我们从Python开源项目中，提取了以下21个代码示例，用于说明如何使用matplotlib.pylab.gca()。
def plot_1d(dataset, nbins, data): with sns.axes_style('white'): plt.rc('font', weight='bold') plt.rc('grid', lw=2) plt.rc('lines', lw=3) plt.figure(1) plt.hist(data, bins=np.arange(nbins+1), color='blue') plt.ylabel('Count', weight='bold', fontsize=24) xticks = list(plt.gca().get_xticks()) while (nbins-1) / float(xticks[-1]) < 1.1: xticks = xticks[:-1] while xticks[0] < 0: xticks = xticks[1:] xticks.append(nbins-1) xticks = list(sorted(xticks)) plt.gca().set_xticks(xticks) plt.xlim([int(np.ceil(-0.05*nbins)),int(np.ceil(nbins*1.05))]) plt.legend(loc='upper right') plt.savefig('plots/marginals-{0}.pdf'.format(dataset.replace('_','-')), bbox_inches='tight') plt.clf() plt.close()
def plot_1d(dataset, nbins): data = np.loadtxt('experiments/uci/data/splits/{0}_all.csv'.format(dataset), skiprows=1, delimiter=',')[:,-1] with sns.axes_style('white'): plt.rc('font', weight='bold') plt.rc('grid', lw=2) plt.rc('lines', lw=3) plt.figure(1) plt.hist(data, bins=np.arange(nbins+1), color='blue') plt.ylabel('Count', weight='bold', fontsize=24) xticks = list(plt.gca().get_xticks()) while (nbins-1) / float(xticks[-1]) < 1.1: xticks = xticks[:-1] while xticks[0] < 0: xticks = xticks[1:] xticks.append(nbins-1) xticks = list(sorted(xticks)) plt.gca().set_xticks(xticks) plt.xlim([int(np.ceil(-0.05*nbins)),int(np.ceil(nbins*1.05))]) plt.legend(loc='upper right') plt.savefig('plots/marginals-{0}.pdf'.format(dataset.replace('_','-')), bbox_inches='tight') plt.clf() plt.close()
def plotValueFunction(self, valueFunction, prefix): '''3d plot of a value function.''' fig, ax = plt.subplots(subplot_kw = dict(projection = '3d')) X, Y = np.meshgrid(np.arange(self.numCols), np.arange(self.numRows)) Z = valueFunction.reshape(self.numRows, self.numCols) for i in xrange(len(X)): for j in xrange(len(X[i])/2): tmp = X[i][j] X[i][j] = X[i][len(X[i]) - j - 1] X[i][len(X[i]) - j - 1] = tmp my_col = cm.jet(np.random.rand(Z.shape[0],Z.shape[1])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = plt.get_cmap('jet')) plt.gca().view_init(elev=30, azim=30) plt.savefig(self.outputPath + prefix + 'value_function.png') plt.close()
def plot_feat_importance(feature_names, clf, name): pylab.clf() coef_ = clf.coef_ important = np.argsort(np.absolute(coef_.ravel())) f_imp = feature_names[important] coef = coef_.ravel()[important] inds = np.argsort(coef) f_imp = f_imp[inds] coef = coef[inds] xpos = np.array(range(len(coef))) pylab.bar(xpos, coef, width=1) pylab.title('Feature importance for %s' % (name)) ax = pylab.gca() ax.set_xticks(np.arange(len(coef))) labels = ax.set_xticklabels(f_imp) for label in labels: label.set_rotation(90) filename = name.replace(" ", "_") pylab.savefig(os.path.join( CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight")
def plot_feat_importance(feature_names, clf, name): pylab.figure(num=None, figsize=(6, 5)) coef_ = clf.coef_ important = np.argsort(np.absolute(coef_.ravel())) f_imp = feature_names[important] coef = coef_.ravel()[important] inds = np.argsort(coef) f_imp = f_imp[inds] coef = coef[inds] xpos = np.array(list(range(len(coef)))) pylab.bar(xpos, coef, width=1) pylab.title('Feature importance for %s' % (name)) ax = pylab.gca() ax.set_xticks(np.arange(len(coef))) labels = ax.set_xticklabels(f_imp) for label in labels: label.set_rotation(90) filename = name.replace(" ", "_") pylab.savefig(os.path.join( CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight")
def hrd_new(self, input_label="", skip=0): """ plot an HR diagram with options to skip the first N lines and add a label string Parameters ---------- input_label : string, optional Diagram label. The default is "". skip : integer, optional Skip the first n lines. The default is 0. """ xl_old=pyl.gca().get_xlim() if input_label == "": my_label="M="+str(self.header_attr['initial_mass'])+", Z="+str(self.header_attr['initial_z']) else: my_label="M="+str(self.header_attr['initial_mass'])+", Z="+str(self.header_attr['initial_z'])+"; "+str(input_label) pyl.plot(self.data[skip:,self.cols['log_Teff']-1],self.data[skip:,self.cols['log_L']-1],label = my_label) pyl.legend(loc=0) xl_new=pyl.gca().get_xlim() pyl.xlabel('log Teff') pyl.ylabel('log L') if any(array(xl_old)==0): pyl.gca().set_xlim(max(xl_new),min(xl_new)) elif any(array(xl_new)==0): pyl.gca().set_xlim(max(xl_old),min(xl_old)) else: pyl.gca().set_xlim([max(xl_old+xl_new),min(xl_old+xl_new)])
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 plotBasisFunctions(self, eigenvalues, eigenvectors): '''3d plot of the basis function. Right now I am plotting eigenvectors, so each coordinate of the eigenvector correspond to the value to be plotted for the correspondent state.''' for i in xrange(len(eigenvalues)): fig, ax = plt.subplots(subplot_kw = dict(projection = '3d')) X, Y = np.meshgrid(np.arange(self.numRows), np.arange(self.numCols)) Z = eigenvectors[:,i].reshape(self.numCols, self.numRows) for ii in xrange(len(X)): for j in xrange(len(X[ii])/2): tmp = X[ii][j] X[ii][j] = X[ii][len(X[ii]) - j - 1] X[ii][len(X[ii]) - j - 1] = tmp my_col = cm.jet(np.random.rand(Z.shape[0],Z.shape[1])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = plt.get_cmap('jet')) plt.gca().view_init(elev=30, azim=30) plt.savefig(self.outputPath + str(i) + '_eig' + '.png') plt.close() plt.plot(eigenvalues, 'o') plt.savefig(self.outputPath + 'eigenvalues.png')
def clean_ticks(): ax = plt.gca() ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left')
def percent_ylabel(): plt.gca().set_yticklabels(['%d%%' % (x * 100) for x in plt.gca().get_yticks()])
def plotBoundVsK(KVals=np.arange(1,50), alpha=0.5, gamma=10, labels=None, betaFunc='prior'): if labels is None: txtlabel = str(alpha) labels = [None, None] else: txtlabel = 'alpha\n' + str(alpha) exactVals = np.zeros(len(KVals)) boundVals = np.zeros(len(KVals)) for ii, K in enumerate(KVals): betaVec = 1.0/(1.0 + gamma) * np.ones(K+1) for k in range(1, K): betaVec[k] = betaVec[k] * (1 - np.sum(betaVec[:k])) betaVec[-1] = 1 - np.sum(betaVec[:-1]) print betaVec assert np.allclose(betaVec.sum(), 1.0) exactVals[ii] = cDir_exact(alpha, betaVec) boundVals[ii] = cDir_surrogate(alpha, betaVec) assert np.all(exactVals >= boundVals) pylab.plot(KVals, exactVals, 'k-', linewidth=LINEWIDTH, label=labels[0]) pylab.plot(KVals, boundVals, 'r--', linewidth=LINEWIDTH, label=labels[1]) index = -1 pylab.text(KVals[index]+.25, boundVals[index], txtlabel, fontsize=LEGENDSIZE-8) pylab.xlim([0, np.max(KVals)+7.5]) pylab.gca().set_xticks([0, 10, 20, 30, 40, 50]) pylab.xlabel("K", fontsize=FONTSIZE) pylab.ylabel("cDir function", fontsize=FONTSIZE) pylab.tick_params(axis='both', which='major', labelsize=TICKSIZE)
def makeFigure(hmmKappa=0): Data, trueResp = makeDataAndTrueResp() kemptyVals = np.asarray([0, 1, 2, 3.]) ELBOVals = np.zeros_like(kemptyVals, dtype=np.float) # Iterate over the number of empty states (0, 1, 2, ...) for ii, kempty in enumerate(kemptyVals): resp = makeNewRespWithEmptyStates(trueResp, kempty) ELBOVals[ii] = resp2ELBO_HDPHMM(Data, resp, hmmKappa=hmmKappa) # Make largest value the one with kempty=0, to make plot look good ELBOVals -= ELBOVals[0] # Plot the results from matplotlib import pylab figH = pylab.figure(figsize=(6, 4)) plotargs = dict(markersize=10, linewidth=3) pylab.plot(kemptyVals, ELBOVals, 'o--', label='HDP surrogate', color='b', markeredgecolor='b', **plotargs) pylab.xlabel('num. empty topics', fontsize=20) pylab.ylabel('change in ELBO', fontsize=20) B = 0.25 pylab.xlim([-B, kemptyVals[-1] + B]) pylab.xticks(kemptyVals) axH = pylab.gca() axH.tick_params(axis='both', which='major', labelsize=15) legH = pylab.legend(loc='upper left', prop={'size': 15}) figH.subplots_adjust(bottom=0.16, left=0.2) pylab.show(block=True)
def plot_projections(points): num_images = len(points) plt.figure() plt.suptitle('3D to 2D Projections', fontsize=16) for i in range(num_images): plt.subplot(1, num_images, i+1) ax = plt.gca() ax.set_aspect('equal') ax.plot(points[i][0], points[i][1], 'r.')
def plot_cube(points3d, title=''): fig = plt.figure() fig.suptitle(title, fontsize=16) ax = fig.gca(projection='3d') ax.set_aspect('equal') ax.plot(points3d[0], points3d[1], points3d[2], 'b.') ax.set_xlabel('x axis') ax.set_ylabel('y axis') ax.set_zlabel('z axis') ax.view_init(elev=135, azim=90) return ax
def plotPolicy(self, policy, prefix): plt.clf() for idx in xrange(len(policy)): i, j = self.env.getStateXY(idx) dx = 0 dy = 0 if policy[idx] == 0: # up dy = 0.35 elif policy[idx] == 1: #right dx = 0.35 elif policy[idx] == 2: #down dy = -0.35 elif policy[idx] == 3: #left dx = -0.35 elif self.matrixMDP[i][j] != -1 and policy[idx] == 4: # termination circle = plt.Circle( (j + 0.5, self.numRows - i + 0.5 - 1), 0.025, color='k') plt.gca().add_artist(circle) if self.matrixMDP[i][j] != -1: plt.arrow(j + 0.5, self.numRows - i + 0.5 - 1, dx, dy, head_width=0.05, head_length=0.05, fc='k', ec='k') else: plt.gca().add_patch( patches.Rectangle( (j, self.numRows - i - 1), # (x,y) 1.0, # width 1.0, # height facecolor = "gray" ) ) plt.xlim([0, self.numCols]) plt.ylim([0, self.numRows]) for i in xrange(self.numCols): plt.axvline(i, color='k', linestyle=':') plt.axvline(self.numCols, color='k', linestyle=':') for j in xrange(self.numRows): plt.axhline(j, color='k', linestyle=':') plt.axhline(self.numRows, color='k', linestyle=':') plt.savefig(self.outputPath + prefix + 'policy.png') plt.close()
def _plot_ND_FES(data, ax_labels, weights=None, bins=50, figsize=(4,4)): r""" A wrapper for pyemmas FESs plotting function that can also plot 1D Parameters ---------- data : list of numpy nd.arrays ax_labels : list Returns ------- ax : :obj:`pylab.Axis` object FES_data : numpy nd.array containing the FES (only for 1D data) edges : tuple containimg the axes along which FES is to be plotted (only in the 1D case so far, else it's None) """ _plt.figure(figsize=figsize) ax = _plt.gca() idata = _np.vstack(data) ax.set_xlabel(ax_labels[0]) if idata.shape[1] == 1: h, edges = _np.histogramdd(idata, weights=weights, bins=bins, normed=True) FES_data = -_np.log(h) FES_data -= FES_data.min() ax.plot(edges[0][:-1], FES_data) ax.set_ylabel('$\Delta G / \kappa T $') elif idata.shape[1] == 2: _plot_free_energy(idata[:,0], idata[:,1], weights=weights, nbins=bins, ax=ax, cmap='nipy_spectral' ) ax.set_ylabel(ax_labels[1]) edges, FES_data = [None], None # TODO: retrieve the actual edges from pyemma's "plot_free_energy"'s axes else: raise NotImplementedError('Can only plot 1D or 2D FESs, but data has %s columns' % _np.shape(idata)[0]) return ax, FES_data, edges
def makeFigure(**kwargs): Data, trueResp = makeDataAndTrueResp(**kwargs) kemptyVals = np.asarray([0, 1, 2, 3.]) ELBOVals = np.zeros_like(kemptyVals, dtype=np.float) PointEstELBOVals = np.zeros_like(kemptyVals, dtype=np.float) # Iterate over the number of empty states (0, 1, 2, ...) for ii, kempty in enumerate(kemptyVals): resp = makeNewRespWithEmptyStates(trueResp, kempty) PointEstELBOVals[ii] = resp2ELBO_HDPTopicModel( Data, resp, doPointEstimate=1, **kwargs) ELBOVals[ii] = resp2ELBO_HDPTopicModel(Data, resp, **kwargs) # Make largest value the one with kempty=0, to make plot look good PointEstELBOVals -= PointEstELBOVals[0] ELBOVals -= ELBOVals[0] # Rescale so that yaxis has units on order of 1, not 0.001 scale = np.max(np.abs(ELBOVals)) ELBOVals /= scale PointEstELBOVals /= scale # Set buffer-space for defining plotable area xB = 0.25 B = 0.19 # big buffer for sides where we will put text labels b = 0.01 # small buffer for other sides TICKSIZE = 30 FONTSIZE = 40 LEGENDSIZE = 30 LINEWIDTH = 4 # Plot the results figH = pylab.figure(figsize=(9.1, 6)) axH = pylab.subplot(111) axH.set_position([xB, B, (1 - xB - b), (1 - B - b)]) plotargs = dict(markersize=20, linewidth=LINEWIDTH) pylab.plot(kemptyVals, PointEstELBOVals, 'v-', label='HDP point est', color='b', markeredgecolor='b', **plotargs) pylab.plot(kemptyVals, np.zeros_like(kemptyVals), 's:', label='HDP exact', color='g', markeredgecolor='g', **plotargs) pylab.plot(kemptyVals, ELBOVals, 'o--', label='HDP surrogate', color='r', markeredgecolor='r', **plotargs) pylab.xlabel('num. empty topics', fontsize=FONTSIZE) pylab.ylabel('change in ELBO', fontsize=FONTSIZE) xB = 0.25 pylab.xlim([-xB, kemptyVals[-1] + xB]) pylab.xticks(kemptyVals) pylab.yticks([-1, 0, 1]) axH = pylab.gca() axH.tick_params(axis='both', which='major', labelsize=TICKSIZE) legH = pylab.legend(loc='upper left', prop={'size': LEGENDSIZE})
def illustrate(Colors=Colors): if hasattr(Colors, 'colors'): Colors = Colors.colors from matplotlib import pylab rcParams = pylab.rcParams rcParams['pdf.fonttype'] = 42 rcParams['ps.fonttype'] = 42 rcParams['text.usetex'] = False rcParams['xtick.labelsize'] = 20 rcParams['ytick.labelsize'] = 20 rcParams['legend.fontsize'] = 25 import bnpy Data = get_data(T=1000, nDocTotal=8) for k in xrange(K): zmask = Data.TrueParams['Z'] == k pylab.plot(Data.X[zmask, 0], Data.X[zmask, 1], '.', color=Colors[k], markeredgecolor=Colors[k], alpha=0.4) sigEdges = np.flatnonzero(transPi[k] > 0.0001) for j in sigEdges: if j == k: continue dx = mus[j, 0] - mus[k, 0] dy = mus[j, 1] - mus[k, 1] pylab.arrow(mus[k, 0], mus[k, 1], 0.8 * dx, 0.8 * dy, head_width=2, head_length=4, facecolor=Colors[k], edgecolor=Colors[k]) tx = 0 - mus[k, 0] ty = 0 - mus[k, 1] xy = (mus[k, 0] - 0.2 * tx, mus[k, 1] - 0.2 * ty) ''' pylab.annotate( u'\u27F2', xy=(mus[k,0], mus[k,1]), color=Colors[k], fontsize=35, ) ''' pylab.gca().yaxis.set_ticks_position('left') pylab.gca().xaxis.set_ticks_position('bottom') pylab.axis('image') pylab.ylim([-38, 38]) pylab.xlim([-38, 38])
