def test_boundary_travel_time_anuga(self):
'''
Test running into the boundary and not reaching travel time target
'''
# defining / initializing
an_particle = Particles(self.an_params) # define the particle
np.random.seed(0) # fix the random seed
# generate particles
an_particle.generate_particles(self.Np_tracer, self.seed_xloc,
self.seed_yloc) # init walk data
# set target time for iterations
all_walk_data = an_particle.run_iteration(target_time=1000.0)
# make assertions
# check initial position and travel time
assert all_walk_data['xinds'][0][0] == self.seed_xloc[0]
assert all_walk_data['yinds'][0][0] == self.seed_yloc[0]
assert all_walk_data['travel_times'][0][0] == 0.0
# particle reaches boundary after 18 iterations
# data shouldn't be recorded after boundary is reached
assert len(all_walk_data['xinds'][0]) == 9
assert len(all_walk_data['yinds'][0]) == 9
assert len(all_walk_data['travel_times'][0]) == 9
def test_few_steps_RCM(self):
'''
Test running a few steps
'''
# defining / initializing
particle = Particles(self.params) # define the particle
np.random.seed(0) # fix the random seed
# init the walk data
particle.generate_particles(self.Np_tracer, self.seed_xloc,
self.seed_yloc)
# 3 iterations
for i in list(range(0, 3)):
all_walk_data = particle.run_iteration()
# make assertions
# check initial position and travel time
assert all_walk_data['xinds'][0][0] == self.seed_xloc[0]
assert all_walk_data['yinds'][0][0] == self.seed_yloc[0]
assert all_walk_data['travel_times'][0][0] == 0.0
# check all positions and times
assert all_walk_data['xinds'][0] == [16, 17, 18, 19]
assert all_walk_data['yinds'][0] == [140, 141, 141, 141]
assert all_walk_data['travel_times'][0] == \
[0, 7007593448.337235, 10439964733.462337, 13698473384.876724]
def test_set_time_RCM():
'''
Test setting a time target
'''
# defining / initializing
particle = Particles(params) # define the particle
np.random.seed(0) # fix the random seed
# generate particles
particle.generate_particles(Np_tracer, seed_xloc,
seed_yloc) # init the walk data
# set time
all_walk_data = particle.run_iteration(target_time=5e6)
# make assertions
# check initial position and travel time
assert all_walk_data['xinds'][0][0] == seed_xloc[0]
assert all_walk_data['yinds'][0][0] == seed_yloc[0]
assert all_walk_data['travel_times'][0][0] == 0.0
# check all positions and times
assert pytest.approx(all_walk_data['xinds'][0] == [16, 17, 18, 19, 20])
assert pytest.approx(
all_walk_data['yinds'][0] == [140, 141, 141, 141, 142])
assert pytest.approx(
all_walk_data['travel_times'][0] ==
[0.0, 1414213.56594, 2414213.56846, 3414213.57099, 4828427.13693])
def test_set_time_RCM_previousdata(self):
'''
Test setting a time target when using old walk data
'''
# defining / initializing
particle = Particles(self.params) # define the particle
np.random.seed(0) # fix the random seed
# generate the particles
particle.generate_particles(self.Np_tracer, self.seed_xloc,
self.seed_yloc) # init walk data
# set time
all_walk_data = particle.run_iteration(target_time=5e6)
# make assertions
# check initial position and travel time
assert all_walk_data['xinds'][0][0] == self.seed_xloc[0]
assert all_walk_data['yinds'][0][0] == self.seed_yloc[0]
assert all_walk_data['travel_times'][0][0] == 0.0
# check all positions and times
assert all_walk_data['xinds'][0] == [16, 17]
assert all_walk_data['yinds'][0] == [140, 141]
assert all_walk_data['travel_times'][0] == [0, 7007593448.337235]
def test_boundary_anuga(self):
'''
Test running into the boundary
'''
# defining / initializing
an_particle = Particles(self.an_params) # define the particle
np.random.seed(0) # fix the random seed
an_particle.generate_particles(self.Np_tracer, self.seed_xloc,
self.seed_yloc) # init walk data
# 20 iterations
for i in list(range(0, 20)):
all_walk_data = an_particle.run_iteration()
# make assertions
# check initial position and travel time
assert all_walk_data['xinds'][0][0] == self.seed_xloc[0]
assert all_walk_data['yinds'][0][0] == self.seed_yloc[0]
assert all_walk_data['travel_times'][0][0] == 0.0
# particle reaches boundary after 18 iterations
# data shouldn't be recorded after boundary is reached
assert len(all_walk_data['xinds'][0]) == 15
assert len(all_walk_data['yinds'][0]) == 15
assert len(all_walk_data['travel_times'][0]) == 15
def test_few_steps_anuga(self):
'''
Test running a few steps
'''
# defining / initializing
an_particle = Particles(self.an_params) # define the particle
np.random.seed(0) # fix the random seed
an_particle.generate_particles(self.Np_tracer, self.seed_xloc,
self.seed_yloc) # init walk data
# 3 iterations
for i in list(range(0, 3)):
all_walk_data = an_particle.run_iteration()
# make assertions
# check initial position and travel time
assert all_walk_data['xinds'][0][0] == self.seed_xloc[0]
assert all_walk_data['yinds'][0][0] == self.seed_yloc[0]
assert all_walk_data['travel_times'][0][0] == 0.0
# check all positions and times
assert all_walk_data['xinds'][0] == [5, 6, 7, 8]
assert all_walk_data['yinds'][0] == [10, 10, 10, 10]
assert all_walk_data['travel_times'][0] == \
[0, 132.8105258698104, 258.1975289086354, 375.18128832888203]
def test_few_steps_RCM():
'''
Test running a few steps
'''
# defining / initializing
particle = Particles(params) # define the particle
np.random.seed(0) # fix the random seed
particle.generate_particles(Np_tracer, seed_xloc,
seed_yloc) # init the walk data
# 3 iterations
for i in list(range(0, 3)):
all_walk_data = particle.run_iteration()
# make assertions
# check initial position and travel time
assert all_walk_data['xinds'][0][0] == seed_xloc[0]
assert all_walk_data['yinds'][0][0] == seed_yloc[0]
assert all_walk_data['travel_times'][0][0] == 0.0
# check all positions and times
assert pytest.approx(all_walk_data['xinds'][0] == [16, 17, 18, 19])
assert pytest.approx(all_walk_data['yinds'][0] == [140, 141, 141, 141])
assert pytest.approx(all_walk_data['travel_times'][0] ==
[0.0, 1414213.56594, 2414213.56846, 3414213.57099])
def test_few_steps_anuga():
'''
Test running a few steps
'''
# defining / initializing
an_particle = Particles(an_params) # define the particle
np.random.seed(0) # fix the random seed
an_particle.generate_particles(Np_tracer, seed_xloc,
seed_yloc) # init the walk data
# 3 iterations
for i in list(range(0, 3)):
all_walk_data = an_particle.run_iteration()
# make assertions
# check initial position and travel time
assert all_walk_data['xinds'][0][0] == seed_xloc[0]
assert all_walk_data['yinds'][0][0] == seed_yloc[0]
assert all_walk_data['travel_times'][0][0] == 0.0
# check all positions and times
assert pytest.approx(all_walk_data['xinds'][0] == [5, 6, 7, 8])
assert pytest.approx(all_walk_data['yinds'][0] == [10, 10, 10, 10])
assert pytest.approx(
all_walk_data['travel_times'][0] == [0.0, 2.68302, 5.27901, 7.83891])
"""Plot for quickstart demo 2 particle locations."""
import matplotlib.pyplot as plt
from dorado.example_data.define_params import make_anuga_params
from dorado.particle_track import Particles
import dorado as pr
import numpy as np
np.random.seed(1) # make result consistent for docs
params = make_anuga_params()
particles = Particles(params)
seed_xloc = list(range(20, 30))
seed_yloc = list(range(48, 53))
Np_tracer = 50
particles.generate_particles(Np_tracer, seed_xloc, seed_yloc)
walk_data = particles.run_iteration(target_time=2100)
plt.figure(figsize=(8, 5))
pr.routines.plot_state(particles.depth, walk_data, iteration=0, c='b')
pr.routines.plot_state(particles.depth, walk_data, iteration=-1, c='r')
plt.title('Initial and Final Particle Locations')
plt.tight_layout()
plt.show()
params.qy = qy
params.dx = 50.
params.theta = 1.0
params.model = 'Anuga'
# for parallel routing we define the particle information
seed_xloc = list(range(20, 30))
seed_yloc = list(range(48, 53))
Np_tracer = 200
# Apply the parameters to run the particle routing model
# use 2 cores to route in parallel
print('start parallel')
start_par_time = time.time()
particles = Particles(params)
par_result = parallel_routing(particles, 50, Np_tracer, seed_xloc, seed_yloc,
2)
par_time = time.time() - start_par_time
print('end parallel')
# compare to a serial run
print('start serial')
start_serial_time = time.time()
# do twice to match number of particles parallel is doing
for z in list(range(0, 2)):
particle = Particles(params)
particle.generate_particles(Np_tracer, seed_xloc, seed_yloc)
# do 50 iterations to match parallel
for i in list(range(0, 50)):
all_walk = particle.run_iteration()