def error_over_speed(tau, ws):
speeds = np.arange(0.1, 0.9, 0.1)
y = []
for s in speeds:
tg_var = TARGET(speed=s,
max_speed=0.8,
size=20,
peak_cell_num=peak_cell_num)
can = CAN(target=tg_var,
size=20,
tau=tau,
dt=tau / 10,
kappa=0.1,
beta=-8,
ws_param=ws,
h=0)
can.init_network_activity(peak_cell_num=peak_cell_num, init_time=1)
can.run(sim_time=20)
y.append(can.slope_accuracy(s, 20, peak_cell_num))
fig, ax = plt.subplots()
ax.xaxis.set_ticks_position('bottom')
ax.set_title("Error in relation to speed [Tau=" + str(tau) + "][WS=" +
str(ws) + "]")
ax.set_xlabel("Speed [m/s]")
ax.set_ylabel("Error")
plt.plot(speeds, y, 'r-')
plt.ylim(-20, 20)
def multiple_runs(overwrite_file):
x = np.arange(0.01, 0.35, 0.01) #Value range for tau
y = np.arange(0.01, 0.51, 0.01) #Value range for ws_param
delta_t = x / 10
if os.path.isfile(str(speed) +
'-Log.csv') == False or overwrite_file == True:
print("No existing data file found;\nCalculating new data set..")
mat_vals = np.empty((len(y), len(x)))
progress = len(y) * len(x) / 100
counter = 0
for j in range(0, len(x)):
for i in range(0, len(y)):
can = CAN(target=tg,
size=20,
tau=x[j],
dt=delta_t[j],
kappa=0.1,
beta=-8,
ws_param=y[i],
h=0)
can.init_network_activity(peak_cell_num=peak_cell_num,
init_time=1)
can.run(sim_time=20)
result = can.slope_accuracy(speed, 20, peak_cell_num)
mat_vals[i, j] = result
counter += 1
print(counter / progress, "%")
np.savetxt(str(speed) + '-Log.csv', mat_vals, delimiter=',')
else:
print("Loading from existing data file..")
mat_vals = np.loadtxt(str(speed) + '-Log.csv', delimiter=',')
#print(mat_vals)
mat_vals = np.clip(mat_vals, -100, 100)
fig, ax = plt.subplots()
mat = ax.matshow(mat_vals,
origin='lower',
cmap='seismic',
aspect='auto',
extent=(0.01 - 0.01 / 2, 0.34 + 0.01 / 2, 0.01 - 0.01 / 2,
0.5 + 0.01 / 2))
ax.xaxis.set_ticks_position('bottom')
ax.set_title("Error of calculated slope to expected slope for " +
str(speed) + " m/s")
ax.set_xlabel("Tau [s]")
ax.set_ylabel("Pi factor")
ax.set_facecolor('tab:gray')
ax.grid()
cb = plt.colorbar(mat, ax=ax)
cb.set_label("Error value")