我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用matplotlib.pylab.subplots_adjust()。
def plot1D_mat(a, b, M, title=''): """ Plot matrix M with the source and target 1D distribution Creates a subplot with the source distribution a on the left and target distribution b on the tot. The matrix M is shown in between. Parameters ---------- a : np.array, shape (na,) Source distribution b : np.array, shape (nb,) Target distribution M : np.array, shape (na,nb) Matrix to plot """ na, nb = M.shape gs = gridspec.GridSpec(3, 3) xa = np.arange(na) xb = np.arange(nb) ax1 = pl.subplot(gs[0, 1:]) pl.plot(xb, b, 'r', label='Target distribution') pl.yticks(()) pl.title(title) ax2 = pl.subplot(gs[1:, 0]) pl.plot(a, xa, 'b', label='Source distribution') pl.gca().invert_xaxis() pl.gca().invert_yaxis() pl.xticks(()) pl.subplot(gs[1:, 1:], sharex=ax1, sharey=ax2) pl.imshow(M, interpolation='nearest') pl.axis('off') pl.xlim((0, nb)) pl.tight_layout() pl.subplots_adjust(wspace=0., hspace=0.2)
def plot_angular_velocities(title, angular_velocities, angular_velocities_filtered, block=True): fig = plt.figure() title_position = 1.05 fig.suptitle(title, fontsize='24') a1 = plt.subplot(1, 2, 1) a1.set_title( "Angular Velocities Before Filtering \nvx [red], vy [green], vz [blue]", y=title_position) plt.plot(angular_velocities[:, 0], c='r') plt.plot(angular_velocities[:, 1], c='g') plt.plot(angular_velocities[:, 2], c='b') a2 = plt.subplot(1, 2, 2) a2.set_title( "Angular Velocities After Filtering \nvx [red], vy [green], vz [blue]", y=title_position) plt.plot(angular_velocities_filtered[:, 0], c='r') plt.plot(angular_velocities_filtered[:, 1], c='g') plt.plot(angular_velocities_filtered[:, 2], c='b') plt.subplots_adjust(left=0.025, right=0.975, top=0.8, bottom=0.05) if plt.get_backend() == 'TkAgg': mng = plt.get_current_fig_manager() max_size = mng.window.maxsize() max_size = (max_size[0], max_size[1] * 0.45) mng.resize(*max_size) plt.show(block=block)
def threehistsx(x1,x2,x3,x1leg='$x_1$',x2leg='$x_2$',x3leg='$x_3$',fig=1,fontsize=12,bins1=10,bins2=10,bins3=10): """ Script that pretty-plots three histograms of quantities x1, x2 and x3. Arguments: :param x1,x2,x3: arrays with data to be plotted :param x1leg, x2leg, x3leg: legends for each histogram :param fig: which plot window should I use? Example: x1=Lbol(AD), x2=Lbol(JD), x3=Lbol(EHF10) >>> threehists(x1,x2,x3,38,44,'AD','JD','EHF10','$\log L_{\\rm bol}$ (erg s$^{-1}$)') Inspired by http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label. """ pylab.rcParams.update({'font.size': fontsize}) pylab.figure(fig) pylab.clf() pylab.subplot(3,1,1) pylab.hist(x1,label=x1leg,color='b',bins=bins1) pylab.legend(loc='best',frameon=False) pylab.subplot(3,1,2) pylab.hist(x2,label=x2leg,color='r',bins=bins2) pylab.legend(loc='best',frameon=False) pylab.subplot(3,1,3) pylab.hist(x3,label=x3leg,color='y',bins=bins3) pylab.legend(loc='best',frameon=False) pylab.minorticks_on() pylab.subplots_adjust(hspace=0.15) pylab.draw() pylab.show()
def show_top_words_over_time( task_output_path=None, vocabList=None, query_laps=[0, 1, 2, 5, None], ncols=10): ''' ''' nrows = len(query_laps) fig_handle, ax_handles_RC = pylab.subplots( figsize=(SMALL_FIG_SIZE[0] * ncols, SMALL_FIG_SIZE[1] * nrows), nrows=nrows, ncols=ncols, sharex=True, sharey=True) for row_id, lap_val in enumerate(query_laps): cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val) # Plot the current model cur_ax_list = ax_handles_RC[row_id].flatten().tolist() bnpy.viz.PrintTopics.plotCompsFromHModel( cur_model, vocabList=vocabList, fontsize=9, Ktop=7, ax_list=cur_ax_list) cur_ax_list[0].set_ylabel("lap: %d" % lap_val) pylab.subplots_adjust( wspace=0.04, hspace=0.1, left=0.01, right=0.99, top=0.99, bottom=0.1) pylab.tight_layout() ############################################################################### # # Show the topics over time
def _viz_Gauss_before_after( curModel=None, propModel=None, curSS=None, propSS=None, Plan=None, propLscore=None, curLscore=None, Data_b=None, Data_t=None, **kwargs): pylab.subplots( nrows=1, ncols=2, figsize=(8, 4), num=1) h1 = pylab.subplot(1, 2, 1) h1.clear() GaussViz.plotGauss2DFromHModel( curModel, compsToHighlight=Plan['btargetCompID'], figH=h1) if curLscore is not None: pylab.title('%.4f' % (curLscore)) h2 = pylab.subplot(1, 2, 2, sharex=h1, sharey=h1) h2.clear() newCompIDs = np.arange(curModel.obsModel.K, propModel.obsModel.K) GaussViz.plotGauss2DFromHModel( propModel, compsToHighlight=newCompIDs, figH=h2, Data=Data_t) if propLscore is not None: pylab.title('%.4f' % (propLscore)) Lgain = propLscore - curLscore if Lgain > 0: pylab.xlabel('ACCEPT +%.2f' % (Lgain)) else: pylab.xlabel('REJECT %.2f' % (Lgain)) pylab.draw() pylab.subplots_adjust(hspace=0.1, top=0.9, bottom=0.15, left=0.15, right=0.95)
def plot_results(times_A, times_B, signal_A, signal_B, convoluted_signals, time_offset, block=True): fig = plt.figure() title_position = 1.05 matplotlib.rcParams.update({'font.size': 20}) # fig.suptitle("Time Alignment", fontsize='24') a1 = plt.subplot(1, 3, 1) a1.get_xaxis().get_major_formatter().set_useOffset(False) plt.ylabel('angular velocity norm [rad]') plt.xlabel('time [s]') a1.set_title( "Before Time Alignment", y=title_position) plt.hold("on") min_time = min(np.amin(times_A), np.amin(times_B)) times_A_zeroed = times_A - min_time times_B_zeroed = times_B - min_time plt.plot(times_A_zeroed, signal_A, c='r') plt.plot(times_B_zeroed, signal_B, c='b') times_A_shifted = times_A + time_offset a3 = plt.subplot(1, 3, 2) a3.get_xaxis().get_major_formatter().set_useOffset(False) plt.ylabel('correlation') plt.xlabel('sample idx offset') a3.set_title( "Correlation Result \n[Ideally has a single dominant peak.]", y=title_position) plt.hold("on") plt.plot(np.arange(-len(signal_A) + 1, len(signal_B)), convoluted_signals) a2 = plt.subplot(1, 3, 3) a2.get_xaxis().get_major_formatter().set_useOffset(False) plt.ylabel('angular velocity norm [rad]') plt.xlabel('time [s]') a2.set_title( "After Time Alignment", y=title_position) plt.hold("on") min_time = min(np.amin(times_A_shifted), np.amin(times_B)) times_A_shifted_zeroed = times_A_shifted - min_time times_B_zeroed = times_B - min_time plt.plot(times_A_shifted_zeroed, signal_A, c='r') plt.plot(times_B_zeroed, signal_B, c='b') plt.subplots_adjust(left=0.04, right=0.99, top=0.8, bottom=0.15) if plt.get_backend() == 'TkAgg': mng = plt.get_current_fig_manager() max_size = mng.window.maxsize() max_size = (max_size[0], max_size[1] * 0.45) mng.resize(*max_size) plt.show(block=block)
def plot_time_stamped_poses(title, time_stamped_poses_A, time_stamped_poses_B, block=True): fig = plt.figure() title_position = 1.05 fig.suptitle(title + " [A = top, B = bottom]", fontsize='24') a1 = plt.subplot(2, 2, 1) a1.set_title( "Orientation \nx [red], y [green], z [blue], w [cyan]", y=title_position) plt.plot(time_stamped_poses_A[:, 4], c='r') plt.plot(time_stamped_poses_A[:, 5], c='g') plt.plot(time_stamped_poses_A[:, 6], c='b') plt.plot(time_stamped_poses_A[:, 7], c='c') a2 = plt.subplot(2, 2, 2) a2.set_title( "Position (eye coordinate frame) \nx [red], y [green], z [blue]", y=title_position) plt.plot(time_stamped_poses_A[:, 1], c='r') plt.plot(time_stamped_poses_A[:, 2], c='g') plt.plot(time_stamped_poses_A[:, 3], c='b') a3 = plt.subplot(2, 2, 3) plt.plot(time_stamped_poses_B[:, 4], c='r') plt.plot(time_stamped_poses_B[:, 5], c='g') plt.plot(time_stamped_poses_B[:, 6], c='b') plt.plot(time_stamped_poses_B[:, 7], c='c') a4 = plt.subplot(2, 2, 4) plt.plot(time_stamped_poses_B[:, 1], c='r') plt.plot(time_stamped_poses_B[:, 2], c='g') plt.plot(time_stamped_poses_B[:, 3], c='b') plt.subplots_adjust(left=0.025, right=0.975, top=0.8, bottom=0.05) if plt.get_backend() == 'TkAgg': mng = plt.get_current_fig_manager() max_size = mng.window.maxsize() max_size = (max_size[0], max_size[1] * 0.45) mng.resize(*max_size) plt.show(block=block)
def twohists(x1,x2,xmin,xmax,range=None,x1leg='$x_1$',x2leg='$x_2$',xlabel='',fig=1,sharey=False,fontsize=12,bins1=10,bins2=10): """ Script that plots two histograms of quantities x1 and x2 sharing the same X-axis. :param x1,x2: arrays with data to be plotted :param xmin,xmax: lower and upper range of plotted values, will be used to set a consistent x-range for both histograms. :param x1leg, x2leg: legends for each histogram :param xlabel: self-explanatory. :param bins1,bins2: number of bins in each histogram :param fig: which plot window should I use? :param range: in the form (xmin,xmax), same as range argument for hist and applied to both histograms. Inspired by `Scipy <http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label>`_. """ pylab.rcParams.update({'font.size': fontsize}) fig=pylab.figure(fig) pylab.clf() a=fig.add_subplot(2,1,1) if sharey==True: b=fig.add_subplot(2,1,2, sharex=a, sharey=a) else: b=fig.add_subplot(2,1,2, sharex=a) a.hist(x1,bins1,label=x1leg,color='b',histtype='stepfilled',range=range) a.legend(loc='best',frameon=False) a.set_xlim(xmin,xmax) b.hist(x2,bins2,label=x2leg,color='r',histtype='stepfilled',range=range) b.legend(loc='best',frameon=False) pylab.setp(a.get_xticklabels(), visible=False) b.set_xlabel(xlabel) b.set_ylabel('Number',verticalalignment='bottom') pylab.minorticks_on() pylab.subplots_adjust(hspace=0.15) pylab.draw() pylab.show()
def threehists(x1,x2,x3,xmin,xmax,x1leg='$x_1$',x2leg='$x_2$',x3leg='$x_3$',xlabel='',fig=1,sharey=False,fontsize=12): """ Script that plots three histograms of quantities x1, x2 and x3 sharing the same X-axis. Arguments: - x1,x2,x3: arrays with data to be plotted - xmin,xmax: lower and upper range of plotted values, will be used to set a consistent x-range for both histograms. - x1leg, x2leg, x3leg: legends for each histogram - xlabel: self-explanatory. - sharey: sharing the Y-axis among the histograms? - fig: which plot window should I use? Example: x1=Lbol(AD), x2=Lbol(JD), x3=Lbol(EHF10) >>> threehists(x1,x2,x3,38,44,'AD','JD','EHF10','$\log L_{\\rm bol}$ (erg s$^{-1}$)',sharey=True) Inspired by `Scipy <http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label>`_. """ pylab.rcParams.update({'font.size': fontsize}) fig=pylab.figure(fig) pylab.clf() a=fig.add_subplot(3,1,1) if sharey==True: b=fig.add_subplot(3,1,2, sharex=a, sharey=a) c=fig.add_subplot(3,1,3, sharex=a, sharey=a) else: b=fig.add_subplot(3,1,2, sharex=a) c=fig.add_subplot(3,1,3, sharex=a) a.hist(x1,label=x1leg,color='b',histtype='stepfilled') a.legend(loc='best',frameon=False) a.set_xlim(xmin,xmax) b.hist(x2,label=x2leg,color='r',histtype='stepfilled') b.legend(loc='best',frameon=False) c.hist(x3,label=x3leg,color='y',histtype='stepfilled') c.legend(loc='best',frameon=False) pylab.setp(a.get_xticklabels(), visible=False) pylab.setp(b.get_xticklabels(), visible=False) c.set_xlabel(xlabel) b.set_ylabel('Number') pylab.minorticks_on() pylab.subplots_adjust(hspace=0.15) pylab.draw() pylab.show()
def fourcumplot(x1,x2,x3,x4,xmin,xmax,x1leg='$x_1$',x2leg='$x_2$',x3leg='$x_3$',x4leg='$x_3$',xlabel='',ylabel='$N(x>x\')$',fig=1,sharey=False,fontsize=12,bins1=50,bins2=50,bins3=50,bins4=50): """ Script that plots the cumulative histograms of four variables x1, x2, x3 and x4 sharing the same X-axis. For each bin, Y is the fraction of the sample with values above X. Arguments: - x1,x2,x3,x4: arrays with data to be plotted - xmin,xmax: lower and upper range of plotted values, will be used to set a consistent x-range for both histograms. - x1leg, x2leg, x3leg, x4leg: legends for each histogram - xlabel: self-explanatory. - sharey: sharing the Y-axis among the histograms? - bins1,bins2,...: number of bins in each histogram - fig: which plot window should I use? Inspired by `Scipy <http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label>`_. v1 Jun. 2012: inherited from fourhists. """ pylab.rcParams.update({'font.size': fontsize}) fig=pylab.figure(fig) pylab.clf() a=fig.add_subplot(4,1,1) if sharey==True: b=fig.add_subplot(4,1,2, sharex=a, sharey=a) c=fig.add_subplot(4,1,3, sharex=a, sharey=a) d=fig.add_subplot(4,1,4, sharex=a, sharey=a) else: b=fig.add_subplot(4,1,2, sharex=a) c=fig.add_subplot(4,1,3, sharex=a) d=fig.add_subplot(4,1,4, sharex=a) a.hist(x1,bins1,label=x1leg,color='b',cumulative=-True,normed=True,histtype='stepfilled') a.legend(loc='best',frameon=False) a.set_xlim(xmin,xmax) b.hist(x2,bins2,label=x2leg,color='r',cumulative=-True,normed=True,histtype='stepfilled') b.legend(loc='best',frameon=False) c.hist(x3,bins3,label=x3leg,color='y',cumulative=-True,normed=True,histtype='stepfilled') c.legend(loc='best',frameon=False) d.hist(x4,bins4,label=x4leg,color='g',cumulative=-True,normed=True,histtype='stepfilled') d.legend(loc='best',frameon=False) pylab.setp(a.get_xticklabels(), visible=False) pylab.setp(b.get_xticklabels(), visible=False) pylab.setp(c.get_xticklabels(), visible=False) d.set_xlabel(xlabel) c.set_ylabel(ylabel) pylab.minorticks_on() pylab.subplots_adjust(hspace=0.15) pylab.draw() pylab.show()
