使用matplotlib从绘图中获取数据

Question

我在python中使用matplotlib来构建散点图。

假设我有以下两个数据列表。

X=[1,2,3,4,5]

Y=[6,7,8,9,10]

然后使用X作为X轴值，Y作为Y轴值绘制散点图。所以我会有一张上面有5个散射点的照片，对吧？

现在的问题是：是否有可能为这5个点与实际数据建立连接。例如，当我点击这5个点中的一个时，它可以告诉我我用了什么原始数据来说明这一点？

提前谢谢

Answer 1

使用稍加修改的Joe Kington's DataCursor版本：

import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib.cbook as cbook
import numpy as np

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

class DataCursor(object):
    # https://stackoverflow.com/a/4674445/190597
    """A simple data cursor widget that displays the x,y location of a
    matplotlib artist when it is selected."""
    def __init__(self, artists, x = [], y = [], tolerance = 5, offsets = (-20, 20),
                 formatter = fmt, display_all = False):
        """Create the data cursor and connect it to the relevant figure.
        "artists" is the matplotlib artist or sequence of artists that will be 
            selected. 
        "tolerance" is the radius (in points) that the mouse click must be
            within to select the artist.
        "offsets" is a tuple of (x,y) offsets in points from the selected
            point to the displayed annotation box
        "formatter" is a callback function which takes 2 numeric arguments and
            returns a string
        "display_all" controls whether more than one annotation box will
            be shown if there are multiple axes.  Only one will be shown
            per-axis, regardless. 
        """
        self._points = np.column_stack((x,y))
        self.formatter = formatter
        self.offsets = offsets
        self.display_all = display_all
        if not cbook.iterable(artists):
            artists = [artists]
        self.artists = artists
        self.axes = tuple(set(art.axes for art in self.artists))
        self.figures = tuple(set(ax.figure for ax in self.axes))

        self.annotations = {}
        for ax in self.axes:
            self.annotations[ax] = self.annotate(ax)

        for artist in self.artists:
            artist.set_picker(tolerance)
        for fig in self.figures:
            fig.canvas.mpl_connect('pick_event', self)

    def annotate(self, ax):
        """Draws and hides the annotation box for the given axis "ax"."""
        annotation = ax.annotate(self.formatter, xy = (0, 0), ha = 'right',
                xytext = self.offsets, textcoords = 'offset points', va = 'bottom',
                bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
                arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0')
                )
        annotation.set_visible(False)
        return annotation

    def snap(self, x, y):
        """Return the value in self._points closest to (x, y).
        """
        idx = np.nanargmin(((self._points - (x,y))**2).sum(axis = -1))
        return self._points[idx]
    def __call__(self, event):
        """Intended to be called through "mpl_connect"."""
        # Rather than trying to interpolate, just display the clicked coords
        # This will only be called if it's within "tolerance", anyway.
        x, y = event.mouseevent.xdata, event.mouseevent.ydata
        annotation = self.annotations[event.artist.axes]
        if x is not None:
            if not self.display_all:
                # Hide any other annotation boxes...
                for ann in self.annotations.values():
                    ann.set_visible(False)
            # Update the annotation in the current axis..
            x, y = self.snap(x, y)
            annotation.xy = x, y
            annotation.set_text(self.formatter(x, y))
            annotation.set_visible(True)
            event.canvas.draw()

x=[1,2,3,4,5]
y=[6,7,8,9,10]

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
scat = ax.scatter(x, y)
DataCursor(scat, x, y)
plt.show()

收益率

您可以单击任何点，气球将显示基础数据值。

---

我对DataCursor的细微修改是添加了snap方法，该方法确保显示的数据点来自原始数据集，而不是鼠标实际单击的位置。

---

如果您安装了scipy，则可能更喜欢此版本的光标，它使气球跟随鼠标（不单击）：

import datetime as DT
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import scipy.spatial as spatial

def fmt(x, y, is_date):
    if is_date:
        x = mdates.num2date(x).strftime("%Y-%m-%d")
        return 'x: {x}\ny: {y}'.format(x=x, y=y)
    else:
        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')
            self.is_date = False
        except (TypeError, ValueError):
            x = np.asarray(mdates.date2num(x), dtype='float')
            self.is_date = True
        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.is_date))
        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 = [DT.date.today()+DT.timedelta(days=i) for i in [10,20,30,40,50]]
y = [6,7,8,9,10]

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.scatter(x, y)
cursor = FollowDotCursor(ax, x, y)
fig.autofmt_xdate()
plt.show()

Answer 2

现在可以在几行中使用mpld3来执行此操作：

https://mpld3.github.io/examples/html_tooltips.html