=(-pi, pi))[0]
# induce a random circular shift
result = np.roll(result, npr.randint(0, num_bins))
if visualize is True:
plot_distribution(result, 'Von Mises')
return result
def test_VonMises_range(self):
# Make sure generated random variables are in [-pi, pi].
# Regression test for ticket #986.
for mu in np.linspace(-7., 7., 5):
r = random.vonmises(mu, 1, 50)
assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
def creat_rel_samp(para):
[mu_theta, ka_theta, mn, std] = para
theta = rd.vonmises(mu_theta, ka_theta)
r = abs(rd.normal(mn, std))
X = [np.cos(theta), np.sin(theta), r, 1]
return np.array(X).T
def wander(self):
phi = vonmises(self.last_heading, self.wander_faith)
rho = self.wander_effort * self.max_speed
return Decisions.WANDER, (rho, phi)
def wander(self):
phi = util.wrap_angle(self.last_heading +
vonmises(0, self.wander_faith))
rho = self.wander_effort * self.max_speed
self._move(rho, phi)
def vonmises(size, params):
try:
return random.vonmises(params['mu'], params['kappa'], size)
except ValueError as e:
exit(e)
if __name__ == "__main__":
from numpy.random import vonmises
from dials.array_family import flex
from math import cos, sin, pi
m = 0
k = 0.1
k1 = 1.0 / k
r = 1.0
num = 1000
z = flex.double(flex.grid(100, 100))
for i in range(1000):
u = vonmises(m, k1)
for j in range(num):
t = 2 * pi * j / num
xx = (r - r * cos(u)) + r * cos(t)
yy = (r * sin(u)) + r * sin(t)
ii = 40 + int(xx * 40)
jj = 40 + int(yy * 40)
if ii >= 0 and ii < 100 and jj >= 0 and jj < 100:
z[jj, ii] += 1
from matplotlib import pylab
pylab.imshow(z.as_numpy_array())
pylab.show()
