def concentration_array_demo(det, dt=0.01, t_max=3, draw_iter_interval=50):
"""
Demonstration of setting up plume model and processing the outputted
puff arrays with the ConcentrationArrayGenerator class, the resulting
arrays being displayed with the matplotlib imshow function.
"""
# define simulation region
wind_region = models.Rectangle(0., -2., 10., 2.)
sim_region = models.Rectangle(0., -1., 2., 1.)
# set up wind model
wind_model = models.WindModel(
wind_region,
21.,
11.,
noise_gain=0,
u_av=1,
)
# set up plume model
plume_model = models.PlumeModel(sim_region, (0.1, 0., 0.),
wind_model,
centre_rel_diff_scale=1.5,
puff_release_rate=500,
puff_init_rad=0.001,
puff_spread_rate=0.0003)
# set up concentration array generator
array_gen = processors.ConcentrationArrayGenerator(sim_region, 0.01, 500,
500, 1.)
# set up figure
fig = _set_up_figure('Concentration field array demo')
# display initial concentration field as image
conc_array = array_gen.generate_single_array(plume_model.puff_array)
im_extents = (sim_region.x_min, sim_region.x_max, sim_region.y_min,
sim_region.y_max)
conc_im = plt.imshow(conc_array.T,
extent=im_extents,
vmin=0,
vmax=det,
cmap=cm.binary_r)
conc_im.axes.set_xlabel('x')
conc_im.axes.set_ylabel('y')
# define update and draw functions
def update_func(dt, t):
wind_model.update(dt)
plume_model.update(dt)
draw_func = lambda: conc_im.set_data(
array_gen.generate_single_array(plume_model.puff_array).T)
# start simulation loop
_simulation_loop(dt, t_max, draw_iter_interval, update_func, draw_func,
det)
return fig
def plume_model_demo(dt=0.01, t_max=100, draw_iter_interval=200):
"""
Demonstration of setting up plume model and displaying with matplotlib
plot and quiver functions.
"""
# define simulation region
sim_region = models.Rectangle(0., -2., 8., 2.)
# set up wind model
wind_model = models.WindModel(sim_region, 21., 11., u_av=1., Kx=2., Ky=2.)
# set up plume model
plume_model = models.PlumeModel(sim_region, (0.5, 0., 0.),
wind_model,
puff_release_rate=10,
centre_rel_diff_scale=1.5)
# set up figure window
fig, time_text = _set_up_figure('Plume model demo')
# create quiver plot of initial velocity field
# quiver expects first array dimension (rows) to correspond to y-axis
# therefore need to transpose
vf_plot = plt.quiver(wind_model.x_points,
wind_model.y_points,
wind_model.velocity_field[:, :, 0].T,
wind_model.velocity_field[:, :, 1].T,
width=0.001)
# expand axis limits to make vectors at boundary of field visible
vf_plot.axes.axis(vf_plot.axes.axis() + np.array([-0.5, 0.5, -0.5, 0.5]))
# draw initial puff positions with scatter plot
pp_plot = plt.scatter(plume_model.puff_array[:, 0],
plume_model.puff_array[:, 1],
100 * plume_model.puff_array[:, 3]**0.5,
c='r',
edgecolors='none')
# define update and draw functions
def update_func(dt, t):
wind_model.update(dt)
plume_model.update(dt)
def draw_func():
# update velocity field quiver plot data
vf_plot.set_UVC(wind_model.velocity_field[:, :, 0].T,
wind_model.velocity_field[:, :, 1].T)
# update puff position scatter plot positions and sizes
pp_plot.set_offsets(plume_model.puff_array[:, :2])
pp_plot._sizes = 100 * plume_model.puff_array[:, 3]**0.5
# begin simulation loop
_simulation_loop(dt, t_max, time_text, draw_iter_interval, update_func,
draw_func)
return fig
def conc_point_val_demo(dt=0.01, t_max=5, draw_iter_interval=20, x=1., y=0.0):
"""
Demonstration of setting up plume model and processing the outputted
puff arrays with the ConcentrationPointValueCalculator class, the
resulting concentration time course at a point in the odour plume being
displayed with the matplotlib plot function.
"""
# define simulation region
wind_region = models.Rectangle(0., -2., 10., 2.)
sim_region = models.Rectangle(0., -1., 2., 1.)
# set up wind model
wind_model = models.WindModel(wind_region, 21., 11., u_av=2.)
# set up plume model
plume_model = models.PlumeModel(sim_region, (0.1, 0., 0.),
wind_model,
centre_rel_diff_scale=1.5,
puff_release_rate=25,
puff_init_rad=0.01)
# let simulation run for 10s to get plume established
for t in np.arange(0, 10, dt):
wind_model.update(dt)
plume_model.update(dt)
# set up concentration point value calculator
val_calc = processors.ConcentrationValueCalculator(0.01**2)
conc_vals = []
conc_vals.append(val_calc.calc_conc_point(plume_model.puff_array, x, y))
ts = [0.]
# set up figure
fig, time_text = _set_up_figure('Concentration point value trace demo')
# display initial concentration field as image
conc_line, = plt.plot(ts, conc_vals)
conc_line.axes.set_xlim(0., t_max)
conc_line.axes.set_ylim(0., .5)
conc_line.axes.set_xlabel('t / s')
conc_line.axes.set_ylabel('Normalised concentration')
conc_line.axes.grid(True)
# define update and draw functions
def update_func(dt, t):
wind_model.update(dt)
plume_model.update(dt)
ts.append(t)
conc_vals.append(val_calc.calc_conc_point(plume_model.puff_array, x,
y))
draw_func = lambda: conc_line.set_data(ts, conc_vals)
# start simulation loop
_simulation_loop(dt, t_max, time_text, draw_iter_interval, update_func,
draw_func)
return fig
# Define plume model parameters
plume_model_params = {
'source_pos': (5., 0., 0.),
'centre_rel_diff_scale': 2.,
'puff_release_rate': 10,
'puff_init_rad': 0.001**0.5,
'puff_spread_rate': 0.001,
'init_num_puffs': 10,
'max_num_puffs': 1000,
'model_z_disp': True,
}
# Create plume model object
plume_model = models.PlumeModel(prng=rng,
sim_region=sim_region,
wind_model=wind_model,
**plume_model_params)
# Define concentration array (image) generator parameters
array_gen_params = {
'array_z': 0.,
'nx': 500,
'ny': 250,
'puff_mol_amount': 8.3e8
}
# Create concentration array generator object
array_gen = processors.ConcentrationArrayGenerator(array_xy_region=sim_region,
**array_gen_params)
# Set up figure
def wind_vel_and_conc_demo(dt=0.01, t_max=5, draw_iter_interval=50):
"""
Demonstration of setting up plume model and processing the outputted
puff arrays with the ConcentrationArrayGenerator class, the resulting
arrays being displayed with the matplotlib imshow function.
"""
# define simulation region
wind_region = models.Rectangle(0., -1., 4., 1.)
sim_region = models.Rectangle(0., -1., 4., 1.)
# set up wind model
wind_model = models.WindModel(wind_region,
11.,
11.,
u_av=1.,
noise_gain=9.,
noise_bandwidth=0.3)
# set up plume model
plume_model = models.PlumeModel(sim_region, (0.1, 0., 0.),
wind_model,
centre_rel_diff_scale=1.5,
puff_release_rate=200,
puff_init_rad=0.001)
for t in np.arange(0, 1, dt):
wind_model.update(dt)
plume_model.update(dt)
# set up concentration array generator
array_gen = processors.ConcentrationArrayGenerator(sim_region, 0.01, 1000,
500, 1.)
# set up figure
fig, time_text = _set_up_figure('Concentration array demo')
ax_c = fig.add_subplot('111')
# ax_w = fig.add_subplot('122')
# display initial wind velocity field as quiver plot
vf_plot = ax_c.quiver(wind_model.x_points,
wind_model.y_points,
wind_model.velocity_field[:, :, 0].T,
wind_model.velocity_field[:, :, 1].T,
width=0.002)
ax_c.set_xlabel('x / m')
ax_c.set_ylabel('y / m')
ax_c.set_aspect(1)
# display initial concentration field as image
conc_array = (array_gen.generate_single_array(
plume_model.puff_array).T[::-1])
im_extents = (sim_region.x_min, sim_region.x_max, sim_region.y_min,
sim_region.y_max)
conc_im = ax_c.imshow(conc_array,
extent=im_extents,
vmin=0,
vmax=5e3,
cmap=cm.Reds)
ax_c.set_xlabel('x / m')
ax_c.set_ylabel('y / m')
# define update and draw functions
def update_func(dt, t):
wind_model.update(dt)
plume_model.update(dt)
def draw_func():
conc_im.set_data(
array_gen.generate_single_array(plume_model.puff_array).T[::-1, :])
vf_plot.set_UVC(wind_model.velocity_field[:, :, 0].T,
wind_model.velocity_field[:, :, 1].T)
# start simulation loop
_simulation_loop(dt, t_max, time_text, draw_iter_interval, update_func,
draw_func)
return fig