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‎galleries/examples/event_handling/hover_event_demo.py‎

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"""
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===============
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Pick event demo
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===============
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================
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Hover event demo
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================
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You can enable picking by setting the "picker" property of an artist
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(for example, a Matplotlib Line2D, Text, Patch, Polygon, AxesImage,
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etc.)
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There are a variety of meanings of the picker property:
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* *None* - picking is disabled for this artist (default)
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.. note::
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Data tooltips are currently only supported for the TkAgg backend.
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* bool - if *True* then picking will be enabled and the artist will fire a pick
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event if the mouse event is over the artist.
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You can enable hovering by setting the "hover" property of an artist.
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Hovering adds a tooltip to the bottom right corner
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of the figure canvas, which is displayed when the mouse pointer hovers over the
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artist.
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Setting ``pickradius`` will add an epsilon tolerance in points and the artist
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will fire off an event if its data is within epsilon of the mouse event. For
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some artists like lines and patch collections, the artist may provide
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additional data to the pick event that is generated, for example, the indices
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of the data within epsilon of the pick event
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The hover behavior depends on the type of the argument passed to the
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``set_hover`` method:
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* function - if picker is callable, it is a user supplied function which
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determines whether the artist is hit by the mouse event. ::
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* *None* - hovering is disabled for this artist (default)
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hit, props = picker(artist, mouseevent)
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* list of string literals - hovering is enabled, and hovering over a point
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displays the corresponding string literal.
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to determine the hit test. If the mouse event is over the artist, return
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hit=True and props is a dictionary of properties you want added to the
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PickEvent attributes.
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* function - if hover is callable, it is a user supplied function which
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takes a ``mouseevent`` object (see below), and returns a tuple of transformed
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coordinates
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After you have enabled an artist for picking by setting the "picker"
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property, you need to connect to the figure canvas pick_event to get
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pick callbacks on mouse press events. For example, ::
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After you have enabled an artist for picking by setting the "hover"
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property, you need to connect to the figure canvas hover_event to get
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hover callbacks on mouse over events. For example, ::
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def pick_handler(event):
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def hover_handler(event):
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mouseevent = event.mouseevent
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artist = event.artist
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# now do something with this...
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The pick event (matplotlib.backend_bases.PickEvent) which is passed to
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The hover event (matplotlib.backend_bases.HoverEvent) which is passed to
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your callback is always fired with two attributes:
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mouseevent
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the mouse event that generate the pick event.
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the mouse event that generate the hover event.
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The mouse event in turn has attributes like x and y (the coordinates in
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display space, such as pixels from left, bottom) and xdata, ydata (the
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for details.
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artist
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the matplotlib.artist that generated the pick event.
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the matplotlib.artist that generated the hover event.
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Additionally, certain artists like Line2D and PatchCollection may
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attach additional metadata like the indices into the data that meet
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the picker criteria (for example, all the points in the line that are within
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the specified epsilon tolerance)
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You can set the ``hover`` property of an artist by supplying a ``hover``
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argument to ``Axes.plot()``
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The examples below illustrate each of these methods.
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The examples below illustrate the different ways to use the ``hover`` property.
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.. note::
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These examples exercises the interactive capabilities of Matplotlib, and
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You can copy and paste individual parts, or download the entire example
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using the link at the bottom of the page.
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"""
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# %%
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# Hover with string literal labels
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# --------------------------------
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import matplotlib.pyplot as plt
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import numpy as np
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from numpy.random import rand
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from matplotlib.image import AxesImage
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from matplotlib.lines import Line2D
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from matplotlib.patches import Rectangle
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from matplotlib.text import Text
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# %%
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# Simple picking, lines, rectangles and text
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# ------------------------------------------
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fig, (ax1, ax2) = plt.subplots(2, 1)
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ax1.set_title('click on points, rectangles or text', picker=True)
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ax1.set_ylabel('ylabel', picker=True, bbox=dict(facecolor='red'))
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line, = ax1.plot(rand(100), 'o', picker=True, pickradius=5)
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# Pick the rectangle.
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ax2.bar(range(10), rand(10), picker=True)
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for label in ax2.get_xticklabels(): # Make the xtick labels pickable.
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label.set_picker(True)
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def onpick1(event):
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if isinstance(event.artist, Line2D):
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thisline = event.artist
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xdata = thisline.get_xdata()
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ydata = thisline.get_ydata()
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ind = event.ind
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print('onpick1 line:', np.column_stack([xdata[ind], ydata[ind]]))
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elif isinstance(event.artist, Rectangle):
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patch = event.artist
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print('onpick1 patch:', patch.get_path())
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elif isinstance(event.artist, Text):
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text = event.artist
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print('onpick1 text:', text.get_text())
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fig.canvas.mpl_connect('pick_event', onpick1)
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# %%
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# Picking with a custom hit test function
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# ---------------------------------------
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# You can define custom pickers by setting picker to a callable function. The
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# function has the signature::
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#
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# hit, props = func(artist, mouseevent)
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#
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# to determine the hit test. If the mouse event is over the artist, return
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# ``hit=True`` and ``props`` is a dictionary of properties you want added to
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# the `.PickEvent` attributes.
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def line_picker(line, mouseevent):
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"""
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Find the points within a certain distance from the mouseclick in
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data coords and attach some extra attributes, pickx and picky
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which are the data points that were picked.
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"""
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if mouseevent.xdata is None:
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return False, dict()
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xdata = line.get_xdata()
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ydata = line.get_ydata()
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maxd = 0.05
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d = np.sqrt(
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(xdata - mouseevent.xdata)**2 + (ydata - mouseevent.ydata)**2)
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ind, = np.nonzero(d <= maxd)
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if len(ind):
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pickx = xdata[ind]
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picky = ydata[ind]
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props = dict(ind=ind, pickx=pickx, picky=picky)
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return True, props
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else:
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return False, dict()
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def onpick2(event):
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print('onpick2 line:', event.pickx, event.picky)
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fig, ax = plt.subplots()
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ax.set_title('custom picker for line data')
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line, = ax.plot(rand(100), rand(100), 'o', picker=line_picker)
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fig.canvas.mpl_connect('pick_event', onpick2)
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# %%
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# Picking on a scatter plot
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# -------------------------
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# A scatter plot is backed by a `~matplotlib.collections.PathCollection`.
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x, y, c, s = rand(4, 100)
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def onpick3(event):
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ind = event.ind
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print('onpick3 scatter:', ind, x[ind], y[ind])
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fig, ax = plt.subplots()
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ax.scatter(x, y, 100*s, c, picker=True)
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fig.canvas.mpl_connect('pick_event', onpick3)
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plt.ylabel('some numbers')
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ax.plot(rand(3), 'o', hover=['London', 'Paris', 'Barcelona'])
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plt.show()
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# %%
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# Picking images
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# --------------
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# Images plotted using `.Axes.imshow` are `~matplotlib.image.AxesImage`
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# objects.
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# Hover with a callable transformation function
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# ---------------------------------------------
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fig, ax = plt.subplots()
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ax.imshow(rand(10, 5), extent=(1, 2, 1, 2), picker=True)
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ax.imshow(rand(5, 10), extent=(3, 4, 1, 2), picker=True)
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ax.imshow(rand(20, 25), extent=(1, 2, 3, 4), picker=True)
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ax.imshow(rand(30, 12), extent=(3, 4, 3, 4), picker=True)
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ax.set(xlim=(0, 5), ylim=(0, 5))
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def onpick4(event):
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artist = event.artist
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if isinstance(artist, AxesImage):
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im = artist
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A = im.get_array()
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print('onpick4 image', A.shape)
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plt.ylabel('some numbers')
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fig.canvas.mpl_connect('pick_event', onpick4)
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def user_defined_function(event):
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return round(event.xdata * 10, 1), round(event.ydata + 3, 3)
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ax.plot(rand(100), 'o', hover=user_defined_function)
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plt.show()

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