Python matplotlib.pyplot.setp() Examples
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code examples of matplotlib.pyplot.setp().
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Example #1
Source File: plot.py From TaskBot with GNU General Public License v3.0 | 6 votes |
def plot_attention(sentences, attentions, labels, **kwargs): fig, ax = plt.subplots(**kwargs) im = ax.imshow(attentions, interpolation='nearest', vmin=attentions.min(), vmax=attentions.max()) plt.colorbar(im, shrink=0.5, ticks=[0, 1]) plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") ax.set_yticks(range(len(labels))) ax.set_yticklabels(labels, fontproperties=getChineseFont()) # Loop over data dimensions and create text annotations. for i in range(attentions.shape[0]): for j in range(attentions.shape[1]): text = ax.text(j, i, sentences[i][j], ha="center", va="center", color="b", size=10, fontproperties=getChineseFont()) ax.set_title("Attention Visual") fig.tight_layout() plt.show()
Example #2
Source File: DyStockDataViewer.py From DevilYuan with MIT License | 6 votes |
def plotTimeShareChart(self, code, date, n): date = self._daysEngine.codeTDayOffset(code, date, n) if date is None: return DyMatplotlib.newFig() # plot stock time share chart self._plotTimeShareChart(code, date, left=0.05, right=0.95, top=0.95, bottom=0.05) # plot index time share chart #self._plotTimeShareChart(self._daysEngine.getIndex(code), date, left=0.05, right=0.95, top=0.45, bottom=0.05) # layout f = plt.gcf() plt.setp([a.get_xticklabels() for a in f.axes[::2]], visible=False) f.show()
Example #3
Source File: visualize_attention.py From atis with MIT License | 6 votes |
def render(self, filename): """ Renders the attention graph over timesteps. Args: filename (string): filename to save the figure to. """ figure, axes = plt.subplots() graph = np.stack(self.attentions) axes.imshow(graph, cmap=plt.cm.Blues, interpolation="nearest") axes.xaxis.tick_top() axes.set_xticks(range(len(self.keys))) axes.set_xticklabels(self.keys) plt.setp(axes.get_xticklabels(), rotation=90) axes.set_yticks(range(len(self.generated_values))) axes.set_yticklabels(self.generated_values) axes.set_aspect(1, adjustable='box') plt.tick_params(axis='x', which='both', bottom='off', top='off') plt.tick_params(axis='y', which='both', left='off', right='off') figure.savefig(filename)
Example #4
Source File: test_artist.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_setp(): # Check empty list plt.setp([]) plt.setp([[]]) # Check arbitrary iterables fig, axes = plt.subplots() lines1 = axes.plot(range(3)) lines2 = axes.plot(range(3)) martist.setp(chain(lines1, lines2), 'lw', 5) plt.setp(axes.spines.values(), color='green') # Check `file` argument sio = io.StringIO() plt.setp(lines1, 'zorder', file=sio) assert sio.getvalue() == ' zorder: float\n'
Example #5
Source File: plotting.py From Computable with MIT License | 6 votes |
def _label_axis(ax, kind='x', label='', position='top', ticks=True, rotate=False): from matplotlib.artist import setp if kind == 'x': ax.set_xlabel(label, visible=True) ax.xaxis.set_visible(True) ax.xaxis.set_ticks_position(position) ax.xaxis.set_label_position(position) if rotate: setp(ax.get_xticklabels(), rotation=90) elif kind == 'y': ax.yaxis.set_visible(True) ax.set_ylabel(label, visible=True) # ax.set_ylabel(a) ax.yaxis.set_ticks_position(position) ax.yaxis.set_label_position(position) return
Example #6
Source File: visFunction.py From uiKLine with MIT License | 6 votes |
def plotSigHeats(signals,markets,start=0,step=2,size=1,iters=6): """ 打印信号回测盈损热度图,寻找参数稳定岛 """ sigMat = pd.DataFrame(index=range(iters),columns=range(iters)) for i in range(iters): for j in range(iters): climit = start + i*step wlimit = start + j*step caps,poss = plotSigCaps(signals,markets,climit=climit,wlimit=wlimit,size=size,op=False) sigMat[i][j] = caps[-1] sns.heatmap(sigMat.values.astype(np.float64),annot=True,fmt='.2f',annot_kws={"weight": "bold"}) xTicks = [i+0.5 for i in range(iters)] yTicks = [iters-i-0.5 for i in range(iters)] xyLabels = [str(start+i*step) for i in range(iters)] _, labels = plt.yticks(yTicks,xyLabels) plt.setp(labels, rotation=0) _, labels = plt.xticks(xTicks,xyLabels) plt.setp(labels, rotation=90) plt.xlabel('Loss Stop @') plt.ylabel('Profit Stop @') return sigMat
Example #7
Source File: AE_ts_model.py From AE_ts with MIT License | 6 votes |
def plot_data(X_train, y_train, plot_row=5): counts = dict(Counter(y_train)) num_classes = len(np.unique(y_train)) f, axarr = plt.subplots(plot_row, num_classes) for c in np.unique(y_train): # Loops over classes, plot as columns c = int(c) ind = np.where(y_train == c) ind_plot = np.random.choice(ind[0], size=plot_row) for n in range(plot_row): # Loops over rows axarr[n, c].plot(X_train[ind_plot[n], :]) # Only shops axes for bottom row and left column if n == 0: axarr[n, c].set_title('Class %.0f (%.0f)' % (c, counts[float(c)])) if not n == plot_row - 1: plt.setp([axarr[n, c].get_xticklabels()], visible=False) if not c == 0: plt.setp([axarr[n, c].get_yticklabels()], visible=False) f.subplots_adjust(hspace=0) # No horizontal space between subplots f.subplots_adjust(wspace=0) # No vertical space between subplots plt.show() return
Example #8
Source File: DyStockDataViewer.py From DevilYuan with MIT License | 6 votes |
def _plotAckRWExtremas(self, event): code = event.data['code'] df = event.data['df'] regionalLocals = event.data['regionalLocals'] DyMatplotlib.newFig() f = plt.gcf() index = df.index startDay = index[0].strftime('%Y-%m-%d') endDay = index[-1].strftime('%Y-%m-%d') # plot stock periods = self._plotCandleStick(code, startDate=startDay, endDate=endDay, baseDate=endDay, left=0.05, right=0.95, top=0.95, bottom=0.5, maIndicator='close') self._plotRegionalLocals(f.axes[0], index, regionalLocals) plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False) f.show()
Example #9
Source File: DyStockDataViewer.py From DevilYuan with MIT License | 6 votes |
def _plotAckHSARs(self, event): code = event.data['code'] df = event.data['df'] hsars = event.data['hsars'] DyMatplotlib.newFig() f = plt.gcf() index = df.index startDay = index[0].strftime('%Y-%m-%d') endDay = index[-1].strftime('%Y-%m-%d') # plot stock periods = self._plotCandleStick(code, startDate=startDay, endDate=endDay, baseDate=endDay, left=0.05, right=0.95, top=0.95, bottom=0.5, maIndicator='close') self._plotHSARs(f.axes[0], hsars) plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False) f.show()
Example #10
Source File: chord_plot.py From jMetalPy with MIT License | 6 votes |
def hover_over_bin(event, handle_tickers, handle_plots, colors, fig): is_found = False for iobj in range(len(handle_tickers)): for ibin in range(len(handle_tickers[iobj])): cont = False if not is_found: cont, ind = handle_tickers[iobj][ibin].contains(event) if cont: is_found = True if cont: plt.setp(handle_tickers[iobj][ibin], facecolor=colors[iobj]) [h.set_visible(True) for h in handle_plots[iobj][ibin]] is_found = True fig.canvas.draw_idle() else: plt.setp(handle_tickers[iobj][ibin], facecolor=(1, 1, 1)) for h in handle_plots[iobj][ibin]: h.set_visible(False) fig.canvas.draw_idle()
Example #11
Source File: DyStockDataViewer.py From DevilYuan with MIT License | 6 votes |
def plotAckKama(self, event): code, startDate, endDate = '002551.SZ', '2015-07-01', '2016-03-01' # load if not self._daysEngine.load([-200, startDate, endDate], codes=[code]): return DyMatplotlib.newFig() # plot basic stock K-Chart periods = self._plotCandleStick(code, startDate=startDate, endDate=endDate, netCapitalFlow=True, left=0.05, right=0.95, top=0.95, bottom=0.5) # plot customized stock K-Chart self._plotKamaCandleStick(code, periods=periods, left=0.05, right=0.95, top=0.45, bottom=0.05) # layout f = plt.gcf() plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False) f.show()
Example #12
Source File: SpectraKeras_MLP.py From SpectralMachine with GNU General Public License v3.0 | 6 votes |
def plotWeights(En, A, model): import matplotlib.pyplot as plt plt.figure(tight_layout=True) plotInd = 511 for layer in model.layers: try: w_layer = layer.get_weights()[0] ax = plt.subplot(plotInd) newX = np.arange(En[0], En[-1], (En[-1]-En[0])/w_layer.shape[0]) plt.plot(En, np.interp(En, newX, w_layer[:,0]), label=layer.get_config()['name']) plt.legend(loc='upper right') plt.setp(ax.get_xticklabels(), visible=False) plotInd +=1 except: pass ax1 = plt.subplot(plotInd) ax1.plot(En, A[0], label='Sample data') plt.xlabel('Raman shift [1/cm]') plt.legend(loc='upper right') plt.savefig('keras_MLP_weights' + '.png', dpi = 160, format = 'png') # Save plot #************************************
Example #13
Source File: viz.py From dgl with Apache License 2.0 | 6 votes |
def draw_heatmap(array, input_seq, output_seq, dirname, name): dirname = os.path.join('log', dirname) if not os.path.exists(dirname): os.makedirs(dirname) fig, axes = plt.subplots(2, 4) cnt = 0 for i in range(2): for j in range(4): axes[i, j].imshow(array[cnt].transpose(-1, -2)) axes[i, j].set_yticks(np.arange(len(input_seq))) axes[i, j].set_xticks(np.arange(len(output_seq))) axes[i, j].set_yticklabels(input_seq, fontsize=4) axes[i, j].set_xticklabels(output_seq, fontsize=4) axes[i, j].set_title('head_{}'.format(cnt), fontsize=10) plt.setp(axes[i, j].get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") cnt += 1 fig.suptitle(name, fontsize=12) plt.tight_layout() plt.savefig(os.path.join(dirname, '{}.pdf'.format(name))) plt.close()
Example #14
Source File: SpectraKeras_CNN.py From SpectralMachine with GNU General Public License v3.0 | 6 votes |
def plotWeights(En, A, model): import matplotlib.pyplot as plt plt.figure(tight_layout=True) plotInd = 511 for layer in model.layers: try: w_layer = layer.get_weights()[0] ax = plt.subplot(plotInd) newX = np.arange(En[0], En[-1], (En[-1]-En[0])/w_layer.shape[0]) plt.plot(En, np.interp(En, newX, w_layer[:,0]), label=layer.get_config()['name']) plt.legend(loc='upper right') plt.setp(ax.get_xticklabels(), visible=False) plotInd +=1 except: pass ax1 = plt.subplot(plotInd) ax1.plot(En, A[0], label='Sample data') plt.xlabel('Raman shift [1/cm]') plt.legend(loc='upper right') plt.savefig('keras_MLP_weights' + '.png', dpi = 160, format = 'png') # Save plot #************************************
Example #15
Source File: PiecewiseConstant.py From python_primer with MIT License | 6 votes |
def _test(): PC = PiecewiseConstant([(0.4, 1), (0.2, 1.5), (0.1, 3)], xmax=4) I, I_s = Indicator(-3, 5), Indicator(-3, 5, eps=1) H, H_s = Heaviside(), Heaviside(eps=1) ax1 = plt.subplot(311) ax2, ax3 = plt.subplot(323), plt.subplot(324) ax4, ax5 = plt.subplot(325), plt.subplot(326) x, y = PC.plot() ax1.plot(x, y) ax1.set_ylim([0, 0.5]) ax1.set_title('PiecewiseConstant') titles = ['Indicator', 'Indicator (eps=1)', 'Heaviside', 'Heaviside (eps=1)'] for f, ax, title in zip([I, I_s, H, H_s], [ax2, ax3, ax4, ax5], titles): x, y = f.plot(-6, 8) ax.plot(x, y) ax.set_ylim([-0.5, 1.5]) ax.set_title(title) for ax in [ax2, ax3]: plt.setp(ax.get_xticklabels(), visible=False) plt.show()
Example #16
Source File: visFunction.py From uiKLine with MIT License | 6 votes |
def plotSigHeats(signals,markets,start=0,step=2,size=1,iters=6): """ 打印信号回测盈损热度图,寻找参数稳定岛 """ sigMat = pd.DataFrame(index=range(iters),columns=range(iters)) for i in range(iters): for j in range(iters): climit = start + i*step wlimit = start + j*step caps,poss = plotSigCaps(signals,markets,climit=climit,wlimit=wlimit,size=size,op=False) sigMat[i][j] = caps[-1] sns.heatmap(sigMat.values.astype(np.float64),annot=True,fmt='.2f',annot_kws={"weight": "bold"}) xTicks = [i+0.5 for i in range(iters)] yTicks = [iters-i-0.5 for i in range(iters)] xyLabels = [str(start+i*step) for i in range(iters)] _, labels = plt.yticks(yTicks,xyLabels) plt.setp(labels, rotation=0) _, labels = plt.xticks(xTicks,xyLabels) plt.setp(labels, rotation=90) plt.xlabel('Loss Stop @') plt.ylabel('Profit Stop @') return sigMat
Example #17
Source File: utils.py From seq2seq-summarizer with MIT License | 6 votes |
def show_attention_map(src_words, pred_words, attention, pointer_ratio=None): fig, ax = plt.subplots(figsize=(16, 4)) im = plt.pcolormesh(np.flipud(attention), cmap="GnBu") # set ticks and labels ax.set_xticks(np.arange(len(src_words)) + 0.5) ax.set_xticklabels(src_words, fontsize=14) ax.set_yticks(np.arange(len(pred_words)) + 0.5) ax.set_yticklabels(reversed(pred_words), fontsize=14) if pointer_ratio is not None: ax1 = ax.twinx() ax1.set_yticks(np.concatenate([np.arange(0.5, len(pred_words)), [len(pred_words)]])) ax1.set_yticklabels('%.3f' % v for v in np.flipud(pointer_ratio)) ax1.set_ylabel('Copy probability', rotation=-90, va="bottom") # let the horizontal axes labelling appear on top ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False) # rotate the tick labels and set their alignment plt.setp(ax.get_xticklabels(), rotation=-45, ha="right", rotation_mode="anchor")
Example #18
Source File: burst_plot.py From FRETBursts with GNU General Public License v2.0 | 6 votes |
def _alex_plot_style(g, colorbar=True): """Set plot style and colorbar for an ALEX joint plot. """ g.set_axis_labels(xlabel="E", ylabel="S") g.ax_marg_x.grid(True) g.ax_marg_y.grid(True) g.ax_marg_x.set_xlabel('') g.ax_marg_y.set_ylabel('') plt.setp(g.ax_marg_y.get_xticklabels(), visible=True) plt.setp(g.ax_marg_x.get_yticklabels(), visible=True) g.ax_marg_x.locator_params(axis='y', tight=True, nbins=3) g.ax_marg_y.locator_params(axis='x', tight=True, nbins=3) if colorbar: pos = g.ax_joint.get_position().get_points() X, Y = pos[:, 0], pos[:, 1] cax = plt.axes([1., Y[0], (X[1] - X[0]) * 0.045, Y[1] - Y[0]]) plt.colorbar(cax=cax)
Example #19
Source File: matplotlib_trading_chart.py From tensortrade with Apache License 2.0 | 6 votes |
def render(self, current_step, net_worths, benchmarks, trades, window_size=50): net_worth = round(net_worths[-1], 2) initial_net_worth = round(net_worths[0], 2) profit_percent = round((net_worth - initial_net_worth) / initial_net_worth * 100, 2) self.fig.suptitle('Net worth: $' + str(net_worth) + ' | Profit: ' + str(profit_percent) + '%') window_start = max(current_step - window_size, 0) step_range = slice(window_start, current_step) times = self.df.index.values[step_range] self._render_net_worth(step_range, times, current_step, net_worths, benchmarks) self._render_price(step_range, times, current_step) self._render_volume(step_range, times) self._render_trades(step_range, trades) self.price_ax.set_xticklabels(times, rotation=45, horizontalalignment='right') # Hide duplicate net worth date labels plt.setp(self.net_worth_ax.get_xticklabels(), visible=False) # Necessary to view frames before they are unrendered plt.pause(0.001)
Example #20
Source File: plot_helpers.py From topmodel with MIT License | 5 votes |
def plot_boxplot(vals, label): fig, ax = plt.subplots() ax.boxplot(vals) plt.setp(ax, xticklabels=label) return save_image()
Example #21
Source File: plotter.py From message-analyser with MIT License | 5 votes |
def pie_messages_per_author(msgs, your_name, target_name, path_to_save): forwarded = len([msg for msg in msgs if msg.is_forwarded]) msgs = list(filter(lambda msg: not msg.is_forwarded, msgs)) your_messages_len = len([msg for msg in msgs if msg.author == your_name]) target_messages_len = len(msgs) - your_messages_len data = [your_messages_len, target_messages_len, forwarded] labels = [f"{your_name}\n({your_messages_len})", f"{target_name}\n({target_messages_len})", f"forwarded\n({forwarded})"] explode = (.0, .0, .2) fig, ax = plt.subplots(figsize=(13, 8), subplot_kw=dict(aspect="equal")) wedges, _, autotexts = ax.pie(x=data, explode=explode, colors=["#4982BB", "#5C6093", "#53B8D7"], autopct=lambda pct: f"{pct:.1f}%", wedgeprops={"edgecolor": "black", "alpha": 0.8}) ax.legend(wedges, labels, loc="upper right", bbox_to_anchor=(1, 0, 0.5, 1)) plt.setp(autotexts, size=10, weight="bold") fig.savefig(os.path.join(path_to_save, pie_messages_per_author.__name__ + ".png"), dpi=500) # plt.show() plt.close("all") log_line(f"{pie_messages_per_author.__name__} was created.")
Example #22
Source File: PlotROC.py From 3D-convolutional-speaker-recognition with Apache License 2.0 | 5 votes |
def Plot_ROC_Fn(label,distance,save_path): fpr, tpr, thresholds = metrics.roc_curve(label, distance, pos_label=1) AUC = metrics.roc_auc_score(label, distance, average='macro', sample_weight=None) # AP = metrics.average_precision_score(label, -distance, average='macro', sample_weight=None) # Calculating EER intersect_x = fpr[np.abs(fpr - (1 - tpr)).argmin(0)] EER = intersect_x print("EER = ", float(("{0:.%ie}" % 1).format(intersect_x))) # AUC(area under the curve) calculation print("AUC = ", float(("{0:.%ie}" % 1).format(AUC))) # # AP(average precision) calculation. # # This score corresponds to the area under the precision-recall curve. # print("AP = ", float(("{0:.%ie}" % 1).format(AP))) # Plot the ROC fig = plt.figure() ax = fig.gca() lines = plt.plot(fpr, tpr, label='ROC Curve') plt.setp(lines, linewidth=2, color='r') ax.set_xticks(np.arange(0, 1.1, 0.1)) ax.set_yticks(np.arange(0, 1.1, 0.1)) plt.title('ROC.jpg') plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') # # Cutting the floating number # AUC = '%.2f' % AUC # EER = '%.2f' % EER # # AP = '%.2f' % AP # # # Setting text to plot # # plt.text(0.5, 0.6, 'AP = ' + str(AP), fontdict=None) # plt.text(0.5, 0.5, 'AUC = ' + str(AUC), fontdict=None) # plt.text(0.5, 0.4, 'EER = ' + str(EER), fontdict=None) plt.grid() plt.show() fig.savefig(save_path)
Example #23
Source File: regression03.py From AILearners with Apache License 2.0 | 5 votes |
def plotstageWiseMat(): font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=14) xArr, yArr = loadDataSet('C:/Users/Administrator/Desktop/blog/github/AILearners/data/ml/jqxxsz/8.Regression/abalone.txt') returnMat = stageWise(xArr, yArr, 0.005, 1000) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(returnMat) ax_title_text = ax.set_title(u'前向逐步回归:迭代次数与回归系数的关系', FontProperties = font) ax_xlabel_text = ax.set_xlabel(u'迭代次数', FontProperties = font) ax_ylabel_text = ax.set_ylabel(u'回归系数', FontProperties = font) plt.setp(ax_title_text, size = 15, weight = 'bold', color = 'red') plt.setp(ax_xlabel_text, size = 10, weight = 'bold', color = 'black') plt.setp(ax_ylabel_text, size = 10, weight = 'bold', color = 'black') plt.show()
Example #24
Source File: demo_axes_divider.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
def demo_locatable_axes_hard(fig1): from mpl_toolkits.axes_grid1 import SubplotDivider, Size from mpl_toolkits.axes_grid1.mpl_axes import Axes divider = SubplotDivider(fig1, 2, 2, 2, aspect=True) # axes for image ax = Axes(fig1, divider.get_position()) # axes for colorbar ax_cb = Axes(fig1, divider.get_position()) h = [Size.AxesX(ax), # main axes Size.Fixed(0.05), # padding, 0.1 inch Size.Fixed(0.2), # colorbar, 0.3 inch ] v = [Size.AxesY(ax)] divider.set_horizontal(h) divider.set_vertical(v) ax.set_axes_locator(divider.new_locator(nx=0, ny=0)) ax_cb.set_axes_locator(divider.new_locator(nx=2, ny=0)) fig1.add_axes(ax) fig1.add_axes(ax_cb) ax_cb.axis["left"].toggle(all=False) ax_cb.axis["right"].toggle(ticks=True) Z, extent = get_demo_image() im = ax.imshow(Z, extent=extent, interpolation="nearest") plt.colorbar(im, cax=ax_cb) plt.setp(ax_cb.get_yticklabels(), visible=False)
Example #25
Source File: analyse_results_paper.py From YAFS with MIT License | 5 votes |
def set_box_color(bp, color): plt.setp(bp['boxes'], color=color) plt.setp(bp['whiskers'], color=color) plt.setp(bp['caps'], color=color) plt.setp(bp['medians'], color=color) # ============================================================================= # Boxplot matriz of each app - gtw/user # =============================================================================
Example #26
Source File: regression.py From AILearners with Apache License 2.0 | 5 votes |
def plotlwlrRegression(): font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=14) xArr, yArr = loadDataSet('C:/Users/Administrator/Desktop/blog/github/AILearners/data/ml/jqxxsz/8.Regression/ex0.txt') #加载数据集 yHat_1 = lwlrTest(xArr, xArr, yArr, 1.0) #根据局部加权线性回归计算yHat yHat_2 = lwlrTest(xArr, xArr, yArr, 0.01) #根据局部加权线性回归计算yHat yHat_3 = lwlrTest(xArr, xArr, yArr, 0.003) #根据局部加权线性回归计算yHat xMat = np.mat(xArr) #创建xMat矩阵 yMat = np.mat(yArr) #创建yMat矩阵 srtInd = xMat[:, 1].argsort(0) #排序,返回索引值 xSort = xMat[srtInd][:,0,:] fig, axs = plt.subplots(nrows=3, ncols=1,sharex=False, sharey=False, figsize=(10,8)) axs[0].plot(xSort[:, 1], yHat_1[srtInd], c = 'red') #绘制回归曲线 axs[1].plot(xSort[:, 1], yHat_2[srtInd], c = 'red') #绘制回归曲线 axs[2].plot(xSort[:, 1], yHat_3[srtInd], c = 'red') #绘制回归曲线 axs[0].scatter(xMat[:,1].flatten().A[0], yMat.flatten().A[0], s = 20, c = 'blue', alpha = .5) #绘制样本点 axs[1].scatter(xMat[:,1].flatten().A[0], yMat.flatten().A[0], s = 20, c = 'blue', alpha = .5) #绘制样本点 axs[2].scatter(xMat[:,1].flatten().A[0], yMat.flatten().A[0], s = 20, c = 'blue', alpha = .5) #绘制样本点 #设置标题,x轴label,y轴label axs0_title_text = axs[0].set_title(u'局部加权回归曲线,k=1.0',FontProperties=font) axs1_title_text = axs[1].set_title(u'局部加权回归曲线,k=0.01',FontProperties=font) axs2_title_text = axs[2].set_title(u'局部加权回归曲线,k=0.003',FontProperties=font) plt.setp(axs0_title_text, size=8, weight='bold', color='red') plt.setp(axs1_title_text, size=8, weight='bold', color='red') plt.setp(axs2_title_text, size=8, weight='bold', color='red') plt.xlabel('X') plt.show()
Example #27
Source File: demo_axes_divider.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
def demo_simple_image(ax): Z, extent = get_demo_image() im = ax.imshow(Z, extent=extent, interpolation="nearest") cb = plt.colorbar(im) plt.setp(cb.ax.get_yticklabels(), visible=False)
Example #28
Source File: plot_confusion_matrix.py From quantum-neural-networks with Apache License 2.0 | 5 votes |
def plot_confusion_matrix(cm, classes, title=None, cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ fig, ax = plt.subplots() im = ax.imshow(cm, interpolation='nearest', cmap=cmap) # We want to show all ticks... ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]), # ... and label them with the respective list entries xticklabels=classes, yticklabels=classes, ylabel='True label', xlabel='Predicted label') # Rotate the tick labels and set their alignment. plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") # Loop over data dimensions and create text annotations. fmt = '.2f' thresh = cm.max() / 2. for i in range(cm.shape[0]): for j in range(cm.shape[1]): ax.text(j, i, format(cm[i, j], fmt), ha="center", va="center", color="white" if cm[i, j] > thresh else "black") fig.tight_layout() return ax
Example #29
Source File: confidence_analyzer.py From assistant-dialog-skill-analysis with Apache License 2.0 | 5 votes |
def create_threshold_graph(data): """ display threshold analysis graph :param data: :return: None """ sns.set(rc={"figure.figsize": (20.7, 10.27)}) plt.ylim(0, 1.1) plt.axvline(0.2, 0, 1) plot = sns.lineplot(data=data, palette="tab10", linewidth=3.5) plt.setp(plot.legend().get_texts(), fontsize="22") plot.set_xlabel("Threshold T", fontsize=18) plot.set_ylabel("Metrics mentioned above", fontsize=18)
Example #30
Source File: test_patheffects.py From neural-network-animation with MIT License | 5 votes |
def test_patheffect2(): ax2 = plt.subplot(111) arr = np.arange(25).reshape((5, 5)) ax2.imshow(arr) cntr = ax2.contour(arr, colors="k") plt.setp(cntr.collections, path_effects=[path_effects.withStroke(linewidth=3, foreground="w")]) clbls = ax2.clabel(cntr, fmt="%2.0f", use_clabeltext=True) plt.setp(clbls, path_effects=[path_effects.withStroke(linewidth=3, foreground="w")])