我们从Python开源项目中，提取了以下19个代码示例，用于说明如何使用seaborn.tsplot()。
def plot_time_series(df, target, tag='eda', directory=None): r"""Plot time series data. Parameters ---------- df : pandas.DataFrame The dataframe containing the ``target`` feature. target : str The target variable for the time series plot. tag : str Unique identifier for the plot. directory : str, optional The full specification of the plot location. Returns ------- None : None. References ---------- http://seaborn.pydata.org/generated/seaborn.tsplot.html """ logger.info("Generating Time Series Plot") # Generate the time series plot ts_plot = sns.tsplot(data=df[target]) ts_fig = ts_plot.get_figure() # Save the plot write_plot('seaborn', ts_fig, 'time_series_plot', tag, directory) # # Function plot_candlestick #
def plot_reward_by_episode(self, ax=None): self.make_palette() full_data = pd.DataFrame() for idx, (benchmark_data, name) in enumerate(self.benchmarks): plot_data = to_timeseries(benchmark_data, x_label="Episode", y_label="Average Episode Reward", target=rewards_by_episode, cut_x=benchmark_data.min_x('episodes'), smooth=10) plot_data['Benchmark'] = name full_data = full_data.append(plot_data) plot = sns.tsplot(data=full_data, time="Episode", value="Average Episode Reward", unit="experiment", condition='Benchmark', ax=ax, ci=[68, 95], color=self.palette) return plot
def plot_reward_by_timestep(self, ax=None): self.make_palette() full_data = pd.DataFrame() for idx, (benchmark_data, name) in enumerate(self.benchmarks): plot_data = to_timeseries(benchmark_data, x_label="Time step", y_label="Average Episode Reward", target=rewards_by_timestep, cut_x=benchmark_data.min_x('timesteps'), smooth=10) plot_data['Benchmark'] = name full_data = full_data.append(plot_data) plot = sns.tsplot(data=full_data, time="Time step", value="Average Episode Reward", unit="experiment", condition='Benchmark', ax=ax, ci=[68, 95], color=self.palette) return plot
def plot_reward_by_second(self, ax=None): self.make_palette() full_data = pd.DataFrame() for idx, (benchmark_data, name) in enumerate(self.benchmarks): plot_data = to_timeseries(benchmark_data, x_label="Second", y_label="Average Episode Reward", target=rewards_by_second, cut_x=benchmark_data.min_x('seconds'), smooth=10) plot_data['Benchmark'] = name full_data = full_data.append(plot_data) plot = sns.tsplot(data=full_data, time="Second", value="Average Episode Reward", unit="experiment", condition='Benchmark', ax=ax, ci=[68, 95], color=self.palette) return plot
def phase1_plot_update(f, ax1, ax2, data, passed, results, fails, failure_criteria, progress): ax1.cla() ax2.cla() sns.tsplot(data.ch1, time=data.time, color="g" if passed else "r", ax=ax1, interpolate=True) if len(results) > 1: try: sns.distplot(results, norm_hist=False, rug=True, ax=ax2) except Exception: pass ax1.set(title='Transition Waveform', xlabel='Time (sec)', ylabel='Amplitude (V)') ax2.set(title="Risetime Histogram", ylabel="Density", xlabel="Risetime (sec)") ax2.annotate('Outside Spec: %d / %d\nCompleted %d%%' % (len(fails), len(results), progress), xy=(0.75,0.90), xycoords='axes fraction', fontsize=14) xlims = ax2.get_xlim() ax2.axvspan(failure_criteria,xlims[1] - 0.001*(xlims[1] - xlims[0]), alpha=0.1, color='red') plt.pause(0.01)
def phase2_plot_update(f, ax1, data, passed, peak, hf1, hf2, progress): # Update the plot with latest measurement ax1.cla() freq_mhz = map(lambda x: x/1e6, data.frequency) sns.tsplot(data.ch1, time=freq_mhz, ax=ax1, interpolate=True) ax1.plot(peak[0]/1e6, peak[1], 'v') ax1.set(title='Beatnote Spectrum', xlabel='Frequency (MHz)', ylabel='Power (dBm)') ax1.annotate('Peak (%.2f MHz)\nLinewidth (%.2f kHz)\nCompleted %d%%' % (peak[0]/1e6, (hf2[0]-hf1[0])/1e3, progress), xy=(0.80,0.90), xycoords='axes fraction', fontsize=14) ax1.axvspan(hf1[0]/1e6,hf2[0]/1e6, alpha=0.1, color='green' if passed else 'red') plt.pause(0.01)
def show_km(y, n=4, c=['b', 'g', 'r', 'k'], title='KMeans Clustering'): km = cluster.KMeans(n) yi = km.fit_predict(y) #c = ['b', 'g', 'r', 'k'] for i in range(n): sns.tsplot(y[yi==i], color=c[i]) plt.title(title)
def show_cluster(y, yi, c=['b', 'g', 'r', 'k'], title='Clustering Resutls'): #km = cluster.KMeans(n) #yi = km.fit_predict(y) #c = ['b', 'g', 'r', 'k'] set_yi = list(set(yi)) n = len(set_yi) for i in set_yi: sns.tsplot(y[yi==i], color=c[set_yi.index(i)]) plt.title(title)
def plot( self): sns.tsplot(data=self.pdo, time="alpha", unit="unit", condition="Method", value="r2") plt.xscale('log') plt.ylabel( r'$r^2$')
def show_both_cell( self, c, cell_id): X1part = self.X1part X2part = self.X2part y = self.y cell = self.cell X1_ylim = self.X1_ylim X2_ylim = self.X2_ylim cmethod = self.cmethod X3_int = X2part[ np.where(y==c)[0],:] X3_vel = X1part[ np.where(y==c)[0],:] cell3 = cell[ np.where(y==c)[0]] km = getattr(cluster, cmethod)(**cparam_d) y3 = km.fit_predict( X3_int) # redefine based on cell_id X3_int = X3_int[ np.where(cell3==cell_id)[0],:] X3_vel = X3_vel[ np.where(cell3==cell_id)[0],:] y3 = y3[np.where(cell3==cell_id)[0]] n_0 = X3_int[ np.where( y3==0)[0]].shape[0] n_1 = X3_int[ np.where( y3==1)[0]].shape[0] plt.figure(figsize=(9,4)) plt.subplot(1,2,1) if n_0 > 0: sns.tsplot( X3_int[ np.where( y3==0)[0],:], color="blue") if n_1 > 0: sns.tsplot( X3_int[ np.where( y3==1)[0],:], color="green") plt.ylim(X2_ylim) plt.title("Cluster{0}:Intensity {1}:{2}".format(c, n_0, n_1)) #plt.show() plt.subplot(1,2,2) #print("Velocity") if n_0 > 0: sns.tsplot( X3_vel[ np.where( y3==0)[0],:], color="blue") if n_1 > 0: sns.tsplot( X3_vel[ np.where( y3==1)[0],:], color="green") plt.ylim(X1_ylim) plt.title("Cluster{0}:Velocity {1}:{2}".format(c, n_0, n_1)) plt.show()
def np_tsplot( N): sns.tsplot( get_ts_df( N), time='time', unit='unit', value='value')
def tsplot_clusters( X, y): """ X, 2d array with y, cluster index """ for yit in list(set(y)): sns.tsplot( X[y==yit,:], color=plt.cm.rainbow(yit/max(y)))
def tsplot(df, add_plot=None, figsize=None, xlim=None, ylim=None, xlabel=None, ylabel=None, label_size=None, tick_size=None, title=None, title_size=None, err=0, **kwargs): """ :param df: :param figsize: :param xlim: :param ylim: :param xlabel: :param ylabel: :param label_size: :param tick_size: :param title: :param title_size: :param err: 0 = standard deviation, 1 = standard error :param kwargs: :return: """ if not add_plot: fig, axes = plt.subplots(1,1,figsize=figsize) else: fig, axes = add_plot fig.patch.set_facecolor('white') axes.spines['top'].set_visible(False) axes.spines['right'].set_visible(False) if xlim: axes.set_xlim(xlim) if ylim: axes.set_ylim(ylim) if title: axes.set_title(title, size=title_size) if xlabel: axes.set_xlabel(xlabel, size=label_size) else: axes.set_xlabel('Time (s)', size=label_size) if ylabel: axes.set_ylabel(ylabel, size=label_size) else: axes.set_ylabel('Responses', size=label_size) axes.tick_params(labelsize=tick_size, direction='out', top='off', right='off') if err: sns.tsplot(df.T.values, err_style='sterr_band', ax=axes, **kwargs) else: sns.tsplot(df.T.values, err_style='std_band', ax=axes, **kwargs) return fig, axes
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
def show_both( self, c): X1part = self.X1part X2part = self.X2part y = self.y cell = self.cell X1_ylim = self.X1_ylim X2_ylim = self.X2_ylim cmethod = self.cmethod cparam_d = self.cparam_d #print("Cluster:", c) X3_int = X2part[ np.where(y==c)[0],:] X3_vel = X1part[ np.where(y==c)[0],:] #km = cluster.KMeans(2) #km = getattr(cluster, cmethod)(2) km = getattr(cluster, cmethod)(**cparam_d) y3 = km.fit_predict( X3_int) plt.figure(figsize=(9,4)) plt.subplot(1,2,1) #print("Intensity") n_0 = X3_int[ np.where( y3==0)[0]].shape[0] n_1 = X3_int[ np.where( y3==1)[0]].shape[0] sns.tsplot( X3_int[ np.where( y3==0)[0],:], color="blue") sns.tsplot( X3_int[ np.where( y3==1)[0],:], color="green") plt.ylim(X2_ylim) plt.title("Cluster{0}:X2 {1}:{2}".format(c, n_0, n_1)) #plt.show() plt.subplot(1,2,2) #print("Velocity") sns.tsplot( X3_vel[ np.where( y3==0)[0],:], color="blue") sns.tsplot( X3_vel[ np.where( y3==1)[0],:], color="green") plt.ylim(X1_ylim) plt.title("Cluster{0}:X1 {1}:{2}".format(c, n_0, n_1)) plt.show() cell3 = cell[ np.where(y==c)[0]] plt.subplot(1,2,1) plt.stem( cell3[np.where( y3==0)[0]], linefmt='b-', markerfmt='bo') plt.title("Cell Index - Subcluster 1") plt.subplot(1,2,2) plt.stem( cell3[np.where( y3==1)[0]], linefmt='g-', markerfmt='go') plt.title("Cell Index - Subcluster 2") plt.show() return y3
def show_both_kmeans( self, c): X1part = self.X1part X2part = self.X2part y = self.y cell = self.cell X1_ylim = self.X1_ylim X2_ylim = self.X2_ylim cmethod = self.cmethod cparam_d = self.cparam_d nc = cparam_d["n_clusters"] #print("Cluster:", c) X3_int = X2part[ y==c,:] X3_vel = X1part[ y==c,:] #km = cluster.KMeans(2) #km = getattr(cluster, cmethod)(2) assert cmethod == "KMeans" km = cluster.KMeans( nc) y3 = km.fit_predict( X3_int) plt.figure(figsize=(9,4)) plt.subplot(1,2,1) #print("Intensity") n_l = [ X3_int[ y3==i].shape[0] for i in range(nc)] for i in range(nc): sns.tsplot( X3_int[ y3==i,:], color=plt.cm.rainbow(i/nc)) plt.ylim(X2_ylim) plt.title("Cluster{0}:X2 {1}".format(c, n_l)) #plt.show() plt.subplot(1,2,2) #print("Velocity") for i in range(nc): sns.tsplot( X3_vel[ y3==i,:], color=plt.cm.rainbow(i/nc)) plt.ylim(X1_ylim) plt.title("Cluster{0}:X1 {1}".format(c, n_l)) plt.show() return y3
def plotScalabilityResultsForTest(dfs, test, colName, title, xlabel, savePath, ax=None, logxscale=False, logyscale=True): dfs = filter(lambda df: df['test'][0] == test, dfs) if ax is None: plt.figure() ax = plt.gca() lines = [] labels = [] for df in dfs: x = df[colName] y = df[TIME_COL] / 60. # convert to minutes algo = df[ALGORITHM_COL][0] p = ALGO_2_LINE_PARAMS[algo] line, = ax.plot(x, y, label=algo, color=p.color, lw=p.width, ls=p.style) ax.set_xlim([np.min(x), np.max(x)]) lines.append(line) labels.append(algo) # seaborn tsplot version; confidence intervals like invisibly thin # around the lines, so not much point (although verify this once # all experiments run TODO) # df = pd.concat(dfs, ignore_index=True) # colors = dict([algo, p.color] for algo, p in ALGO_2_LINE_PARAMS.items()]) # sb.tsplot(time=colName, value=TIME_COL, unit=SEED_COL, # condition=ALGORITHM_COL, data=df, ax=ax) # lines, labels = ax.get_legend_handles_labels() if logxscale: ax.set_xscale('log') if logyscale: ax.set_yscale('log') # x = df[colName] # ax.set_xlim([np.min(x), np.max(x)]) ax.set_title(title) ax.set_xlabel(xlabel) ax.set_ylabel("Runtime (min)") if savePath: plt.savefig(savePath) return lines, labels
