def print_current(merged, prefix=""):
a1, d1 = merged[prefix + "am"].values, merged[prefix + "dm"].values
a2, d2 = merged["ae"].values, merged["de"].values
ax = plt.gca()
segs = []
widths = []
scale = 3
width_scale = 1000
a1 = np.array(a1)
d1 = np.array(d1)
a2 = np.array(a2)
d2 = np.array(d2)
for i, a in enumerate(a1):
al = (a1[i] - a2[i])
dl = (d1[i] - d2[i])
segs.append(((a1[i] + scale * al, d1[i] + scale * dl),
(a2[i] - scale * al, d2[i] - scale * dl)))
widths.append(np.sqrt(al * al + dl * dl) * width_scale)
ln_coll = LineCollection(segs, color="black", linewidth=widths)
ln_coll.set_antialiased(True)
ax.add_collection(ln_coll)
ax.axis("equal")
ax.invert_xaxis()
plt.legend()
def plot(ax, x, y, time, sim_type): assert(len(x) == len(y) == len(time)) l = len(time) if use_hf_coloration: time_to_grayscale = 0.8 / 23.6 # for HF coloring else: time_to_grayscale = 0.8 / time[l-1] colors = [] for i in range(l-1): if use_hf_coloration: color = get_hf_color(time[i]) # time[] is really HF else: g = 0.8 - (time[i] * time_to_grayscale)**2.0 if sim_type == 'driven': color = (g, 1.0, g, 0.8) else: color = (g, g, 1.0, 1.0) colors.append(color) points = zip(x,y) segments = zip(points[:-1], points[1:]) lc = LineCollection(segments, colors=colors) lc.set_alpha(1.0) lc.set_linewidth(1.0) lc.set_antialiased(True) ax.add_collection(lc) if use_hf_coloration: end_points.append((x[l-1], y[l-1], get_hf_color(time[l-1]))) else: end_points.append((x[l-1], y[l-1], COLOR[sim_type]))
def plot(ax, x, y, time, sim_type):
assert (len(x) == len(y) == len(time))
l = len(time)
if use_hf_coloration:
time_to_grayscale = 0.8 / 23.6 # for HF coloring
else:
time_to_grayscale = 0.8 / time[l - 1]
colors = []
for i in range(l - 1):
if use_hf_coloration:
color = get_hf_color(time[i]) # time[] is really HF
else:
g = 0.8 - (time[i] * time_to_grayscale)**2.0
if sim_type == 'driven':
color = (g, 1.0, g, 0.8)
else:
color = (g, g, 1.0, 1.0)
colors.append(color)
points = zip(x, y)
segments = zip(points[:-1], points[1:])
lc = LineCollection(segments, colors=colors)
lc.set_alpha(1.0)
lc.set_linewidth(1.0)
lc.set_antialiased(True)
ax.add_collection(lc)
if use_hf_coloration:
end_points.append((x[l - 1], y[l - 1], get_hf_color(time[l - 1])))
else:
end_points.append((x[l - 1], y[l - 1], COLOR[sim_type]))
def animation(j):
global T, dT_xx
if j > 0:
dT_xx = dd_x(d_x(T))
# dT_xx = d(T)
m = a * max(dT_xx.max(), abs(dT_xx.min())) * dt
if max(m, dx_max) > dx_max:
time = 0
while time < dt:
dT_xx = dd_x(d_x(T))
# dT_xx = d(T)
m = a * max(dT_xx.max(), abs(dT_xx.min())) * dt
dt_tempo = dx_max / m
T += a * dT_xx * dt_tempo
time += dt_tempo
else:
T += a * dT_xx * dt
ax.clear()
points = np.array([X, T]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-2], points[2:]], axis=1)
lc = LineCollection(segments, cmap='plasma', norm=norm)
lc.set_array(0.5 * (T[:-2] + T[2:]))
lc.set_linewidth(1)
lc.set_antialiased(True)
line = ax.add_collection(lc)
# fig.colorbar(line, ax=ax)
plt.ylim(T_min - delta, T_max + delta)
plt.xlim(x_0, x_f)
def get_solid_line(y, c_map, width, color, onecolor=None, limits=None):
points = np.array([y[3, :], y[2, :]]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
if onecolor:
color = np.array([onecolor] * len(y[0, :]))
lc = LineCollection(segments, colors=color)
else:
if limits:
norm = plt.Normalize(limits[0], limits[1])
lc = LineCollection(segments, cmap=c_map, norm=norm)
else:
lc = LineCollection(segments, cmap=c_map)
lc.set_array(color)
lc.set_linewidth(width)
#lc.set_joinstyle('bevel')
lc.set_snap(False)
lc.set_capstyle('round') #projecting
lc.set_antialiased(True)
return lc
def plotter(choice, numNeurons, numTimeSteps, maxNeurons, maxTimeSteps,
reverse, numInputNeurons, colorNeurons, redStart, redEnd,
greenStart, greenEnd, blueStart, blueEnd, eatNeuron, mateNeuron,
fightNeuron, moveNeuron, yawNeuron, label, behaviorLabels,
inputLabels):
rhythm_file = open_file(choice, "r")
line = next_line(rhythm_file)
line = next_line(rhythm_file)
figwidth = 12.0
figheight = 8.0
fig = pylab.figure(figsize=(figwidth, figheight))
ax = fig.add_subplot(111)
ax.set_xlim(0.5, maxTimeSteps + 0.5)
ax.set_ylim(maxNeurons - 0.5, -0.5)
pylab.title(label)
pylab.ylabel('Neuron Index')
pylab.xlabel('Time Step')
linewidth = 0.715 * fig.get_figwidth() * fig.get_dpi() / maxTimeSteps
for time in range(numTimeSteps):
x = []
y = []
colors = []
for neuron in range(numNeurons):
activ = line.split()
x.append(time + 1)
y.append(neuron - 0.5)
activation = float(activ[1])
if reverse:
activation = 1.0 - activation
if colorNeurons:
if neuron in range(redStart, redEnd + 1):
colors.append((activation, activation * ALT_COLOR_MAX,
activation * ALT_COLOR_MAX, 1.0))
elif neuron in range(greenStart, greenEnd + 1):
colors.append((activation * ALT_COLOR_MAX, activation,
activation * ALT_COLOR_MAX, 1.0))
elif neuron in range(blueStart, blueEnd + 1):
colors.append(
(activation * ALT_COLOR_MAX,
activation * ALT_COLOR_MAX, activation, 1.0))
elif neuron == eatNeuron:
colors.append((activation * ALT_COLOR_MAX, activation,
activation * ALT_COLOR_MAX, 1.0))
elif neuron == mateNeuron:
colors.append(
(activation * ALT_COLOR_MAX,
activation * ALT_COLOR_MAX, activation, 1.0))
elif neuron == fightNeuron:
colors.append((activation, activation * ALT_COLOR_MAX,
activation * ALT_COLOR_MAX, 1.0))
elif neuron == yawNeuron:
colors.append((activation, activation,
activation * ALT_COLOR_MAX, 1.0))
else:
colors.append((activation, activation, activation, 1.0))
else:
colors.append((activation, activation, activation, 1.0))
line = next_line(rhythm_file)
x.append(time + 1)
y.append(neuron + 0.5)
colors.append((activation, activation, activation, 1.0))
points = zip(x, y)
segments = zip(points[:-1], points[1:])
lc = LineCollection(segments, colors=colors)
lc.set_alpha(1.0)
lc.set_linewidth(linewidth)
lc.set_antialiased(False)
ax.add_collection(lc)
rhythm_file.close()
if behaviorLabels:
matplotlib.pyplot.text(maxTimeSteps + 1.5,
eatNeuron,
"Eat",
weight="ultralight",
size="small",
va="center")
matplotlib.pyplot.text(maxTimeSteps + 1.5,
mateNeuron,
"Mate",
weight="ultralight",
size="small",
va="center")
matplotlib.pyplot.text(maxTimeSteps + 1.5,
fightNeuron,
"Fight",
weight="ultralight",
size="small",
va="center")
matplotlib.pyplot.text(maxTimeSteps + 1.5,
moveNeuron,
"Move",
weight="ultralight",
size="small",
va="center")
matplotlib.pyplot.text(maxTimeSteps + 1.5,
yawNeuron,
"Turn",
weight="ultralight",
size="small",
va="center")
matplotlib.pyplot.text(maxTimeSteps + 1.5,
yawNeuron + 1,
"Light",
weight="ultralight",
size="small",
va="center")
matplotlib.pyplot.text(maxTimeSteps + 1.5,
yawNeuron + 2,
"Focus",
weight="ultralight",
size="small",
va="center")
if inputLabels:
matplotlib.pyplot.text(maxTimeSteps + 1.5,
0,
"Random",
weight="ultralight",
size="small",
va="center")
matplotlib.pyplot.text(maxTimeSteps + 1.5,
1,
"Health",
weight="ultralight",
size="small",
va="center")
for neuron in range(redStart, redEnd + 1):
matplotlib.pyplot.text(maxTimeSteps + 1.5,
neuron,
"R",
weight="ultralight",
size="small",
va="center")
for neuron in range(greenStart, greenEnd + 1):
matplotlib.pyplot.text(maxTimeSteps + 1.5,
neuron,
"G",
weight="ultralight",
size="small",
va="center")
for neuron in range(blueStart, blueEnd + 1):
matplotlib.pyplot.text(maxTimeSteps + 1.5,
neuron,
"B",
weight="ultralight",
size="small",
va="center")
def plotter(choice, numNeurons, numTimeSteps, maxNeurons, maxTimeSteps, reverse, numInputNeurons, colorNeurons, redStart, redEnd, greenStart, greenEnd, blueStart, blueEnd, eatNeuron, mateNeuron, fightNeuron, moveNeuron, yawNeuron, label, behaviorLabels, inputLabels):
rhythm_file = open_file(choice, "r")
line = next_line(rhythm_file)
line = next_line(rhythm_file)
figwidth = 12.0
figheight = 8.0
fig = pylab.figure(figsize=(figwidth,figheight))
ax = fig.add_subplot(111)
ax.set_xlim(0.5, maxTimeSteps+0.5)
ax.set_ylim(maxNeurons-0.5, -0.5)
pylab.title(label)
pylab.ylabel('Neuron Index')
pylab.xlabel('Time Step')
linewidth = 0.715 * fig.get_figwidth() * fig.get_dpi() / maxTimeSteps
for time in range(numTimeSteps):
x = []
y = []
colors = []
for neuron in range(numNeurons):
activ = line.split()
x.append(time+1)
y.append(neuron-0.5)
activation = float(activ[1])
if reverse:
activation = 1.0 - activation
if colorNeurons:
if neuron in range(redStart, redEnd+1):
colors.append((activation, activation*ALT_COLOR_MAX, activation*ALT_COLOR_MAX, 1.0))
elif neuron in range(greenStart, greenEnd+1):
colors.append((activation*ALT_COLOR_MAX, activation, activation*ALT_COLOR_MAX, 1.0))
elif neuron in range(blueStart, blueEnd+1):
colors.append((activation*ALT_COLOR_MAX, activation*ALT_COLOR_MAX, activation, 1.0))
elif neuron == eatNeuron:
colors.append((activation*ALT_COLOR_MAX, activation, activation*ALT_COLOR_MAX, 1.0))
elif neuron == mateNeuron:
colors.append((activation*ALT_COLOR_MAX, activation*ALT_COLOR_MAX, activation, 1.0))
elif neuron == fightNeuron:
colors.append((activation, activation*ALT_COLOR_MAX, activation*ALT_COLOR_MAX, 1.0))
elif neuron == yawNeuron:
colors.append((activation, activation, activation*ALT_COLOR_MAX, 1.0))
else:
colors.append((activation, activation, activation, 1.0))
else:
colors.append((activation, activation, activation, 1.0))
line = next_line(rhythm_file)
x.append(time+1)
y.append(neuron+0.5)
colors.append((activation, activation, activation, 1.0))
points = zip(x, y)
segments = zip(points[:-1], points[1:])
lc = LineCollection(segments, colors=colors)
lc.set_alpha(1.0)
lc.set_linewidth(linewidth)
lc.set_antialiased(False)
ax.add_collection(lc)
rhythm_file.close()
if behaviorLabels:
matplotlib.pyplot.text(maxTimeSteps+1.5, eatNeuron, "Eat", weight="ultralight", size="small", va="center")
matplotlib.pyplot.text(maxTimeSteps+1.5, mateNeuron, "Mate", weight="ultralight", size="small", va="center")
matplotlib.pyplot.text(maxTimeSteps+1.5, fightNeuron, "Fight", weight="ultralight", size="small", va="center")
matplotlib.pyplot.text(maxTimeSteps+1.5, moveNeuron, "Move", weight="ultralight", size="small", va="center")
matplotlib.pyplot.text(maxTimeSteps+1.5, yawNeuron, "Turn", weight="ultralight", size="small", va="center")
matplotlib.pyplot.text(maxTimeSteps+1.5, yawNeuron+1, "Light", weight="ultralight", size="small", va="center")
matplotlib.pyplot.text(maxTimeSteps+1.5, yawNeuron+2, "Focus", weight="ultralight", size="small", va="center")
if inputLabels:
matplotlib.pyplot.text(maxTimeSteps+1.5, 0, "Random", weight="ultralight", size="small", va="center")
matplotlib.pyplot.text(maxTimeSteps+1.5, 1, "Health", weight="ultralight", size="small", va="center")
for neuron in range(redStart, redEnd+1):
matplotlib.pyplot.text(maxTimeSteps+1.5, neuron, "R", weight="ultralight", size="small", va="center")
for neuron in range(greenStart, greenEnd+1):
matplotlib.pyplot.text(maxTimeSteps+1.5, neuron, "G", weight="ultralight", size="small", va="center")
for neuron in range(blueStart, blueEnd+1):
matplotlib.pyplot.text(maxTimeSteps+1.5, neuron, "B", weight="ultralight", size="small", va="center")
