我们从Python开源项目中，提取了以下14个代码示例，用于说明如何使用pylab.rc()。
def ezrc(fontSize=22., lineWidth=2., labelSize=None, tickmajorsize=10, tickminorsize=5, figsize=(8, 6)): """ slides - Define params to make pretty fig for slides """ from pylab import rc, rcParams if labelSize is None: labelSize = fontSize + 5 rc('figure', figsize=figsize) rc('lines', linewidth=lineWidth) rcParams['grid.linewidth'] = lineWidth rcParams['font.sans-serif'] = ['Helvetica'] rcParams['font.serif'] = ['Helvetica'] rcParams['font.family'] = ['Times New Roman'] rc('font', size=fontSize, family='serif', weight='bold') rc('axes', linewidth=lineWidth, labelsize=labelSize) rc('legend', borderpad=0.1, markerscale=1., fancybox=False) rc('text', usetex=True) rc('image', aspect='auto') rc('ps', useafm=True, fonttype=3) rcParams['xtick.major.size'] = tickmajorsize rcParams['xtick.minor.size'] = tickminorsize rcParams['ytick.major.size'] = tickmajorsize rcParams['ytick.minor.size'] = tickminorsize rcParams['text.latex.preamble'] = ["\\usepackage{amsmath}"]
def setMargins(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None): """ Tune the subplot layout via the meanings (and suggested defaults) are:: left = 0.125 # the left side of the subplots of the figure right = 0.9 # the right side of the subplots of the figure bottom = 0.1 # the bottom of the subplots of the figure top = 0.9 # the top of the subplots of the figure wspace = 0.2 # the amount of width reserved for blank space between subplots hspace = 0.2 # the amount of height reserved for white space between subplots The actual defaults are controlled by the rc file """ plt.subplots_adjust(left, bottom, right, top, wspace, hspace) plt.draw_if_interactive()
def plot_trajectories(self): pylab.clf() pylab.rc('text', usetex=True) pylab.rc('font', size=18) pylab.subplot(121) self.plot_com() pylab.subplot(122) self.plot_zmp()
def plot_measurements(time_points, ydata): pl.rc("text", usetex = True) pl.rc("font", family="serif") pl.subplot2grid((4, 2), (0, 0)) pl.plot(time_points, ydata[:,0]) pl.title("Considered measurement data") pl.xlabel("t") pl.ylabel("X", rotation = 0, labelpad = 20) pl.subplot2grid((4, 2), (1, 0)) pl.plot(time_points, ydata[:,1]) pl.xlabel("t") pl.ylabel("Y", rotation = 0, labelpad = 15) pl.subplot2grid((4, 2), (2, 0)) pl.plot(time_points, ydata[:,2]) pl.xlabel("t") pl.ylabel(r"\phi", rotation = 0, labelpad = 15) pl.subplot2grid((4, 2), (3, 0)) pl.plot(time_points, ydata[:,3]) pl.xlabel("t") pl.ylabel("v", rotation = 0, labelpad = 20) pl.subplot2grid((4, 2), (0, 1), rowspan = 4) pl.plot(ydata[:,0], ydata[:, 1]) pl.title("Considered racecar track (measured)") pl.xlabel("X") pl.ylabel("Y", rotation = 0, labelpad = 20) pl.show()
def plot_simulation_results_initial_controls(time_points, y_sim): pl.rc("text", usetex = True) pl.rc("font", family="serif") pl.subplot2grid((4, 2), (0, 0)) pl.plot(time_points, y_sim[:,0]) pl.title("Simulation results for initial controls") pl.xlabel("t") pl.ylabel("X", rotation = 0, labelpad = 20) pl.subplot2grid((4, 2), (1, 0)) pl.plot(time_points, y_sim[:,1]) pl.xlabel("t") pl.ylabel("Y", rotation = 0, labelpad = 15) pl.subplot2grid((4, 2), (2, 0)) pl.plot(time_points, y_sim[:,2]) pl.xlabel("t") pl.ylabel(r"\phi", rotation = 0, labelpad = 15) pl.subplot2grid((4, 2), (3, 0)) pl.plot(time_points, y_sim[:,3]) pl.xlabel("t") pl.ylabel("v", rotation = 0, labelpad = 20) pl.subplot2grid((4, 2), (0, 1), rowspan = 4) pl.plot(y_sim[:,0], y_sim[:, 1]) pl.title("Simulated race car path for initial controls") pl.xlabel("X") pl.ylabel("Y", rotation = 0, labelpad = 20) pl.show()
def drawDf(pl, df, title=''): pl.figure if title != '': if agl.is_utf8(title): title = title.decode('utf8') #pl.title(title, fontproperties=getFont()) if not isinstance(df, type(None)): pl.rc('font', family='simhei') df.plot(title=title) pl.show() pl.close()
def drawTwoDf(pl, df1, df2, title=''): """?????df""" pl.figure pl.rc('font', family='simhei') if title != '': if agl.is_utf8(title): title = title.decode('utf8') fig = pl.gcf() ax1 = fig.add_subplot(211) df1.plot(ax=ax1, title=title) ax2 = fig.add_subplot(212) df2.plot(ax=ax2) pl.show() pl.close()
def rc(self, *args, **kwargs): pl.rc(*args, **kwargs)
def showFourier(self): psd2D = np.log(np.abs(self.four)**2+1) (height,width) = psd2D.shape py.figure(figsize=(10,10*height/width),facecolor='white') py.clf() py.rc('text',usetex=True) py.xlabel(r'$\omega_1$',fontsize=24) py.ylabel(r'$\omega_2$',fontsize=24) py.xticks(fontsize=16) py.yticks(fontsize=16) py.imshow( psd2D, cmap='Greys_r',extent=[-pi,pi,-pi,pi],aspect='auto') py.show()
def analyse_(self, inputs, outputs, idx2word, inputs_unk=None, return_attend=False, name=None, display=False): def cut_zero(sample, idx2word, ppp=None, Lmax=None): if Lmax is None: Lmax = self.config['dec_voc_size'] if ppp is None: if 0 not in sample: return ['{}'.format(idx2word[w].encode('utf-8')) if w < Lmax else '{}'.format(idx2word[inputs[w - Lmax]].encode('utf-8')) for w in sample] return ['{}'.format(idx2word[w].encode('utf-8')) if w < Lmax else '{}'.format(idx2word[inputs[w - Lmax]].encode('utf-8')) for w in sample[:sample.index(0)]] else: if 0 not in sample: return ['{0} ({1:1.1f})'.format( idx2word[w].encode('utf-8'), p) if w < Lmax else '{0} ({1:1.1f})'.format( idx2word[inputs[w - Lmax]].encode('utf-8'), p) for w, p in zip(sample, ppp)] idz = sample.index(0) return ['{0} ({1:1.1f})'.format( idx2word[w].encode('utf-8'), p) if w < Lmax else '{0} ({1:1.1f})'.format( idx2word[inputs[w - Lmax]].encode('utf-8'), p) for w, p in zip(sample[:idz], ppp[:idz])] if inputs_unk is None: result, _, ppp = self.generate_(inputs[None, :], return_attend=return_attend) else: result, _, ppp = self.generate_(inputs_unk[None, :], return_attend=return_attend) source = '{}'.format(' '.join(cut_zero(inputs.tolist(), idx2word, Lmax=len(idx2word)))) target = '{}'.format(' '.join(cut_zero(outputs.tolist(), idx2word, Lmax=len(idx2word)))) decode = '{}'.format(' '.join(cut_zero(result, idx2word))) if display: print source print target print decode idz = result.index(0) p1, p2 = [np.asarray(p) for p in zip(*ppp)] print p1.shape import pylab as plt # plt.rc('text', usetex=True) # plt.rc('font', family='serif') visualize_(plt.subplots(), 1 - p1[:idz, :].T, grid=True, name=name) visualize_(plt.subplots(), 1 - p2[:idz, :].T, name=name) # visualize_(plt.subplots(), 1 - np.mean(p2[:idz, :], axis=1, keepdims=True).T) return target == decode
def analyse_(self, inputs, outputs, idx2word, inputs_unk=None, return_attend=False, name=None, display=False): def cut_zero(sample, idx2word, ppp=None, Lmax=None): if Lmax is None: Lmax = self.config['dec_voc_size'] if ppp is None: if 0 not in sample: return ['{}'.format(idx2word[w].encode('utf-8')) if w < Lmax else '{}'.format(idx2word[inputs[w - Lmax]].encode('utf-8')) for w in sample] return ['{}'.format(idx2word[w].encode('utf-8')) if w < Lmax else '{}'.format(idx2word[inputs[w - Lmax]].encode('utf-8')) for w in sample[:sample.index(0)]] else: if 0 not in sample: return ['{0} ({1:1.1f})'.format( idx2word[w].encode('utf-8'), p) if w < Lmax else '{0} ({1:1.1f})'.format( idx2word[inputs[w - Lmax]].encode('utf-8'), p) for w, p in zip(sample, ppp)] idz = sample.index(0) return ['{0} ({1:1.1f})'.format( idx2word[w].encode('utf-8'), p) if w < Lmax else '{0} ({1:1.1f})'.format( idx2word[inputs[w - Lmax]].encode('utf-8'), p) for w, p in zip(sample[:idz], ppp[:idz])] if inputs_unk is None: result, _, ppp = self.generate_(inputs[None, :], return_attend=return_attend) else: result, _, ppp = self.generate_(inputs_unk[None, :], return_attend=return_attend) source = '{}'.format(' '.join(cut_zero(inputs.tolist(), idx2word, Lmax=len(idx2word)))) target = '{}'.format(' '.join(cut_zero(outputs.tolist(), idx2word, Lmax=len(idx2word)))) decode = '{}'.format(' '.join(cut_zero(result, idx2word))) if display: print(source) print(target) print(decode) idz = result.index(0) p1, p2 = [np.asarray(p) for p in zip(*ppp)] print(p1.shape) import pylab as plt # plt.rc('text', usetex=True) # plt.rc('font', family='serif') visualize_(plt.subplots(), 1 - p1[:idz, :].T, grid=True, name=name) visualize_(plt.subplots(), 1 - p2[:idz, :].T, name=name) # visualize_(plt.subplots(), 1 - np.mean(p2[:idz, :], axis=1, keepdims=True).T) return target == decode
def plot_simulation_results_initial_and_optimized_controls(time_points, \ y_sim_init, y_sim_opt): pl.rc("text", usetex = True) pl.rc("font", family="serif") pl.subplot2grid((4, 2), (0, 0)) pl.plot(time_points, y_sim_init[:,0], label = "initial") pl.plot(time_points, y_sim_opt[:,0], label = "optimized") pl.title("Simulation results for initial and optimized control") pl.xlabel("$t$") pl.ylabel("$X$", rotation = 0) pl.legend(loc = "lower left") pl.subplot2grid((4, 2), (1, 0)) pl.plot(time_points, y_sim_init[:,1], label = "initial") pl.plot(time_points, y_sim_opt[:,1], label = "optimized") pl.xlabel("$t$") pl.ylabel("$Y$", rotation = 0) pl.legend(loc = "lower left") pl.subplot2grid((4, 2), (2, 0)) pl.plot(time_points, y_sim_init[:,2], label = "initial") pl.plot(time_points, y_sim_opt[:,2], label = "optimized") pl.xlabel("$t$") pl.ylabel("$\psi$", rotation = 0) pl.legend(loc = "lower left") pl.subplot2grid((4, 2), (3, 0)) pl.plot(time_points, y_sim_init[:,3], label = "initial") pl.plot(time_points, y_sim_opt[:,3], label = "optimized") pl.xlabel("$t$") pl.ylabel("$v$", rotation = 0) pl.legend(loc = "upper left") pl.subplot2grid((4, 2), (0, 1), rowspan = 4) pl.plot(y_sim_init[:,0], y_sim_init[:,1], label = "initial") pl.plot(y_sim_opt[:,0], y_sim_opt[:,1], label = "optimized") pl.title("Simulated race car path for initial and optimized controls") pl.xlabel("$X$") pl.ylabel("$Y$", rotation = 0) pl.legend(loc = "lower left") pl.show()
def plot_initial_and_optimized_controls(time_points, \ udata_init, udata_opt, umin, umax): pl.rc("text", usetex = True) pl.rc("font", family="serif") pl.subplot2grid((2, 1), (0, 0)) pl.step(time_points[:-1], udata_init[:,0], label = "$\delta_{init}$") pl.step(time_points[:-1], udata_init[:,1], label = "$D_{init}$") pl.plot([time_points[0], time_points[-2]], [umin[0], umin[0]], \ color = "b", linestyle = "dashed", label = "$\delta_{min}$") pl.plot([time_points[0], time_points[-2]], [umax[0], umax[0]], \ color = "b", linestyle = "dotted", label = "$\delta_{max}$") pl.plot([time_points[0], time_points[-2]], [umin[1], umin[1]], \ color = "g", linestyle = "dashed", label = "$D_{min}$") pl.plot([time_points[0], time_points[-2]], [umax[1], umax[1]], \ color = "g", linestyle = "dotted", label = "$D_{max}$") pl.ylabel("$\delta,\,D$", rotation = 0) pl.ylim(-0.6, 1.1) pl.title("Initial and optimized controls") pl.legend(loc = "upper right") pl.subplot2grid((2, 1), (1, 0)) pl.step(time_points[:-1], udata_opt[:,0], label = "$\delta_{opt,coll}$") pl.step(time_points[:-1], udata_opt[:,1], label = "$D_{opt,coll}$") pl.plot([time_points[0], time_points[-2]], [umin[0], umin[0]], \ color = "b", linestyle = "dashed", label = "$\delta_{min}$") pl.plot([time_points[0], time_points[-2]], [umax[0], umax[0]], \ color = "b", linestyle = "dotted", label = "$\delta_{max}$") pl.plot([time_points[0], time_points[-2]], [umin[1], umin[1]], \ color = "g", linestyle = "dashed", label = "$D_{min}$") pl.plot([time_points[0], time_points[-2]], [umax[1], umax[1]], \ color = "g", linestyle = "dotted", label = "$D_{max}$") pl.xlabel("$t$") pl.ylabel("$\delta,\,D$", rotation = 0) pl.ylim(-0.6, 1.1) pl.legend(loc = "upper right") pl.show()
