使用matplotlib更新散点图中的标记样式

Question

我正在开发一个交互式绘图应用程序,它要求用户从matplotlib散点图中选择数据点.为清楚起见,我希望能够在点击(或通过任何方式选择)时改变绘制点的颜色和形状.

由于matplotlib.collections.PathCollection班级有set_facecolors方法,改变点的颜色相对简单.但是,我看不到更新标记形状的类似方法.

有没有办法做到这一点？

问题的准系统说明:

import numpy as np
import matplotlib.pyplot as plt

x = np.random.normal(0,1.0,100)
y = np.random.normal(0,1.0,100)

scatter_plot = plt.scatter(x, y, facecolor="b", marker="o")

#update the colour 
new_facecolors = ["r","g"]*50
scatter_plot.set_facecolors(new_facecolors)

#update the marker? 
#new_marker = ["o","s"]*50
#scatter_plot.???(new_marker)  #<--how do I access the marker shapes?  

plt.show()

有任何想法吗？

Answer 1

如果您真正关注的是突出显示用户选择的点,则可以dot = ax.scatter(...)在所选点的顶部添加另一个点(带).稍后,为响应用户点击,您可以使用dot.set_offsets((x, y))更改点的位置.

Joe Kington写了一个很好的例子(DataCursor),说明当用户点击艺术家时(如散点图),添加一个显示数据坐标的注释.

这是一个衍生示例(FollowDotCursor),当用户将鼠标悬停在某个点上时,它会突出显示并注释数据点.

随着DataCursor数据坐标显示的是在用户点击-这可能不是完全相同的坐标的基础数据.

与FollowDotCursor数据坐标显示总是在其最靠近鼠标底层数据的点.

import numpy as np
import matplotlib.pyplot as plt
import scipy.spatial as spatial

def fmt(x, y):
    return 'x: {x:0.2f}\ny: {y:0.2f}'.format(x=x, y=y)

class FollowDotCursor(object):
    """Display the x,y location of the nearest data point.
    """
    def __init__(self, ax, x, y, tolerance=5, formatter=fmt, offsets=(-20, 20)):
        try:
            x = np.asarray(x, dtype='float')
        except (TypeError, ValueError):
            x = np.asarray(mdates.date2num(x), dtype='float')
        y = np.asarray(y, dtype='float')
        self._points = np.column_stack((x, y))
        self.offsets = offsets
        self.scale = x.ptp()
        self.scale = y.ptp() / self.scale if self.scale else 1
        self.tree = spatial.cKDTree(self.scaled(self._points))
        self.formatter = formatter
        self.tolerance = tolerance
        self.ax = ax
        self.fig = ax.figure
        self.ax.xaxis.set_label_position('top')
        self.dot = ax.scatter(
            [x.min()], [y.min()], s=130, color='green', alpha=0.7)
        self.annotation = self.setup_annotation()
        plt.connect('motion_notify_event', self)

    def scaled(self, points):
        points = np.asarray(points)
        return points * (self.scale, 1)

    def __call__(self, event):
        ax = self.ax
        # event.inaxes is always the current axis. If you use twinx, ax could be
        # a different axis.
        if event.inaxes == ax:
            x, y = event.xdata, event.ydata
        elif event.inaxes is None:
            return
        else:
            inv = ax.transData.inverted()
            x, y = inv.transform([(event.x, event.y)]).ravel()
        annotation = self.annotation
        x, y = self.snap(x, y)
        annotation.xy = x, y
        annotation.set_text(self.formatter(x, y))
        self.dot.set_offsets((x, y))
        bbox = ax.viewLim
        event.canvas.draw()

    def setup_annotation(self):
        """Draw and hide the annotation box."""
        annotation = self.ax.annotate(
            '', xy=(0, 0), ha = 'right',
            xytext = self.offsets, textcoords = 'offset points', va = 'bottom',
            bbox = dict(
                boxstyle='round,pad=0.5', fc='yellow', alpha=0.75),
            arrowprops = dict(
                arrowstyle='->', connectionstyle='arc3,rad=0'))
        return annotation

    def snap(self, x, y):
        """Return the value in self.tree closest to x, y."""
        dist, idx = self.tree.query(self.scaled((x, y)), k=1, p=1)
        try:
            return self._points[idx]
        except IndexError:
            # IndexError: index out of bounds
            return self._points[0]

x = np.random.normal(0,1.0,100)
y = np.random.normal(0,1.0,100)
fig, ax = plt.subplots()

cursor = FollowDotCursor(ax, x, y, formatter=fmt, tolerance=20)
scatter_plot = plt.scatter(x, y, facecolor="b", marker="o")

#update the colour 
new_facecolors = ["r","g"]*50
scatter_plot.set_facecolors(new_facecolors)    

plt.show()