def overlay_contour_plot(prefix,
q1,
q2,
lead_q1_q2_trials_dyn,
follows_q1_q2_trials_nrmse,
xlabel,
ylabel,
logx=False,
logy=False,
logz=False,
levels="auto",
levels2="auto"):
"""
"""
# Lead contour
X, Y = np.meshgrid(q1, q2)
Z = np.zeros((len(q2), len(q1)))
for i, listQ2_listTrials_nrmse in enumerate(lead_q1_q2_trials_dyn):
for j, listTrials_nrmse in enumerate(listQ2_listTrials_nrmse):
Z[j, i] = np.mean(listTrials_nrmse)
plt.clf()
# fig, ax = plt.subplots()
# ax.ticklabel_format(useOffset=False,style='plain')
if levels is "auto":
plt.contourf(X, Y, Z, cmap=cm.magma)
else:
plt.contourf(X, Y, Z, cmap=cm.magma, levels=levels)
# cbar = plt.colorbar()
# cbar.set_label('${NRMSE}$')
color_list = ['r', 'b', 'g']
for l, follow in enumerate(follows_q1_q2_trials_nrmse):
Z = np.zeros((len(q2), len(q1)))
for i, listQ2_listTrials_nrmse in enumerate(follow):
for j, listTrials_nrmse in enumerate(listQ2_listTrials_nrmse):
Z[j, i] = np.mean(listTrials_nrmse)
if levels2 is "auto":
CS = plt.contour(X, Y, Z, colors=color_list[l])
else:
CS = plt.contour(X, Y, Z, colors=color_list[l], levels=levels2)
plt.clabel(CS, fontsize=10, inline=1)
plt.xlabel(xlabel, fontsize=30)
plt.ylabel(ylabel, fontsize=30)
if logx:
plt.xscale('log')
if logy:
plt.yscale('log')
if logz:
plt.zscale('log')
plt.tight_layout()
plt.savefig(prefix + '_q1_q2_nrmse_contour.png')
plt.clf()
plt.close()
def plot_dynrange_contour(prefix,
Q1,
Q2,
Aq1_Aq2_Asample_Arates_p,
Aq1_Aq2_Asample_Arates_F,
xlabel,
ylabel,
logx=False,
logy=False,
logz=False,
levels=None):
"""
plot the dynamic rage averaged over a number of sample graphs versus two variables
"""
X, Y = np.meshgrid(Q1, Q2)
Z = np.zeros((len(Q2), len(Q1)))
for i in range(Aq1_Aq2_Asample_Arates_p.shape[0]):
for j in range(Aq1_Aq2_Asample_Arates_p.shape[1]):
dynranges = []
for k in range(Aq1_Aq2_Asample_Arates_p.shape[2]):
dynrange, _, _ = calc_dynamical_range(
Aq1_Aq2_Asample_Arates_p[i, j, k],
Aq1_Aq2_Asample_Arates_F[i, j, k])
dynranges.append(dynrange)
Z[j, i] = np.mean(dynranges)
plt.clf()
if levels == None:
plt.contourf(X, Y, Z, cmap=cm.magma)
else:
plt.contourf(X, Y, Z, cmap=cm.magma, levels=levels)
cbar = plt.colorbar()
cbar.set_label('$\Delta$')
plt.xlabel(xlabel, fontsize=30)
plt.ylabel(ylabel, fontsize=30)
if logx:
plt.xscale('log')
if logy:
plt.yscale('log')
if logz:
plt.zscale('log')
plt.tight_layout()
plt.savefig(prefix + '_q1_q2_delta_contour.png')
plt.clf()
plt.close()
def performance_contour_plot(prefix,
q1,
q2,
listQ1_listQ2_listTrials_nrmse,
xlabel,
ylabel,
zlabel,
logx=False,
logy=False,
logz=False,
levels="auto"):
X, Y = np.meshgrid(q1, q2)
Z = np.zeros((len(q2), len(q1)))
for i, listQ2_listTrials_nrmse in enumerate(
listQ1_listQ2_listTrials_nrmse):
for j, listTrials_nrmse in enumerate(listQ2_listTrials_nrmse):
# print listTrials_nrmse, X[j,i], Y[j,i])
Z[j, i] = np.mean(listTrials_nrmse)
plt.clf()
# fig, ax = plt.subplots()
# ax.ticklabel_format(useOffset=False,style='plain')
if levels is "auto":
plt.contourf(X, Y, Z, cmap=cm.magma)
else:
plt.contourf(X, Y, Z, cmap=cm.magma, levels=levels)
cbar = plt.colorbar()
cbar.set_label(zlabel) #"${NRMSE}$"
plt.xlabel(xlabel, fontsize=30)
plt.ylabel(ylabel, fontsize=30)
if logx:
plt.xscale('log')
if logy:
plt.yscale('log')
if logz:
plt.zscale('log')
plt.tight_layout()
plt.savefig(prefix + '_q1_q2_mc_contour.png')
plt.clf()
plt.close()
def contour_plot(prefix,
q1,
q2,
listQ1_listQ2_listTrials_val,
xlabel,
ylabel,
logx=False,
logy=False,
logz=False,
levels=None,
zlabel=None):
X, Y = np.meshgrid(q1, q2)
Z = np.zeros((len(q2), len(q1)))
for i, listQ2_listTrials_val in enumerate(listQ1_listQ2_listTrials_val):
for j, listTrials_val in enumerate(listQ2_listTrials_val):
Z[j, i] = np.mean(listTrials_val)
plt.clf()
# fig, ax = plt.subplots()
# ax.ticklabel_format(useOffset=False,style='plain')
if levels == None:
plt.contourf(X, Y, Z, cmap=cm.magma)
else:
plt.contourf(X, Y, Z, cmap=cm.magma, levels=levels)
cbar = plt.colorbar()
if zlabel:
cbar.set_label(zlabel)
plt.xlabel(xlabel, fontsize=30)
plt.ylabel(ylabel, fontsize=30)
if logx:
plt.xscale('log')
if logy:
plt.yscale('log')
if logz:
plt.zscale('log')
plt.tight_layout()
plt.savefig(prefix + '_q1_q2_contour.png')
plt.clf()
plt.close()
def plot_performance_contour(prefix,
q1,
q2,
listQ1_listQ2_listTrials_mc,
xlabel,
ylabel,
logx=False,
logy=False,
logz=False,
levels=None):
X, Y = np.meshgrid(q1, q2)
Z = np.zeros((len(q2), len(q1)))
for i, listQ2_listTrials_mc in enumerate(listQ1_listQ2_listTrials_mc):
for j, listTrials_mc in enumerate(listQ2_listTrials_mc):
Z[j, i] = np.mean(
listTrials_mc) # Take average over all signal inputs
plt.clf()
if levels == None:
plt.contourf(X, Y, Z, cmap=cm.magma)
else:
plt.contourf(X, Y, Z, cmap=cm.magma, levels=levels)
cbar = plt.colorbar()
cbar.set_label('${MC}$')
plt.xlabel(xlabel, fontsize=30)
plt.ylabel(ylabel, fontsize=30)
if logx:
plt.xscale('log')
if logy:
plt.yscale('log')
if logz:
plt.zscale('log')
plt.tight_layout()
plt.savefig(prefix + '_q1_q2_mc_contour.png')
plt.clf()
plt.close()