我们从Python开源项目中，提取了以下30个代码示例，用于说明如何使用seaborn.set_context()。
def plot_heatmap(): data = load_dispersion_data() linkage = data["linkage"] sns.set_context("notebook", font_scale=1.25) p = sns.clustermap(data=data["dispersion"], row_linkage=linkage, col_linkage=linkage, vmin=0.50, vmax=1.00, cmap=cmap_clustermap, figsize=(12, 10)) labels = p.data2d.columns # Sanity check, make sure the plotted dendrogram matches the saved values assert((labels == data["dendrogram_order"]).all())
def __init__(self, parent, mlca, width=6, height=6, dpi=100): figure = Figure(figsize=(width, height), dpi=dpi, tight_layout=True) axes = figure.add_subplot(111) super(LCAResultsPlot, self).__init__(figure) self.setParent(parent) activity_names = [format_activity_label(next(iter(f.keys()))) for f in mlca.func_units] # From https://stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html cmap = sns.cubehelix_palette(8, start=.5, rot=-.75, as_cmap=True) hm = sns.heatmap( # mlca.results / np.average(mlca.results, axis=0), # Normalize to get relative results mlca.results, annot=True, linewidths=.05, cmap=cmap, xticklabels=["\n".join(x) for x in mlca.methods], yticklabels=activity_names, ax=axes, square=False, ) hm.tick_params(labelsize=8) self.setMinimumSize(self.size()) # sns.set_context("notebook")
def plot_evaluation_episode_reward(): pylab.clf() sns.set_context("poster") pylab.plot(0, 0) episodes = [0] average_scores = [0] median_scores = [0] for n in xrange(len(csv_evaluation)): params = csv_evaluation[n] episodes.append(params[0]) average_scores.append(params[1]) median_scores.append(params[2]) pylab.plot(episodes, average_scores, sns.xkcd_rgb["windows blue"], lw=2) pylab.xlabel("episodes") pylab.ylabel("average score") pylab.savefig("%s/evaluation_episode_average_reward.png" % args.plot_dir) pylab.clf() pylab.plot(0, 0) pylab.plot(episodes, median_scores, sns.xkcd_rgb["windows blue"], lw=2) pylab.xlabel("episodes") pylab.ylabel("median score") pylab.savefig("%s/evaluation_episode_median_reward.png" % args.plot_dir)
def plot_group(data_frame, path_output): # optional import import seaborn as sns path_output_image = os.path.join(path_output, "summary_statistics.png") # # Plotting swarmplot # plt.figure(num=None, figsize=(15, 7), dpi=120) # sns.set_style("whitegrid") # # plt.title('Violin plot with single measurements') # sns.violinplot(x="Group", y="DAB+ area", data=data_frame, inner=None) # sns.swarmplot(x="Group", y="DAB+ area", data=data_frame, color="w", alpha=.5) # plt.savefig(path_output_image) # # plt.tight_layout() sns.set_style("whitegrid") sns.set_context("talk") plt.figure(num=None, figsize=(15, 7), dpi=120) plt.ylim(0, 100) plt.title('Box plot') sns.boxplot(x="Group", y="DAB+ area, %", data=data_frame) plt.tight_layout() plt.savefig(path_output_image, dpi=300)
def plot_predict_proba(y_pred_probs, clf, pdf=None): """Plots the predict proba distribution""" fig, ax = plt.subplots(1, figsize=(18, 8)) sns.set_style("white") sns.set_context("poster", font_scale=2.25, rc={"lines.linewidth": 1.25, "lines.markersize": 8}) sns.distplot(y_pred_probs) plt.xlabel('predict_proba') plt.ylabel('frequency') plt.title(clf + ' proba') if pdf: pdf.savefig() plt.close() else: plt.show()
def swarm(data,x,y,xscale='linear',yscale='linear'): # set default pretty settings from Seaborn sns.set(style="white", palette="muted") sns.set_context("notebook", font_scale=1, rc={"lines.linewidth": 0.2}) # createthe plot g = sns.swarmplot(x=x, y=y, data=data, palette='RdYlGn') plt.tick_params(axis='both', which='major', pad=10) g.set(xscale=xscale) g.set(yscale=yscale) # Setting plot limits start = data[y].min().min() plt.ylim(start,); sns.despine()
def histogram(data,variables): sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 0}) sns.set_style('white') var_length = len(variables) fig, axes = plt.subplots(1, var_length, figsize=(19, 5)) for i in range(var_length): axes[i].hist(data[variables[i]],lw=0,color="indianred",bins=8); axes[i].tick_params(axis='both', which='major', pad=15) axes[i].set_xlabel(variables[i]) axes[i].set_yticklabels(""); sns.despine(left=True)
def correlation(data,title=''): corr = data.corr(method='spearman') mask = np.zeros_like(corr) mask[np.triu_indices_from(mask)] = True sns.set(style="white") sns.set_context("notebook", font_scale=2, rc={"lines.linewidth": 0.3}) rcParams['figure.figsize'] = 25, 12 rcParams['font.family'] = 'Verdana' rcParams['figure.dpi'] = 300 g = sns.heatmap(corr, mask=mask, linewidths=1, cmap="RdYlGn", annot=False) g.set_xticklabels(data,rotation=25,ha="right"); plt.tick_params(axis='both', which='major', pad=15);
def plotPrediction(pred): """ Plots the prediction than encodes it to base64 :param pred: prediction accuracies :return: base64 encoded image as string """ labels = ['setosa', 'versicolor', 'virginica'] sns.set_context(rc={"figure.figsize": (5, 5)}) with sns.color_palette("RdBu_r", 3): ax = sns.barplot(x=labels, y=pred) ax.set(ylim=(0, 1)) # Base64 encode the plot stringIObytes = cStringIO.StringIO() sns.plt.savefig(stringIObytes, format='jpg') sns.plt.show() stringIObytes.seek(0) base64data = base64.b64encode(stringIObytes.read()) return base64data
def phase1_plot_setup(): # Set up a 1x2 plot f, (ax1, ax2) = plt.subplots(1,2) f.suptitle('Phase 1 - Rise Times', fontsize=18, fontweight='bold') # Choose a colour palette and font size/style colours = sns.color_palette("muted") sns.set_context('poster') # Maximise the plotting window plot_backend = matplotlib.get_backend() mng = plt.get_current_fig_manager() if plot_backend == 'TkAgg': mng.resize(*mng.window.maxsize()) elif plot_backend == 'wxAgg': mng.frame.Maximize(True) elif plot_backend == 'Qt4Agg': mng.window.showMaximized() return f, ax1, ax2
def phase2_plot_setup(): # Set up a 1x1 plot f, ax1 = plt.subplots(1,1) f.suptitle('Phase 2 - Line Width', fontsize=18, fontweight='bold') # Choose a colour palette and font size/style colours = sns.color_palette("muted") sns.set_context('poster') # Maximise the plotting window plot_backend = matplotlib.get_backend() mng = plt.get_current_fig_manager() if plot_backend == 'TkAgg': mng.resize(*mng.window.maxsize()) elif plot_backend == 'wxAgg': mng.frame.Maximize(True) elif plot_backend == 'Qt4Agg': mng.window.showMaximized() return f, ax1
def plotSleepValueHeatmap(intradayStats, sleepValue=1): sns.set_context("poster") sns.set_style("darkgrid") xTicksDiv = 20 #stepSize = int(len(xticks)/xTicksDiv) stepSize = 60 xticks = [x for x in intradayStats.columns.values] keptticks = xticks[::stepSize] xticks = ['' for _ in xticks] xticks[::stepSize] = keptticks plt.figure(figsize=(16, 4.2)) g = sns.heatmap(intradayStats.loc[sleepValue].reshape(1,-1)) g.set_xticklabels(xticks, rotation=45) g.set_yticklabels([]) g.set_ylabel(sleepStats.SLEEP_VALUES[sleepValue]) plt.tight_layout() sns.plt.show()
def style_matplotlib_for_notebook(self): sns.set_context("notebook", rc=self.cfg['matplotlib']['notebook'])
def sb_initialize_fonts(self, font_size=12, title_size=12, label_size=10): import seaborn as sns sns.set_context("paper", rc={"font.size":font_size,"axes.titlesize":title_size,"axes.labelsize":label_size}) # end of sb_initialize_fonts
def plot_episode_reward(): pylab.clf() sns.set_context("poster") pylab.plot(0, 0) episodes = [0] scores = [0] for n in xrange(len(csv_episode)): params = csv_episode[n] episodes.append(params[0]) scores.append(params[1]) pylab.plot(episodes, scores, sns.xkcd_rgb["windows blue"], lw=2) pylab.xlabel("episodes") pylab.ylabel("score") pylab.savefig("%s/episode_reward.png" % args.plot_dir)
def plot_training_episode_highscore(): pylab.clf() sns.set_context("poster") pylab.plot(0, 0) episodes = [0] highscore = [0] for n in xrange(len(csv_training_highscore)): params = csv_training_highscore[n] episodes.append(params[0]) highscore.append(params[1]) pylab.plot(episodes, highscore, sns.xkcd_rgb["windows blue"], lw=2) pylab.xlabel("episodes") pylab.ylabel("highscore") pylab.savefig("%s/training_episode_highscore.png" % args.plot_dir)
def plot_learning_function(xdata, ydata, yerr, order, aplot, poly): with sns.axes_style('whitegrid'): sns.set_context('paper') xp = np.linspace(np.min(xdata), np.max(xdata), 1000)[:, None] plt.figure() plt.errorbar(xdata, ydata, yerr, label='Nearest similarity', marker='s') plt.plot(xp, poly(xp), '-', label='Learning function (poly of order {})'.format(order)) # plt.plot(xp, least_squares_mdl(res.x, xp), '-', label='least squares') plt.xlabel(r'Mutation level') plt.ylabel(r'Average similarity (not normalised)') plt.legend(loc='lower left') plt.savefig(aplot, transparent=True, bbox_inches='tight') plt.close()
def kde(x,y,title='',color='YlGnBu',xscale='linear',yscale='linear'): sns.set_style('white') sns.set_context('notebook', font_scale=1, rc={"lines.linewidth": 0.5}) g = sns.kdeplot(x,y,shade=True, cut=2, cmap=color, shade_lowest=False, legend=True, set_title="test") plt.tick_params(axis='both', which='major', pad=10) sns.plt.title(title) g.set(xscale=xscale) g.set(yscale=yscale) sns.despine()
def bubble(data,x,y,hue,bsize,palette='Reds',xscale='linear',yscale='linear',title='',suptitle=0): if suptitle == 0: suptitle = bsize sns.set(style="whitegrid") sns.set_context("notebook", font_scale=3, rc={"lines.linewidth": 0.3}) sns.set_color_codes("bright") size = (1500 / float(data[bsize].max())) size = data[bsize] * size g = sns.PairGrid(data, hue=hue, palette=palette, y_vars=y, x_vars=x, size=12, aspect=3) g.map(plt.scatter, s=size); g.set(xscale=xscale) g.set(yscale=yscale) g.add_legend(title=hue, bbox_to_anchor=(0.9, 0.6)) plt.title(title, fontsize=48, y=1.12, color="gray"); plt.suptitle("size = " + suptitle, verticalalignment='top', fontsize=35, y=1.01, x=0.48, color="gray") plt.xlabel(x, fontsize=38, labelpad=30, color="gray"); plt.ylabel(y, fontsize=38, labelpad=30, color="gray"); plt.tick_params(axis='both', which='major', pad=25) plt.axhline(linewidth=2.5, color="black"); plt.axvline(linewidth=2.5, color="black"); plt.ylim(0,); plt.xlim(0,);
def update_graph(dff): sns.set_style("white") sns.set_style("ticks") sns.set_context("talk") dff.ix[::10].plot("date", "overdue", figsize=(7, 4), lw=3) onemonth = datetime.timedelta(30) plt.xlim(dff.date.min(), dff.date.max()+onemonth) plt.ylabel("Overdue dataset") plt.xlabel("Date") plt.savefig("docs/graph.png")
def setupPlot(context="talk", style="white", font_scale=1.0): sns.set_context(context, font_scale=font_scale) sns.set_style(style)
def set_context(context="talk"): sns.set_context(context)
def analyse(sm, labels, root='', plotting_context=None, file_format='pdf', force_silhouette=False, threshold=None): """Perform analysis. Parameters ---------- sm : array, shape = [n_samples, n_samples] Precomputed similarity matrix. labels : array, shape = [n_samples] Association of each sample to a clsuter. root : string The root path for the output creation. plotting_context : dict, None, or one of {paper, notebook, talk, poster} See seaborn.set_context(). file_format : ('pdf', 'png') Choose the extension for output images. """ sns.set_context(plotting_context) if force_silhouette or sm.shape[0] < 8000: silhouette.plot_clusters_silhouette(1. - sm.toarray(), labels, max(labels), root=root, file_format=file_format) else: logging.warn( "Silhouette analysis is not performed due to the " "matrix dimensions. With a matrix %ix%i, you would need to " "allocate %.2fMB in memory. If you know what you are doing, " "specify 'force_silhouette = True' in the config file in %s, " "then re-execute the analysis.\n", sm.shape[0], sm.shape[0], sm.shape[0]**2 * 8 / (2.**20), root) # Generate dendrogram import scipy.spatial.distance as ssd Z = hierarchy.linkage(ssd.squareform(1. - sm.toarray()), method='complete', metric='euclidean') plt.close() fig, (ax) = plt.subplots(1, 1) fig.set_size_inches(20, 15) hierarchy.dendrogram(Z, ax=ax) ax.axhline(threshold, color="red", linestyle="--") plt.show() filename = os.path.join(root, 'dendrogram_{}.{}' .format(extra.get_time(), file_format)) fig.savefig(filename) logging.info('Figured saved %s', filename) plt.close() fig, (ax) = plt.subplots(1, 1) fig.set_size_inches(20, 15) plt.hist(1. - sm.toarray(), bins=50, normed=False) plt.ylim([0, 10]) fig.savefig(filename + "_histogram_distances.pdf")
def bubble(data,x,y,hue,bsize,palette='Reds',xscale='linear',yscale='linear',title='',suptitle=0,xlim_start=0,ylim_start=0,xlim_end=0,ylim_end=0): import seaborn as sns import matplotlib.pyplot as plt """ x > should be int or float y > should be int or float hue > should be boolean or category bsize > should be int or float """ if suptitle == 0: suptitle = bsize y_modifier = (data[y].max() - data[y].min()) * 0.1 x_modifier = (data[x].max() - data[x].min()) * 0.1 if ylim_start == 0: ylim_start = data[y].min() if xlim_start == 0: xlim_start = data[x].min() if ylim_end == 0: ylim_end = data[y].max() + y_modifier if xlim_end == 0: xlim_end = data[x].max() + (x_modifier * 2) sns.set(style="whitegrid") sns.set_context("notebook", font_scale=3, rc={"lines.linewidth": 0.3}) sns.set_color_codes("bright") size = (1500 / float(data[bsize].max())) size = data[bsize] * size g = sns.PairGrid(data, hue=hue, palette=palette, y_vars=y, x_vars=x, size=12, aspect=3) g.map(plt.scatter, s=5000); g.set(xscale=xscale) g.set(yscale=yscale) g.add_legend(title=hue, bbox_to_anchor=(0.9, 0.6)) plt.title(title, fontsize=48, y=1.12, color="gray"); plt.suptitle("size = " + suptitle, verticalalignment='top', fontsize=35, y=1.01, x=0.48, color="gray") plt.xlabel(x, fontsize=38, labelpad=30, color="gray"); plt.ylabel(y, fontsize=38, labelpad=30, color="gray"); plt.tick_params(axis='both', which='major', pad=25) plt.axhline(linewidth=2.5, color="black"); plt.axvline(linewidth=2.5, color="black"); plt.ylim(ylim_start,ylim_end); plt.xlim(xlim_start,xlim_end);
def bars(data,color='black',title=''): data = pd.DataFrame(data.value_counts()) data = data.reset_index() data.columns = ['keyword','value'] data['keyword'] = data['keyword'][1:] data = data.dropna() data = data.reset_index(drop=True) data = data.sort_values('value',ascending=False) sns.set_context("notebook", font_scale=1.2, rc={"lines.linewidth": 0}) x = data.head(20)['keyword'].astype(str) y = data.head(20)['value'].astype(int) f, ax = plt.subplots(figsize=(16, 3)) sns.set_style('white') ## change color of the bar based on value colors = [color if _y >=0 else 'red' for _y in y] sns.barplot(x, y, palette=colors, ax=ax) plt.title(title, fontsize=18, y=1.12, color="gray"); ax.set_xticklabels('') ax.set_ylabel('') ax.set_xlabel('') ax.tick_params(axis='both', which='major', pad=30) for n, (label, _y) in enumerate(zip(x, y)): ax.annotate( s='{:.1f}'.format(abs(_y)), xy=(n, _y), ha='center',va='center', xytext=(0,-10), size=12, textcoords='offset points', color="white", weight="bold" ) ax.set_yticklabels(""); ax.set_xticklabels(data.head(20)['keyword'],rotation=25,ha="right"); ax.tick_params(axis='both', which='major', pad=15) sns.despine(left=True)
def plotForcingSubplots(tsdata, filename=None, ci=95, show_figure=False, save_fig_kwargs=None): sns.set_context('paper') expList = tsdata['expName'].unique() nrows = 1 ncols = len(expList) width = 2 * ncols height = 2 fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharey=True, figsize=(width, height)) def dataForExp(expName): df = tsdata.query("expName == '%s'" % expName).copy() df.drop(['expName'], axis=1, inplace=True) df = pd.melt(df, id_vars=['runId'], var_name='year') return df for ax, expName in zip(axes, expList): df = dataForExp(expName) pos = expName.find('-') title = expName[:pos] if pos >= 0 else expName ax.set_title(title.capitalize()) tsm.tsplot(df, time='year', unit='runId', value='value', ci=ci, ax=ax) ylabel = 'W m$^{-2}$' if ax == axes[0] else '' ax.set_ylabel(ylabel) ax.set_xlabel('') # no need to say "year" ax.axhline(0, color='navy', linewidth=0.5, linestyle='-') plt.setp(ax.get_xticklabels(), rotation=270) plt.tight_layout() # Save the file if filename: if isinstance(save_fig_kwargs, dict): fig.savefig(filename, **save_fig_kwargs) else: fig.savefig(filename) # Display the figure if show_figure: plt.show() return fig
