我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用pylab.get_cmap()。
def view_raw_templates(file_name, n_temp=2, square=True): N_e, N_t, N_tm = templates.shape if not numpy.iterable(n_temp): if square: idx = numpy.random.permutation(numpy.arange(N_tm//2))[:n_temp**2] else: idx = numpy.random.permutation(numpy.arange(N_tm//2))[:n_temp] else: idx = n_temp import matplotlib.colors as colors my_cmap = pylab.get_cmap('winter') cNorm = colors.Normalize(vmin=0, vmax=N_e) scalarMap = pylab.cm.ScalarMappable(norm=cNorm, cmap=my_cmap) pylab.figure() for count, i in enumerate(idx): if square: pylab.subplot(n_temp, n_temp, count + 1) if (numpy.mod(count, n_temp) != 0): pylab.setp(pylab.gca(), yticks=[]) if (count < n_temp*(n_temp - 1)): pylab.setp(pylab.gca(), xticks=[]) else: pylab.subplot(len(idx), 1, count + 1) if count != (len(idx) - 1): pylab.setp(pylab.gca(), xticks=[]) for j in xrange(N_e): colorVal = scalarMap.to_rgba(j) pylab.plot(templates[j, :, i], color=colorVal) pylab.title('Template %d' %i) pylab.tight_layout() pylab.show()
def set_cmap_synthetic(self, cmap): self._cmap = pylab.get_cmap(cmap) self._cNorm = colors.Normalize(vmin=0, vmax=self.nb_cells) self._scalarMap_synthetic = pylab.cm.ScalarMappable(norm=self._cNorm, cmap=self._cmap)
def set_cmap_circus(self, cmap): self._cmap = pylab.get_cmap(cmap) self._cNorm = colors.Normalize(vmin=0, vmax=self.nb_templates) self._scalarMap_circus = pylab.cm.ScalarMappable(norm=self._cNorm, cmap=self._cmap)
def plot_nodes_over_data_scattermatrix_hexbin(X, Y, mdl, predictions, distances, activities): """plot single components X over Y with SOM sample""" idim = X.shape[1] odim = Y.shape[1] numplots = idim * odim + 2 fig3 = pl.figure() fig3.suptitle("Predictions over data xy scattermatrix/hexbin (%s)" % (mdl.__class__.__name__)) gs = gridspec.GridSpec(idim, odim) fig3axes = [] for i in range(idim): fig3axes.append([]) for o in range(odim): fig3axes[i].append(fig3.add_subplot(gs[i,o])) err = 0 # colsa = ["k", "r", "g", "c", "m", "y"] # colsb = ["k", "r", "g", "c", "m", "y"] colsa = ["k" for col in range(idim)] colsb = ["r" for col in range(odim)] for i in range(odim): # odim * 2 for j in range(idim): # pl.subplot(numplots, 1, (i*idim)+j+1) ax = fig3axes[j][i] # target = Y[h,i] # X__ = X_[j] # X[h,j] # err += np.sum(np.square(target - prediction)) # ax.plot(X__, [target], colsa[j] + ".", alpha=0.25, label="target_%d" % i) # ax.plot(X__, [prediction[0,i]], colsb[j] + "o", alpha=0.25, label="pred_%d" % i) # ax.plot(X[:,j], Y[:,i], colsa[j] + ".", alpha=0.25, label="target_%d" % i) ax.hexbin(X[:,j], Y[:,i], gridsize = 20, alpha=0.75, cmap=pl.get_cmap("gray")) ax.plot(X[:,j], predictions[:,i], colsb[j] + "o", alpha=0.15, label="pred_%d" % i, markersize=8) # pred1 = mdl.filter_e.neuron(mdl.filter_e.flat_to_coords(activities_sorted[-1])) # ax.plot(X__, [pred1], "ro", alpha=0.5) # pred2 = mdl.filter_e.neuron(mdl.filter_e.flat_to_coords(activities_sorted[-2])) # ax.plot(X__, [pred2], "ro", alpha=0.25) print("accum total err = %f" % (err / X.shape[0] / (idim * odim))) fig3.show()
def plot_hebbsom_links_distances_activations(X, Y, mdl, predictions, distances, activities): """plot the hebbian link matrix, and all node distances and activities for all inputs""" hebblink_log = np.log(mdl.hebblink_filter.T + 1.0) fig4 = pl.figure() fig4.suptitle("Debugging SOM: hebbian links, distances, activities (%s)" % (mdl.__class__.__name__)) gs = gridspec.GridSpec(4, 1) # pl.plot(X, Y, "k.", alpha=0.5) # pl.subplot(numplots, 1, numplots-1) ax1 = fig4.add_subplot(gs[0]) # im1 = ax1.imshow(mdl.hebblink_filter, interpolation="none", cmap=pl.get_cmap("gray")) im1 = ax1.pcolormesh(hebblink_log, cmap=pl.get_cmap("gray")) ax1.set_xlabel("in (e)") ax1.set_ylabel("out (p)") cbar = fig4.colorbar(mappable = im1, ax=ax1, orientation="horizontal") ax2 = fig4.add_subplot(gs[1]) distarray = np.array(distances) print("distarray.shape", distarray.shape) pcm = ax2.pcolormesh(distarray.T) cbar = fig4.colorbar(mappable = pcm, ax=ax2, orientation="horizontal") # pl.subplot(numplots, 1, numplots) ax3 = fig4.add_subplot(gs[2]) actarray = np.array(activities) print("actarray.shape", actarray.shape) pcm = ax3.pcolormesh(actarray.T) cbar = fig4.colorbar(mappable = pcm, ax=ax3, orientation="horizontal") ax4 = fig4.add_subplot(gs[3]) ax4.plot(hebblink_log.flatten()) print("hebblink_log", hebblink_log) fig4.show()
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 plot_rels(data, labels=None, colors=None, outfile="rels", latent=None, alpha=0.8): ns, n = data.shape if labels is None: labels = list(map(str, range(n))) ncol = 5 # ncol = 4 nrow = int(np.ceil(float(n * (n - 1) / 2) / ncol)) #nrow=1 #pylab.rcParams.update({'figure.autolayout': True}) fig, axs = pylab.subplots(nrow, ncol) fig.set_size_inches(5 * ncol, 5 * nrow) #fig.set_canvas(pylab.gcf().canvas) pairs = list(combinations(range(n), 2)) #[:4] pairs = sorted(pairs, key=lambda q: q[0]**2+q[1]**2) # Puts stronger relationships first if colors is not None: colors = (colors - np.min(colors)) / (np.max(colors) - np.min(colors)).clip(1e-7) for ax, pair in zip(axs.flat, pairs): if latent is None: ax.scatter(data[:, pair[0]], data[:, pair[1]], marker='.', edgecolors='none', alpha=alpha) else: # cs = 'rgbcmykrgbcmyk' markers = 'x+.o,<>^^<>,+x.' for j, ind in enumerate(np.unique(latent)): inds = (latent == ind) ax.scatter(data[inds, pair[0]], data[inds, pair[1]], c=colors[inds], cmap=pylab.get_cmap("jet"), marker=markers[j], alpha=0.5, 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='.') pylab.rcParams['font.size'] = 12 #6 pylab.draw() #fig.set_tight_layout(True) fig.tight_layout() 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') #df') pylab.close('all') return True
def plot_nodes_over_data_scattermatrix_hexbin(X, Y, mdl, predictions, distances, activities, saveplot = False): """models_actinf.plot_nodes_over_data_scattermatrix_hexbin Plot models nodes (if applicable) over the hexbinned data expanding dimensions as a scattermatrix. """ idim = X.shape[1] odim = Y.shape[1] numplots = idim * odim + 2 fig = pl.figure() fig.suptitle("Predictions over data xy scattermatrix/hexbin (%s)" % (mdl.__class__.__name__)) gs = gridspec.GridSpec(idim, odim) figaxes = [] for i in range(idim): figaxes.append([]) for o in range(odim): figaxes[i].append(fig.add_subplot(gs[i,o])) err = 0 # colsa = ["k", "r", "g", "c", "m", "y"] # colsb = ["k", "r", "g", "c", "m", "y"] colsa = ["k" for col in range(idim)] colsb = ["r" for col in range(odim)] for i in range(odim): # odim * 2 for j in range(idim): # pl.subplot(numplots, 1, (i*idim)+j+1) ax = figaxes[j][i] # target = Y[h,i] # X__ = X_[j] # X[h,j] # err += np.sum(np.square(target - prediction)) # ax.plot(X__, [target], colsa[j] + ".", alpha=0.25, label="target_%d" % i) # ax.plot(X__, [prediction[0,i]], colsb[j] + "o", alpha=0.25, label="pred_%d" % i) # ax.plot(X[:,j], Y[:,i], colsa[j] + ".", alpha=0.25, label="target_%d" % i) ax.hexbin(X[:,j], Y[:,i], gridsize = 20, alpha=0.75, cmap=pl.get_cmap("gray")) ax.plot(X[:,j], predictions[:,i], colsb[j] + "o", alpha=0.15, label="pred_%d" % i, markersize=8) # pred1 = mdl.filter_e.neuron(mdl.filter_e.flat_to_coords(activities_sorted[-1])) # ax.plot(X__, [pred1], "ro", alpha=0.5) # pred2 = mdl.filter_e.neuron(mdl.filter_e.flat_to_coords(activities_sorted[-2])) # ax.plot(X__, [pred2], "ro", alpha=0.25) # print("accum total err = %f" % (err / X.shape[0] / (idim * odim))) if saveplot: filename = "plot_nodes_over_data_scattermatrix_hexbin_%s.jpg" % (mdl.__class__.__name__,) savefig(fig, filename) fig.show()
def plot_hebbsom_links_distances_activations(X, Y, mdl, predictions, distances, activities, saveplot = False): """plot the hebbian link matrix, and all node distances and activities for all inputs""" hebblink_log = np.log(mdl.hebblink_filter.T + 1.0) fig = pl.figure() fig.suptitle("Debugging SOM: hebbian links, distances, activities (%s)" % (mdl.__class__.__name__)) gs = gridspec.GridSpec(4, 1) # pl.plot(X, Y, "k.", alpha=0.5) # pl.subplot(numplots, 1, numplots-1) ax1 = fig.add_subplot(gs[0]) ax1.set_title('hebbian associative links') # im1 = ax1.imshow(mdl.hebblink_filter, interpolation="none", cmap=pl.get_cmap("gray")) im1 = ax1.pcolormesh(hebblink_log, cmap=pl.get_cmap("gray")) ax1.set_xlabel("in (e)") ax1.set_ylabel("out (p)") cbar = fig.colorbar(mappable = im1, ax=ax1, orientation="horizontal") ax2 = fig.add_subplot(gs[1]) ax2.set_title('distances over time') distarray = np.array(distances) # print("distarray.shape", distarray.shape) pcm = ax2.pcolormesh(distarray.T) cbar = fig.colorbar(mappable = pcm, ax=ax2, orientation="horizontal") # pl.subplot(numplots, 1, numplots) ax3 = fig.add_subplot(gs[2]) ax3.set_title('activations propagated via hebbian links') actarray = np.array(activities) # print("actarray.shape", actarray.shape) pcm = ax3.pcolormesh(actarray.T) cbar = fig.colorbar(mappable = pcm, ax=ax3, orientation="horizontal") ax4 = fig.add_subplot(gs[3]) ax4.set_title('flattened link table') ax4.plot(hebblink_log.flatten()) # print("hebblink_log", hebblink_log) if saveplot: filename = "plot_hebbsom_links_distances_activations_%s.jpg" % (mdl.__class__.__name__,) savefig(fig, filename) fig.show()
def plot_predictions_over_data(X, Y, mdl, saveplot = False, ax = None, datalim = 1000): do_hexbin = False if X.shape[0] > 4000: do_hexbin = False # True X = X[-4000:] Y = Y[-4000:] # plot prediction idim = X.shape[1] odim = Y.shape[1] numsamples = 1 # 2 Y_samples = [] for i in range(numsamples): Y_samples.append(mdl.predict(X)) # print("Y_samples[0]", Y_samples[0]) fig = pl.figure() fig.suptitle("Predictions over data xy (numsamples = %d, (%s)" % (numsamples, mdl.__class__.__name__)) gs = gridspec.GridSpec(odim, 1) for i in range(odim): ax = fig.add_subplot(gs[i]) target = Y[:,i] if do_hexbin: ax.hexbin(X, Y, gridsize = 20, alpha=1.0, cmap=pl.get_cmap("gray")) else: ax.plot(X, target, "k.", label="Y_", alpha=0.5) for j in range(numsamples): prediction = Y_samples[j][:,i] # print("X", X.shape, "prediction", prediction.shape) # print("X", X, "prediction", prediction) if do_hexbin: ax.hexbin(X[:,i], prediction, gridsize = 30, alpha=0.6, cmap=pl.get_cmap("Reds")) else: ax.plot(X[:,i], prediction, "r.", label="Y_", alpha=0.25) # get limits xlim = ax.get_xlim() ylim = ax.get_ylim() error = target - prediction mse = np.mean(np.square(error)) mae = np.mean(np.abs(error)) xran = xlim[1] - xlim[0] yran = ylim[1] - ylim[0] ax.text(xlim[0] + xran * 0.1, ylim[0] + yran * 0.3, "mse = %f" % mse) ax.text(xlim[0] + xran * 0.1, ylim[0] + yran * 0.5, "mae = %f" % mae) if saveplot: filename = "plot_predictions_over_data_%s.jpg" % (mdl.__class__.__name__,) savefig(fig, filename) fig.show()
def _plot_features(out_dir, signal, sampling_rate, logmel, delta, delta_delta, specgram, filename): try: os.makedirs(out_dir) except: pass sampling_interval = 1.0 / sampling_rate times = np.arange(len(signal)) * sampling_interval pylab.clf() plt.rcParams['font.size'] = 18 pylab.figure(figsize=(len(signal) / 2000, 16)) ax1 = pylab.subplot(511) pylab.plot(times, signal) pylab.title("Waveform") pylab.xlabel("Time [sec]") pylab.ylabel("Amplitude") pylab.xlim([0, len(signal) * sampling_interval]) ax2 = pylab.subplot(512) specgram = np.log(specgram) pylab.pcolormesh(np.arange(0, specgram.shape[0]), np.arange(0, specgram.shape[1]) * 8000 / specgram.shape[1], specgram.T, cmap=pylab.get_cmap("jet")) pylab.title("Spectrogram") pylab.xlabel("Time [sec]") pylab.ylabel("Frequency [Hz]") pylab.colorbar() ax3 = pylab.subplot(513) pylab.pcolormesh(np.arange(0, logmel.shape[0]), np.arange(1, 41), logmel.T, cmap=pylab.get_cmap("jet")) pylab.title("Log mel filter bank features") pylab.xlabel("Frame") pylab.ylabel("Filter number") pylab.colorbar() ax4 = pylab.subplot(514) pylab.pcolormesh(np.arange(0, delta.shape[0]), np.arange(1, 41), delta.T, cmap=pylab.get_cmap("jet")) pylab.title("Deltas") pylab.xlabel("Frame") pylab.ylabel("Filter number") pylab.colorbar() ax5 = pylab.subplot(515) pylab.pcolormesh(np.arange(0, delta_delta.shape[0]), np.arange(1, 41), delta_delta.T, cmap=pylab.get_cmap("jet")) pylab.title("Delta-deltas") pylab.xlabel("Frame") pylab.ylabel("Filter number") pylab.colorbar() pylab.tight_layout() pylab.savefig(os.path.join(out_dir, filename), bbox_inches="tight")
