(Not sure if seeking help for matplotlib is appropriate here but I thought I would give it a try; if not, please direct me to somewhere I can get that assistance. Anyway...) Alright, I'm trying to run the code listed below. It results in the following color plot: http://s14.postimg.org/m16s2mmox/figure_1.png I need help resolving the x-axis labels, which are the right number but in the wrong location. I need them to align evenly with the columns, not be scaled proportionally to their actual value. I.e., if I were to plot 0.5, 0.6, 0.9, and 2.0 on a real number line from 0 to 4, that is exactly where they would land, but I don't want that. How do I get them to space evenly? Also, once I scale this up to a much, much finer resolution (i.e., many more rows and columns), which I will do once I've debugged this, it's not going to try to plot every single value on the x-axis, will it? If so, how would I fix that to print, say, every 10th value, or a total of only 5 values? Here is the code. Portions of this simulator not used for this run have been removed for brevity: ##SIMULATOR import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from scipy.integrate import odeint #Network constants tau = 20; tau_S = 10000 g_Ca = 3.6; g_K = 10; g_S = 4 #Siemens E_Ca = 25; E_K = -75; E_S = -75 #mV E_half_m = -20; E_half_n = -16; E_half_S = -35.245 #mV E_exc = 0; E_inh = -75 #mV k_m = 12; k_n = 5.6; k_S = 10 #(unitless) Theta_syn = -40 #mV #Initial conditions V0_0 = -60 n0 = np.array([0.6, 0.6]) S0 = np.array([0.1, 0.1]) #Simulation constants stopTime = 30000/100; timeStep = 4 #ms tStartTrack = 2000/100 #Time when Lyapunov exponent calculation begins. nStartTrack = int(tStartTrack/timeStep) #init step for Lyap expo #Plotting constants doNorm = True; V1VsV2 = False; nVsV = False; timeSeries = True pigments = ['r', 'g', 'b'] #Plotting colors. toPlot = [0, 1] #Indeces of the neurons to plot, starting at 0. #Heaviside function. Currently approximated; can be optimised. #Continuous equation: Gamma(x) = 1.0/(1 + np.exp(-100*(x - Theta_syn))) def Heaviside(x): mult = -100.0; diff = mult*(x - Theta_syn); output = np.ones_like(x) #Initialized to 1's. output[x < Theta_syn] = 0 #0 <= x < Theta_syn. output[abs(diff) < 50] = 1.0/(1 + np.exp(diff[abs(diff) < 50])) return output #ODE for Sherman model (Belykh and Reimbayev) def f(X, t, params): N = len(X)/3 V = X[:N]; n = X[N:2*N]; S = X[2*N:3*N] m_inf_E = 1/(1 + np.exp((E_half_m - V)/k_m)) n_inf_E = 1/(1 + np.exp((E_half_n - V)/k_n)) S_inf_E = 1/(1 + np.exp((E_half_S - V)/k_S)) I_Ca = g_Ca * m_inf_E * (V - E_Ca) I_K = g_K * n * (V - E_K) I_S = g_S * S * (V - E_S) dV = (-I_Ca - I_K - I_S)/tau +\ g_inh*(E_inh - V)*Heaviside(np.dot(V, connex))/tau dn = (n_inf_E - n)/tau dS = (S_inf_E - S)/tau_S return np.concatenate((dV, dn, dS)) # else: #if not useForLoops # g_inhibitory = params[0]; V1 = params[1] #Connection matrix. Simulator starts here. connex = np.array([[-1,1], [1,-1]]) inhibID = 7 #Used for below to avoid commenting out code. #List of the inhibitory conductivities (g_inh) to track: if inhibID == 7: inhibConductivity = np.array([0.5, 0.6, 0.9, 2.0]) voltageID = 5 #Used for below to avoid commenting out code. #Starting V1: if voltageID == 5: startingVoltage1 = np.arange(V0_0 + 10, V0_0 + 30, 5) Vsize = startingVoltage1.size #Whether the g_inh and V1 arrays have just one element: justOneRun = (Vsize == 1 and inhibConductivity.size == 1) t = np.arange(0, stopTime, timeStep) #useForLoops = True #if useForLoops: normVsVSpread = np.zeros((Vsize, inhibConductivity.size)) for i in range(inhibConductivity.size): g_inh = inhibConductivity[i] for j in range(Vsize): pctComplete = 100*((1.*i + j/Vsize)/inhibConductivity.size) #Integration and initial conditions V0 = np.array([V0_0, startingVoltage1[j]]); N = len(V0) #Solver soln = odeint(f, np.concatenate((V0, n0, S0)), t, args=(None,)) E = np.transpose(soln[:, :N]) n = np.transpose(soln[:, N:N*2]) VArray = E[:2, nStartTrack:] maxVSpread = np.amax(VArray) - np.amin(VArray) maxNorm = np.amax(abs(VArray[1] - VArray[0])) normVsVSpread[j, i] = maxNorm/maxVSpread #else: #DO NOT USE YET # gInhVsV1 = np.meshgrid(inhibConductivity, startingVoltage1) # V0 = np.array([V0_0, None]) #V1 will be dealt with inside the ODE # soln = odeint(f, np.concatenate((V0, n0, S0)), t, args=(gInhVsV1,)) #Single-run plotting strParameters = 'V0_0 = ' + str(V0[0]) + ' mV; n0 = ' + str(n0) +\ '; S0 = ' + str(S0) mpl.rcParams['image.cmap'] = 'gist_heat' fig = plt.subplots() plt.title('|V1 - V0|/(Vmax - Vmin).\n' + strParameters) plt.xlabel('g_inh (Siemens)') plt.ylabel('V1 (mV)') # xticks = np.linspace(np.amin(inhibConductivity),\ # np.amax(inhibConductivity), num = 4) # plt.xticks(xticks) plt.xticks(inhibConductivity) # plt.yticks(startingVoltage1) plt.pcolormesh(normVsVSpread) plt.colorbar(shrink=0.8) plt.show()