def toggle(event):
global scatter_currently_headings
K = -1.*K_slider.val
x_0 = x_0_slider.val
time = stime.val*60.
wind_mag = windmag_slider.val
cone_angle = np.radians(cone_angle_slider.val)
track_heading_angles,dispersing_speeds = f0(intended_heading_angles,wind_mag,wind_angle)
if scatter_currently_headings:
intersection_distances,dispersal_distances = f1_wedge(
track_heading_angles,source_pos,wind_angle,cone_angle)
success_probabilities = f2(intersection_distances,K,x_0,source_pos,wind_angle)
plume_assignments = f3(success_probabilities,dispersal_distances)
dispersal_travel_times,release_to_chosen_plume_distances = f4(
plume_assignments,dispersal_distances,dispersing_speeds)
fly_dots.set_offsets(
scipy.c_[
release_to_chosen_plume_distances*np.cos(track_heading_angles),
release_to_chosen_plume_distances*np.sin(track_heading_angles)])
scatter_currently_headings = not(scatter_currently_headings)
else:
fly_dots.set_offsets(
scipy.c_[
time*dispersing_speeds*np.cos(track_heading_angles),
time*dispersing_speeds*np.sin(track_heading_angles)])
scatter_currently_headings = not(scatter_currently_headings)
fig1.canvas.draw_idle()
def update1(val):
wind_mag = windmag_slider.val
track_heading_angles, dispersing_speeds = f0(intended_heading_angles,
wind_mag, wind_angle)
time = stime.val * 60.
fly_dots.set_offsets(
scipy.c_[time * dispersing_speeds * np.cos(track_heading_angles),
time * dispersing_speeds * np.sin(track_heading_angles)])
fig1.canvas.draw_idle()
import matplotlib.pyplot as plt
import numpy as np
from pompy import models
wind_mag = 1.
wind_angle = np.pi
cone_angle = np.radians(10.)
#Source locations
location_list = [(200, 100), (300, -100), (-100, -200)]
source_pos = np.array([np.array(tup) for tup in location_list])
#fly headings: = np.pi/3 and 4*np.pi/3
intended_heading_angles = np.radians(np.array([60, 110, 200, 240]))
track_heading_angles, dispersing_speeds = f0(intended_heading_angles, wind_mag,
wind_angle)
#Convert track_heading_angles to a list of plume intersection locations for each fly
intersection_distances, dispersal_distances = f1_wedge(track_heading_angles,
source_pos, wind_angle,
cone_angle)
print('intersection_distances: ')
print(np.array2string(intersection_distances))
print('dispersal_distances: ')
print(np.array2string(dispersal_distances))
#Display plumes
im_extents = (-500, 500, -500, 500)
# gaussianfitPlumes = models.GaussianFitPlume(source_pos,wind_angle,wind_mag)
# conc_im = gaussianfitPlumes.conc_im(im_extents,samples=200)
def update(val):
wind_mag = windmag_slider.val
K = -1. * K_slider.val
x_0 = x_0_slider.val
track_heading_angles, dispersing_speeds = f0(intended_heading_angles,
wind_mag, wind_angle)
intersection_distances, dispersal_distances = f1(track_heading_angles,
source_pos, wind_angle)
success_probabilities = f2(intersection_distances, K, x_0, source_pos,
wind_angle)
plume_assignments = f3(success_probabilities, dispersal_distances)
dispersal_travel_times = f4(plume_assignments, dispersal_distances,
dispersing_speeds)
arrival_times,chasing_times,\
which_flies,which_traps = f5(plume_assignments,
dispersal_travel_times,intersection_distances,fly_speed,release_times)
#(1) Update the CDF figure
new_maxes = 400 * np.ones(8)
trap_counts = np.zeros(8)
first_hit_times = np.full(8, np.nan)
for i in range(8):
row = sim_reorder[i] - 1
ax = axes[i]
t_sim = arrival_times[which_traps == i] / 60.
try:
(n, bins) = np.histogram(
t_sim,
bins=num_bins, #cumulative=True,
# histtype='step',
range=(0, max(t_sim)))
cum_n = np.cumsum(n)
lines[i].set_ydata(np.hstack((np.array([
0,
]), cum_n)))
lines[i].set_xdata(bins)
new_maxes[i] = max(400., 50 * np.ceil(max(cum_n) / 50.))
# xs,ys = cdf_patches[i].get_xy().T
# # print(patches[i].get_xy())
# old_bbox = cdf_patches[i].get_extents()
# print(old_bbox)
# [[x0, y0], [x1, y1]] = old_bbox.get_points()
# x0 = min(t_sim)
# y0 = max(t_sim)
#
# # new_bbox = Bbox([[x0, y0], [x1, y1]])
# # cdf_patches[i].set_clip_box(new_bbox)
# old_bbox.set_points([[x0, y0], [x1, y1]])
# new = cdf_patches[i].get_extents()
# print(new)
# print('--------')
# # cdf_patches[i].set_xy(np.vstack((xs,ys)).T)
# # raw_input()
# # cdf_patches[i].set_x(min(t_sim))
# # cdf_patches[i].set_width(max(t_sim)-min(t_sim))
# cdf_patches[i].update(min(t_sim),max(t_sim)-min(t_sim))
except (ValueError):
lines[i].set_ydata(np.zeros_like(lines[i].get_ydata))
try:
trap_counts[i] = max(cum_n)
except:
trap_counts[i] = 0
patch_object = cdf_patches[i]
patch_object.update(min(t_sim), max(t_sim) - min(t_sim))
patch_object.rectangle.set_height(ax.get_ylim()[1])
#print(patch_object.rectangle.get_x())
#print('---------')
cdf_steepnesses[i] = trap_counts[i] / (max(t_sim) - min(t_sim))
first_hit_times[i] = min(t_sim)
for i, ax in enumerate(axes):
ax.set_ylim([0, np.max(new_maxes)])
#(2) update the fly heading display
time = stime.val * 60.
fly_dots.set_offsets(
scipy.c_[time * dispersing_speeds * np.cos(track_heading_angles),
time * dispersing_speeds * np.sin(track_heading_angles)])
#(3) update the trap histogram figure
trap_cmap_vals = (first_hit_times - vmin) / vmax
# print('--------')
# print(trap_cmap_vals)
for i, trap_patch, trap_count in zip(range(8), trap_patches, trap_counts):
trap_patch.set_radius(radius_scale * trap_count / max(new_maxes))
trap_patch.set_alpha(min(cdf_steepnesses[i] / steepness_max, 1.))
color = tuple(
np.array(
(trap_cmap(trap_cmap_vals[i])[:-1])).astype(float).tolist())
# print(color)
trap_patch.set_color(color)
fig1.canvas.draw_idle()
fig2.canvas.draw_idle()
fig3.canvas.draw_idle()
def update(val):
global scatter_currently_headings
wind_mag = windmag_slider.val
K = -1.*K_slider.val
x_0 = x_0_slider.val
cone_angle = np.radians(cone_angle_slider.val)
track_heading_angles,dispersing_speeds = f0(intended_heading_angles,wind_mag,wind_angle)
intersection_distances,dispersal_distances = f1_wedge(
track_heading_angles,source_pos,wind_angle,cone_angle)
success_probabilities = f2(intersection_distances,K,x_0,source_pos,wind_angle)
plume_assignments = f3(success_probabilities,dispersal_distances)
dispersal_travel_times,release_to_chosen_plume_distances = f4(
plume_assignments,dispersal_distances,dispersing_speeds)
arrival_times,chasing_times,\
which_flies,which_traps = f5(plume_assignments,
dispersal_travel_times,intersection_distances,fly_speed,release_times)
#(1) Update the CDF figure
new_maxes = 400*np.ones(8)
trap_counts = np.zeros(8)
first_hit_times = np.full(8,np.nan)
for i in range(8):
row = sim_reorder[i]-1
ax = axes[i]
t_sim = arrival_times[which_traps==i]/60.
try:
(n, bins) = np.histogram(t_sim,bins=num_bins,#cumulative=True,
# histtype='step',
range=(0,max(t_sim)))
cum_n = np.cumsum(n)
lines[i].set_ydata(np.hstack((np.array([0,]),cum_n)))
lines[i].set_xdata(bins)
new_maxes[i] = max(400.,50*np.ceil(max(cum_n)/50.))
trap_counts[i]=max(cum_n)
except(ValueError):
lines[i].set_ydata(np.zeros_like(lines[i].get_ydata))
trap_counts[i]=0
patch_object = cdf_patches[i]
try:
patch_object.update(min(t_sim),max(t_sim)-min(t_sim))
patch_object.rectangle.set_height(ax.get_ylim()[1])
cdf_steepnesses[i] = trap_counts[i]/(max(t_sim)-min(t_sim))
first_hit_times[i] = min(t_sim)
except(ValueError):
pass
#print(patch_object.rectangle.get_x())
#print('---------')
for i,ax in enumerate(axes):
ax.set_ylim([0,np.max(new_maxes)])
#(2) update the fly heading display
time = stime.val*60.
if scatter_currently_headings:
fly_dots.set_offsets(
scipy.c_[
time*dispersing_speeds*np.cos(track_heading_angles),
time*dispersing_speeds*np.sin(track_heading_angles)])
else:
fly_dots.set_offsets(
scipy.c_[
release_to_chosen_plume_distances*np.cos(track_heading_angles),
release_to_chosen_plume_distances*np.sin(track_heading_angles)])
#(2a) update the plume wedges
wedge_points = plot_wedges(source_pos,wind_angle,cone_angle)
for i,wedge in enumerate(plume_wedges):
wedge.set_xy(wedge_points[:,i,:])
#(3) update the trap histogram figure
trap_cmap_vals = (first_hit_times-vmin)/vmax
# print('--------')
# print(trap_cmap_vals)
for i,trap_patch,trap_count in zip(range(8),trap_patches,trap_counts):
trap_patch.set_radius(radius_scale*trap_count/max(new_maxes))
trap_patch.set_alpha(min(cdf_steepnesses[i]/steepness_max,1.))
color = tuple(np.array((trap_cmap(trap_cmap_vals[i])[:-1])).astype(float).tolist())
# print(color)
trap_patch.set_color(color)
fig1.canvas.draw_idle()
fig2.canvas.draw_idle()
fig3.canvas.draw_idle()
