我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用matplotlib.cm.cool()。
def conv1(model): n1, n2, x, y, z = model.conv1.W.shape fig = plt.figure() for nn in range(0, n1): ax = fig.add_subplot(4, 5, nn+1, projection='3d') ax.set_xlim(0.0, x) ax.set_ylim(0.0, y) ax.set_zlim(0.0, z) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_zticklabels([]) for xx in range(0, x): for yy in range(0, y): for zz in range(0, z): max = np.max(model.conv1.W.data[nn, :]) min = np.min(model.conv1.W.data[nn, :]) step = (max - min) / 1.0 C = (model.conv1.W.data[nn, 0, xx, yy, zz] - min) / step color = cm.cool(C) C = abs(1.0 - C) ax.plot(np.array([xx]), np.array([yy]), np.array([zz]), "o", color=color, ms=7.0*C, mew=0.1) plt.savefig("result/graph_conv1.png")
def conv2(model): n1, n2, x, y, z = model.conv2.W.shape for nn in range(0, n1): fig = plt.figure() for mm in range(0, n2): ax = fig.add_subplot(4, 5, mm + 1, projection='3d') ax.set_xlim(0.0, x) ax.set_ylim(0.0, y) ax.set_zlim(0.0, z) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_zticklabels([]) for xx in range(0, x): for yy in range(0, y): for zz in range(0, z): max = np.max(model.conv2.W.data[nn, mm:]) min = np.min(model.conv2.W.data[nn, mm:]) step = (max - min) / 1.0 C = (model.conv2.W.data[nn, mm, xx, yy, zz] - min) / step color = cm.cool(C) C = abs(1.0-C) ax.plot(np.array([xx]), np.array([yy]), np.array([zz]), "o", color=color, ms=7.0 * C, mew=0.1) plt.savefig("result/graph_conv2_" + str(nn) + ".png")
def plot_3d(objective_function, length=20): """ Plot 3D functions :param objective_function: :type objective_function: :param length: :type length: :return: :rtype: """ bounds = objective_function.get_bounds() if len(bounds) != 2: return x_grid = np.linspace(bounds[0][0], bounds[0][1], length) y_grid = np.linspace(bounds[1][0], bounds[1][1], length) x_grid, y_grid = np.meshgrid(x_grid, y_grid) grid = np.vstack((x_grid.flatten(), y_grid.flatten())).T z_points = objective_function.evaluate(grid) z_points = z_points.reshape(length, length) fig = pyplot.figure() axis = fig.gca(projection='3d') surf = axis.plot_surface(x_grid, y_grid, z_points, rstride=1, cstride=1, cmap=cm.cool, linewidth=0, antialiased=False, alpha=0.3) axis.contour(x_grid.tolist(), y_grid.tolist(), z_points.tolist(), zdir='z', offset=z_points.min(), cmap=cm.cool) axis.set_xlim(bounds[0][0], bounds[0][1]) axis.set_ylim(bounds[1][0], bounds[1][1]) pyplot.title(objective_function.__class__.__name__) axis.zaxis.set_major_locator(LinearLocator(10)) axis.zaxis.set_major_formatter(FormatStrFormatter('%.02f')) fig.colorbar(surf, shrink=0.5, aspect=5) pyplot.show()
def report_w3d(self, my_ae): for period in range(np.int(np.log2(my_ae.epoch_limit) + 1)): targetW = my_ae.get_W1(period) x = range(27) y = range(27) ax3d = plt.subplot(self.gs[period, 2]) X, Y = np.meshgrid(x, y) Z = targetW[2][X + (784 - 28) - Y * 28] fig = plt.figure() #ax = Axes3D(fig) ax = fig.gca(projection='3d') # ax.plot_wireframe(X,Y,Z) plt.cool() cset = ax.contourf(X, Y, Z, zdir='z', offset=-4, cmap=cm.coolwarm) cset = ax.contourf(X, Y, Z, zdir='x', offset=-1, cmap=cm.cool) cset = ax.contourf(X, Y, Z, zdir='y', offset=-1, cmap=cm.cool) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, alpha=0.3) # ax.contourf3D(X,Y,Z) # surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, # linewidth=0, antialiased=False) # fig.colorbar(surf, shrink=0.5, aspect=5) ax.view_init(20, 30) ax.set_xlabel('X') ax.set_xlim(0, 27) ax.set_ylabel('Y') ax.set_ylim(0, 27) ax.set_zlabel('Z') ax.set_zlim(-4, 3) plt.show()
def tuneplot(ax1, ax2, data, particleIDs='allIDs', integer=1, addsub=add, clipint=True, showlost=False, QQ='Qx', ms=1, clip=[0], showfit=False): particleIDs = data[particleIDs] if not showlost: lost = data['lost'][:, 0] clip = concatenate([clip, lost]) particleIDs = delete(particleIDs, clip) Q = addsub(integer, data[QQ][particleIDs]) if clipint: zeroQ = find(logical_or(logical_or(Q == 0.0, Q == 1.0), Q == 0.5)) if len(zeroQ) > 0: # trim reference particle with zero tune Q = delete(Q, zeroQ) particleIDs = delete(particleIDs, zeroQ) Qmin, Qmax = nanmin(Q), nanmax(Q) Qdif = Qmax - Qmin if Qdif == 0.0: Qmin -= Qmin/1e4 Qmax += Qmax/1e4 Qdif = Qmax - Qmin colors = cool((Q - Qmin) / Qdif) for i, ID in enumerate(particleIDs): ax1.plot(data['x'][:, ID]*1e3, data['xp'][:, ID]*1e3, '.', c=colors[i], ms=ms) if showlost: for ID in lost: ax1.plot(data['x'][:, ID]*1e3, data['xp'][:, ID]*1e3, '.', c='gray', ms=ms) sm = ScalarMappable(cmap=rainbow, norm=Normalize(vmin=Qmin, vmax=Qmax)) sm._A = [] ax1.set_xlabel(r'Position $x$ / (mm)') ax1.set_ylabel(r'Angle $x^\prime$ / (mrad)') emittance = data['A'][particleIDs]/pi action = emittance/2 # tune shift with action fitfun = lambda x, a, b: a + b*x popt, pcov = curve_fit(fitfun, action, Q) perr = sqrt(diag(pcov)) action2 = linspace(nanmin(action), nanmax(action), 1000) fit1 = fitfun(action2, *popt) print(popt[1]*1e-6*1250) for i, ID in enumerate(particleIDs): ax2.plot(action[i]*1e6, Q[i], 'o', c=colors[i], ms=ms + 1) if showfit: ax2.plot(action2*1e6, fit1, '-k', lw=1, label=r'fit with $TSWA=${:.4}$\pm${:.1} (kHz mm$^-$$^2$mrad$^-$$^2$)'.format(popt[1]*1e-6*1250, perr[1]*1e-6*1250)) # leg = ax2.legend() # leg.get_frame().set_alpha(0) ax2.set_ylim([Qmin, Qmax]) # ax2.yaxis.tick_right() ax2.set_ylabel(r'Fractional Tune $dQ$') # ax2.yaxis.set_label_position('right') ax2.set_xlabel(r'Action $J_x$ / (mm$\cdot$mrad)') tight_layout() return
