我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用pylab.subplots_adjust()。
def plot_importance(names, model, savefig=True): featureNames = numpy.array(names) featureImportance = model.feature_importances_ featureImportance = featureImportance / featureImportance.max() sorted_idx = numpy.argsort(featureImportance) barPos = numpy.arange(sorted_idx.shape[0]) + .5 plot.barh(barPos, featureImportance[sorted_idx], align='center') plot.yticks(barPos, featureNames[sorted_idx]) plot.xlabel('Variable Importance') plot.subplots_adjust(left=0.2, right=0.9, top=0.9, bottom=0.1) if savefig: dt_ = datetime.datetime.now().strftime('%d%b%y_%H%M') plt.savefig("../graphs/featureImportance_" + dt_ + ".png") plot.show() # Plot prediction save the graph with a timestamp
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 resetplot(): import matplotlib import pylab as plt kw = {} for p in ['bottom', 'top', 'left', 'right', 'hspace', 'wspace']: kw[p] = matplotlib.rcParams['figure.subplot.' + p] plt.subplots_adjust(**kw)
def plotaffinegrid(affines, exag=1e3, affineOnly=True, R=0.025, tpre='', bboxes=None): import pylab as plt NR = 3 NC = int(ceil(len(affines)/3.)) #R = 0.025 # 1.5 arcmin #for (exag,affonly) in [(1e2, False), (1e3, True), (1e4, True)]: plt.clf() for i,aff in enumerate(affines): plt.subplot(NR, NC, i+1) dl = aff.refdec - R dh = aff.refdec + R rl = aff.refra - R / aff.rascale rh = aff.refra + R / aff.rascale RR,DD = np.meshgrid(np.linspace(rl, rh, 11), np.linspace(dl, dh, 11)) plotaffine(aff, RR.ravel(), DD.ravel(), exag=exag, affineOnly=affineOnly, doclf=False, units='dots', width=2, headwidth=2.5, headlength=3, headaxislength=3) if bboxes is not None: for bb in bboxes: plt.plot(*bb, linestyle='-', color='0.5') plt.plot(*bboxes[i], linestyle='-', color='k') setRadecAxes(rl,rh,dl,dh) plt.xlabel('') plt.ylabel('') plt.xticks([]) plt.yticks([]) plt.title('field %i' % (i+1)) plt.subplots_adjust(left=0.05, right=0.95, wspace=0.1) if affineOnly: tt = tpre + 'Affine part of transformations' else: tt = tpre + 'Transformations' plt.suptitle(tt + ' (x %g)' % exag)
def plot_rels(data, labels=None, colors=None, outfile="rels", latent=None, alpha=0.8, title=''): ns, n = data.shape if labels is None: labels = map(str, range(n)) ncol = 5 nrow = int(np.ceil(float(n * (n - 1) / 2) / ncol)) fig, axs = pylab.subplots(nrow, ncol) fig.set_size_inches(5 * ncol, 5 * nrow) pairs = list(combinations(range(n), 2)) if colors is not None: colors = (colors - np.min(colors)) / (np.max(colors) - np.min(colors)) for ax, pair in zip(axs.flat, pairs): diff_x = max(data[:, pair[0]]) - min(data[:, pair[0]]) diff_y = max(data[:, pair[1]]) - min(data[:, pair[1]]) ax.set_xlim([min(data[:, pair[0]]) - 0.05 * diff_x, max(data[:, pair[0]]) + 0.05 * diff_x]) ax.set_ylim([min(data[:, pair[1]]) - 0.05 * diff_y, max(data[:, pair[1]]) + 0.05 * diff_y]) ax.scatter(data[:, pair[0]], data[:, pair[1]], c=colors, cmap=pylab.get_cmap("jet"), marker='.', alpha=alpha, edgecolors='none', vmin=0, vmax=1) ax.set_xlabel(shorten(labels[pair[0]])) ax.set_ylabel(shorten(labels[pair[1]])) for ax in axs.flat[axs.size - 1:len(pairs) - 1:-1]: ax.scatter(data[:, 0], data[:, 1], marker='.') fig.suptitle(title, fontsize=16) pylab.rcParams['font.size'] = 12 #6 # pylab.draw() # fig.set_tight_layout(True) pylab.tight_layout() pylab.subplots_adjust(top=0.95) for ax in axs.flat[axs.size - 1:len(pairs) - 1:-1]: ax.set_visible(False) filename = outfile + '.png' if not os.path.exists(os.path.dirname(filename)): os.makedirs(os.path.dirname(filename)) fig.savefig(outfile + '.png') pylab.close('all') return True # Hierarchical graph visualization utilities
def allplot(xb,yb,bins=30,fig=1,xlabel='x',ylabel='y'): """ Input: X,Y : objects referring to the variables produced by PyMC that you want to analyze. Example: X=M.theta, Y=M.slope. Inherited from Tommy LE BLANC's code at astroplotlib|STSCI. """ #X,Y=xb.trace(),yb.trace() X,Y=xb,yb #pylab.rcParams.update({'font.size': fontsize}) fig=pylab.figure(fig) pylab.clf() gs = pylab.GridSpec(2, 2, width_ratios=[3,1], height_ratios=[1,3], wspace=0.07, hspace=0.07) scat=pylab.subplot(gs[2]) histx=pylab.subplot(gs[0], sharex=scat) histy=pylab.subplot(gs[3], sharey=scat) #scat=fig.add_subplot(2,2,3) #histx=fig.add_subplot(2,2,1, sharex=scat) #histy=fig.add_subplot(2,2,4, sharey=scat) # Scatter plot scat.plot(X, Y,linestyle='none', marker='o', color='green', mec='green',alpha=.2, zorder=-99) gkde = scipy.stats.gaussian_kde([X, Y]) x,y = numpy.mgrid[X.min():X.max():(X.max()-X.min())/25.,Y.min():Y.max():(Y.max()-Y.min())/25.] z = numpy.array(gkde.evaluate([x.flatten(), y.flatten()])).reshape(x.shape) scat.contour(x, y, z, linewidths=2) scat.set_xlabel(xlabel) scat.set_ylabel(ylabel) # X-axis histogram histx.hist(X, bins, histtype='stepfilled') pylab.setp(histx.get_xticklabels(), visible=False) # no X label #histx.xaxis.set_major_formatter(pylab.NullFormatter()) # no X label # Y-axis histogram histy.hist(Y, bins, histtype='stepfilled', orientation='horizontal') pylab.setp(histy.get_yticklabels(), visible=False) # no Y label #histy.yaxis.set_major_formatter(pylab.NullFormatter()) # no Y label #pylab.minorticks_on() #pylab.subplots_adjust(hspace=0.1) #pylab.subplots_adjust(wspace=0.1) pylab.draw() pylab.show()
