我们从Python开源项目中，提取了以下18个代码示例，用于说明如何使用seaborn.kdeplot()。
def plot_hist(baseline_samples, target_samples, true_x, true_y): baseline_samples = baseline_samples.squeeze() target_samples = target_samples.squeeze() bmin, bmax = baseline_samples.min(), baseline_samples.max() ax = sns.kdeplot(baseline_samples, shade=True, color=(0.6, 0.1, 0.1, 0.2)) ax = sns.kdeplot(target_samples, shade=True, color=(0.1, 0.1, 0.6, 0.2)) ax.set_xlim(bmin, bmax) y0, y1 = ax.get_ylim() plt.plot([true_y, true_y], [0, y1 - (y1 - y0) * 0.01], linewidth=1, color='r') plt.title('Predictive' + (f' at {true_x:.2f}' if true_x is not None else '')) fig = plt.gcf() fig.set_size_inches(9, 9) # plt.tight_layout() # pad=0.4, w_pad=0.5, h_pad=1.0) name = utils.DATA_DIR.replace('/', '-') # plt.tight_layout(pad=0.6) utils.save_fig('predictive-at-point-' + name)
def __init__(self, parent): fig = Figure(figsize=(4, 4), dpi=100, tight_layout=True) super(DefaultGraph, self).__init__(fig) self.setParent(parent) sns.set(style="dark") for index, s in zip(range(9), np.linspace(0, 3, 10)): axes = fig.add_subplot(3, 3, index + 1) x, y = np.random.randn(2, 50) cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True) sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes) axes.set_xlim(-3, 3) axes.set_ylim(-3, 3) axes.set_xticks([]) axes.set_yticks([]) fig.suptitle("Activity Browser", y=0.5, fontsize=30, backgroundcolor=(1, 1, 1, 0.5)) self.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding) self.updateGeometry()
def joint_plot(x, y, xlabel=None, ylabel=None, xlim=None, ylim=None, loc="best", color='#0485d1', size=8, markersize=50, kind="kde", scatter_color="r"): with sns.axes_style("darkgrid"): if xlabel and ylabel: g = SubsampleJointGrid(xlabel, ylabel, data=DataFrame(data={xlabel: x, ylabel: y}), space=0.1, ratio=2, size=size, xlim=xlim, ylim=ylim) else: g = SubsampleJointGrid(x, y, size=size, space=0.1, ratio=2, xlim=xlim, ylim=ylim) g.plot_joint(sns.kdeplot, shade=True, cmap="Blues") g.plot_sub_joint(plt.scatter, 1000, s=20, c=scatter_color, alpha=0.3) g.plot_marginals(sns.distplot, kde=False, rug=False) g.annotate(ss.pearsonr, fontsize=25, template="{stat} = {val:.2g}\np = {p:.2g}") g.ax_joint.set_yticklabels(g.ax_joint.get_yticks()) g.ax_joint.set_xticklabels(g.ax_joint.get_xticks()) return g
def plot_kde(data, dir=None, filename="kde", color="Greens"): if dir is None: raise Exception() try: os.mkdir(dir) except: pass fig = pylab.gcf() fig.set_size_inches(16.0, 16.0) pylab.clf() bg_color = sns.color_palette(color, n_colors=256)[0] ax = sns.kdeplot(data[:, 0], data[:,1], shade=True, cmap=color, n_levels=30, clip=[[-4, 4]]*2) ax.set_axis_bgcolor(bg_color) kde = ax.get_figure() pylab.xlim(-4, 4) pylab.ylim(-4, 4) kde.savefig("{}/{}.png".format(dir, filename))
def plot_kde(data, dir=None, filename="kde", color="Greens"): if dir is None: raise Exception() try: os.mkdir(dir) except: pass fig = pylab.gcf() fig.set_size_inches(16.0, 16.0) pylab.clf() bg_color = sns.color_palette(color, n_colors=256)[0] ax = sns.kdeplot(data[:, 0], data[:,1], shade=True, cmap=color, n_levels=30, clip=[[-4, 4]]*2) ax.set_axis_bgcolor(bg_color) kde = ax.get_figure() pylab.xlim(-4, 4) pylab.ylim(-4, 4) kde.savefig("{}/{}".format(dir, filename))
def Features_Cumulative_Frequency(X, y): X = pd.DataFrame(X) y = pd.DataFrame(y) df = pd.concat((y,X), axis=1) columns = ['y'] for i in range(1,df.shape[1]): columns.append('x' + str(i)) df.columns = columns for i in range(1,df.shape[1]): x = 'x' + str(i) sns.kdeplot(df[df.y==0][x], cumulative=True, label="y=0") sns.kdeplot(df[df.y==1][x], cumulative=True, label="y=1") plt.xlabel(x + ' features') plt.ylabel('Cumulative frequency') plt.show()
def plot_decision_function(score_df, partition, output_file): """ Plots the decision function for a given partition (either 'train' or 'test') and saves a figure to file. Arguments: :param score_df: a specific folds decision scores and status :param partition: either 'train' or 'test' will plot performance :param output_file: file to output the figure """ ax = sns.kdeplot(score_df.ix[(score_df.status == 1) & (score_df.partition == partition), :] .decision, color='red', label='Deficient', shade=True) ax = sns.kdeplot(score_df.ix[(score_df.status == 0) & (score_df.partition == partition), :] .decision, color='blue', label='Wild-Type', shade=True) ax.set(xlabel='Decision Function', ylabel='Density') ax.set_title('Classifier Decision Function') sns.despine() plt.tight_layout() plt.savefig(output_file) plt.close()
def plotCorrelation(stats): #columnsToDrop = ['sleep_interval_max_len', 'sleep_interval_min_len', # 'sleep_interval_avg_len', 'sleep_inefficiency', # 'sleep_hours', 'total_hours'] #stats = stats.drop(columnsToDrop, axis=1) g = sns.PairGrid(stats) def corrfunc(x, y, **kws): r, p = scipystats.pearsonr(x, y) ax = plt.gca() ax.annotate("r = {:.2f}".format(r),xy=(.1, .9), xycoords=ax.transAxes) ax.annotate("p = {:.2f}".format(p),xy=(.2, .8), xycoords=ax.transAxes) if p>0.04: ax.patch.set_alpha(0.1) g.map_upper(plt.scatter) g.map_diag(plt.hist) g.map_lower(sns.kdeplot, cmap="Blues_d") g.map_upper(corrfunc) sns.plt.show()
def histogramnd(ax, data, **kwargs): """n-dimensional histogram seaborn based """ scatter_data_raw = data scatter_data_cols = ["x_%d" % (i,) for i in range(data.shape[1])] # prepare dataframe df = pd.DataFrame(scatter_data_raw, columns=scatter_data_cols) g = sns.PairGrid(df) # g.map_diag(plt.hist) g.map_diag(sns.kdeplot) g.map_offdiag(plt.hexbin, cmap="gray", gridsize=30, bins="log"); # logger.log(loglevel_debug, "dir(g)", dir(g)) # print g.diag_axes # print g.axes # for i in range(data.shape[1]): # for j in range(data.shape[1]): # 1, 2; 0, 2; 0, 1 # if i == j: # continue # # column gives x axis, row gives y axis, thus need to reverse the selection for plotting goal # # g.axes[i,j].plot(df["%s%d" % (self.cols_goal_base, j)], df["%s%d" % (self.cols_goal_base, i)], "ro", alpha=0.5) # g.axes[i,j].plot(df["x_%d" % (j,)], df["x_%d" % (i,)], "ro", alpha=0.5) plt.show() # run sns scattermatrix on dataframe # plot_scattermatrix(df, ax = None)
def word_count_by_label(articles: pd.DataFrame): """Show graph of word counts by article label.""" palette = sns.color_palette(palette='hls', n_colors=2) true_news_wc = articles[articles['labels'] == 0]['word_count'] fake_news_wc = articles[articles['labels'] == 1]['word_count'] sns.kdeplot(true_news_wc, bw=3, color=palette[0], label='True News') sns.kdeplot(fake_news_wc, bw=3, color=palette[1], label='Fake News') sns.plt.legend() sns.plt.show()
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 plot_kdes(subjects, axes): ftemp = "correlation_analysis/{}_{}_ifs.pkz" for subj, ax in zip(subjects, axes): sticks = moss.load_pkl(ftemp.format(subj, "sticks")).corrmat rest = moss.load_pkl(ftemp.format(subj, "rest")).corrmat triu = np.triu_indices_from(rest, 1) sns.kdeplot(sticks[triu], color=".15", label="residual", ax=ax) sns.kdeplot(rest[triu], color=".45", dashes=[4, 1], label="resting", ax=ax) plt.setp(axes, xlim=(-.25, .8), ylim=(0, 17), xticks=np.linspace(-.2, .8, 6), yticks=[]) for ax in axes: sns.despine(ax=ax, left=True, trim=True) plt.setp(ax.get_xticklabels(), size=6) plt.setp(ax.get_yticklabels(), size=6) axes[0].legend(bbox_to_anchor=(1.2, .8)) for ax in axes[1:]: ax.legend_ = None
def myplot(x, y): x, y = np.array(x), np.array(y) # bins = np.linspace(-10, 10, 100) x1 = sns.kdeplot(x, shade=True) x2 = sns.kdeplot(y, shade=True) x1.set_xlabel('Value') x2.set_ylabel('Percentage') # pyplot.hist(x, bins, alpha=0.5, label='x') # pyplot.hist(y, bins, alpha=0.5, label='y') pyplot.legend(loc='upper right') pyplot.title('Cumulative distributions') pyplot.show()
def create_plot_posterior(params,plabs,cbars='red',nb=50,num=[]): if ( len(num) < 2 ): n = range(0,len(params)) else: n = num plt.figure(1,figsize=(12,4*(len(n))/2)) gs = gridspec.GridSpec(nrows=(len(n)+1)/2,ncols=2) j = 0 for i in n: plt.subplot(gs[j]) vpar, lpar, rpar = find_vals_perc(params[i],1.0) moda = my_mode(params[i]) #best_val = params[i][minchi2_index] #plt.axvline(x=best_val,c='yellow') plt.axvline(x=vpar,c=cbars) plt.axvline(x=moda,c='y',ls='-.') plt.axvline(x=vpar-lpar,c=cbars,ls='--') plt.axvline(x=vpar+rpar,c=cbars,ls='--') plt.xlabel(plabs[i]) plt.tick_params( axis='y',which='both',direction='in') plt.tick_params( axis='x',which='both',direction='in') if ( is_seaborn_plot ): sns.kdeplot(params[i], shade=True) else: plt.hist(params[i],normed=True,bins=nb) j = j + 1 fname = outdir+'/'+star+'_posterior.pdf' print 'Creating ', fname plt.savefig(fname,format='pdf',bbox_inches='tight') plt.close()
def improvement_plot(consensus_data, ordered_genomes, improvement_tgt): def do_kdeplot(x, y, ax, n_levels=None, bw='scott'): try: sns.kdeplot(x, y, ax=ax, cut=0, cmap='Purples_d', shade=True, shade_lowest=False, n_levels=n_levels, bw=bw, rasterized=True) except: logger.warning('Unable to do a KDE fit to AUGUSTUS improvement.') pass with improvement_tgt.open('w') as outf, PdfPages(outf) as pdf, sns.axes_style("whitegrid"): for genome in ordered_genomes: data = pd.DataFrame(consensus_data[genome]['Evaluation Improvement']['changes']) unchanged = consensus_data[genome]['Evaluation Improvement']['unchanged'] if len(data) == 0: continue data.columns = ['transMap original introns', 'transMap intron annotation support', 'transMap intron RNA support', 'Original introns', 'Intron annotation support', 'Intron RNA support', 'transMap alignment goodness', 'Alignment goodness'] fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2) for ax in [ax1, ax2, ax3]: # goodness plots are allowed to auto-set scale ax.set_xlim(0, 100) ax.set_ylim(0, 100) goodness_min = min(data['Alignment goodness']) ax4.set_xlim(goodness_min, 100) ax4.set_ylim(goodness_min, 100) do_kdeplot(data['transMap original introns'], data['Original introns'], ax1, n_levels=25, bw=2) sns.regplot(x=data['transMap original introns'], y=data['Original introns'], ax=ax1, color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False) do_kdeplot(data['transMap intron annotation support'], data['Intron annotation support'], ax2, n_levels=25, bw=2) sns.regplot(x=data['transMap intron annotation support'], y=data['Intron annotation support'], ax=ax2, color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False) do_kdeplot(data['transMap intron RNA support'], data['Intron RNA support'], ax3, n_levels=25, bw=2) sns.regplot(x=data['transMap intron RNA support'], y=data['Intron RNA support'], ax=ax3, color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False) do_kdeplot(data['transMap alignment goodness'], data['Alignment goodness'], ax4, n_levels=20, bw=1) sns.regplot(x=data['transMap alignment goodness'], y=data['Alignment goodness'], ax=ax4, color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False) fig.suptitle('AUGUSTUS metric improvements for {:,} transcripts in {}.\n' '{:,} transMap transcripts were chosen.'.format(len(data), genome, unchanged)) for ax in [ax1, ax2, ax3, ax4]: ax.set(adjustable='box-forced', aspect='equal') fig.subplots_adjust(hspace=0.3) multipage_close(pdf, tight_layout=False)
def main(): #?????????????????, ????????? stock_list = {"zsyh":"600036","jsyh":"601939","szzs":"000001","pfyh":"600000","msyh":"600061"} for stock, code in stock_list.items(): globals()[stock] = tsh.get_hist_data(code,start="2015-01-01",end="2016-04-16") #code:?????start:?????end:???? #print(zsyh) #??????????? make_end_line() print(zsyh.head()) make_end_line() print(zsyh.columns) make_end_line() """ ???? date??? open???? high???? close???? low???? volume???? price_change????? p_change???? ma5?5??? ma10?10??? ma20: 20??? v_ma5: 5??? v_ma10: 10??? v_ma20: 20??? turnover:???[???????] """ print(zsyh.describe()) make_end_line() print(zsyh.info()) make_end_line() plt.show(zsyh["close"].plot(figsize=(12,8))) #??????????? #pd.set_option("display.float_format", lambda x: "%10.3f" % x) plt.show(zsyh["volume"].plot(figsize=(12,8))) zsyh[["close","ma5","ma10","ma20"]].plot(subplots = True) plt.show() plt.show(zsyh[["close","ma5","ma10","ma20"]].plot(figsize=(12,8),linewidth=2)) plt.show(zsyh["p_change"].plot()) plt.show(zsyh["p_change"].plot(figsize=(10,4),legend=True,linestyle="--",marker="o")) #??????????? plt.show(zsyh["p_change"].hist(bins=20)) plt.show(zsyh["p_change"].plot.kde()) #????? #?????(kernel density estimation)????????????????? plt.show(sns.kdeplot(zsyh["p_change"].dropna())) plt.show(sns.distplot(zsyh["p_change"].dropna())) #??????????????????????
