def compute_traces(self, initial_condition=None, plotit=True): if initial_condition == None: initial_condition = self.initial_condition V_i = model.integrate_three_rk4( initial_condition, model.params['g_inh_0']*np.ones((6), float), self.system.dt/float(self.system.stride), self.system.N_output(self.CYCLES), self.system.stride) t = self.system.dt*np.arange(V_i.shape[0]) if plotit: ticks = np.asarray(t[::t.size/10], dtype=int) for i in xrange(self.num_osci): self.li[i].set_data(t, V_i[:, i]-i*2.) self.ax.set_xticks(ticks) self.ax.set_xticklabels(ticks) self.ax.set_xlim(t[0], t[-1]) self.fig.canvas.draw() return t, V_i
def computeTraces(self, initial_condition=None, plotit=True): if initial_condition == None: initial_condition = self.system.load_initial_condition(pl.rand(), pl.rand()) V_i = fh.integrate_three_rk4( initial_condition, self.network.coupling_strength, self.system.dt/float(self.system.stride), self.system.N_output(self.CYCLES), self.system.stride) t = self.system.dt*np.arange(V_i.shape[0]) if plotit: ticks = np.asarray(t[::t.size/10], dtype=int) self.li_b.set_data(t, V_i[:, 0]) self.li_g.set_data(t, V_i[:, 1]-2.) self.li_r.set_data(t, V_i[:, 2]-4.) self.ax.set_xticks(ticks) self.ax.set_xticklabels(ticks) self.ax.set_xlim(t[0], t[-1]) self.fig.canvas.draw() return t, V_i
def computeTraces(self, initial_condition=None, plotit=True): if initial_condition == None: initial_condition = self.system.load_initial_condition(pl.rand(), pl.rand()) V_i = fh.integrate_three_rk4( initial_condition, self.network.coupling_strength, self.system.dt/float(self.system.stride), self.system.N_output(self.CYCLES), self.system.stride) t = self.system.dt*np.arange(V_i.shape[0]) if plotit: ticks = np.asarray(t[::t.size/10], dtype=int) xscale, yscale = t[-1], 2. for (i, li) in enumerate([self.li_b, self.li_g, self.li_r]): tj, Vj = tl.adjustForPlotting(t, V_i[:, i], ratio=xscale/yscale, threshold=0.05*xscale) li.set_data(tj, Vj-i*2) self.ax.set_xticks(ticks) self.ax.set_xticklabels(ticks) self.ax.set_xlim(t[0], t[-1]) self.fig.canvas.draw() return t, V_i
def compute_traces(self, initial_condition=None, plotit=True): if initial_condition == None: initial_condition = self.initial_condition V_i = fh.integrate_three_rk4( initial_condition, self.network.coupling_strength, self.system.dt/float(self.system.stride), self.system.N_output(self.CYCLES), self.system.stride) t = self.system.dt*np.arange(V_i.shape[0]) if plotit: ticks = np.asarray(t[::t.size/10], dtype=int) self.li_b.set_data(t, V_i[:, 0]) self.li_g.set_data(t, V_i[:, 1]-2.) self.li_r.set_data(t, V_i[:, 2]-4.) self.ax.set_xticks(ticks) self.ax.set_xticklabels(ticks) self.ax.set_xlim(t[0], t[-1]) self.fig.canvas.draw() return t, V_i
def compute_traces(self, initial_condition=None, plotit=True): if initial_condition == None: initial_condition = self.initial_condition V_i = model.integrate_three_rk4( initial_condition, model.params['g_inh_0'] * np.ones((6), float), self.system.dt / float(self.system.stride), self.system.N_output(self.CYCLES), self.system.stride) t = self.system.dt * np.arange(V_i.shape[0]) if plotit: ticks = np.asarray(t[::t.size / 10], dtype=int) for i in xrange(self.num_osci): self.li[i].set_data(t, V_i[:, i] - i * 2.) self.ax.set_xticks(ticks) self.ax.set_xticklabels(ticks) self.ax.set_xlim(t[0], t[-1]) self.fig.canvas.draw() return t, V_i