Python pyplot.setp方法代码示例

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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

#************************************ 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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") 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import setp [as 别名]
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