(编辑:jimmy 日期: 2024/12/27 浏览:2)
mpldatacursor
包可以为matplotlib
提供交互式的数据光标(弹出式注释框)。
它的典型功能是:
鼠标左键单击
图表数据元素时会弹出文本框显示最近的数据元素的坐标值。鼠标右键单击
文本框取消显示数据光标。d
键时切换显示\关闭数据光标。如果matplotlib版本低于3.3可以直接使用pip安装
pip install mpldatacursor
如果matplotlib版本高于3.3,虽然pip安装成功,但是运行案例时会出现AttributeError: 'ScalarFormatter' object has no attribute 'pprint_val'
错误。
通过查看源码可知:
try: # Again, older versions of mpl return formatter.pprint_val(x) except AttributeError: # 3.3.0 or later return formatter.format_data_short(x)
通过分析,预计是因为使用了国内pip源,mpldatacursor
包还未修复该问题(pip 安装的 mpldatacursor
包版本号是0.7.1)。
因此,建议到https://github.com/joferkington/mpldatacursor
下载源码,进行源码安装(源码安装的 mpldatacursor
包版本号是0.7.dev0)。
python setup.py install
mpldatacursor
包基本应用方式比较简单:
mpldatacursor
包中导入datacursor
函数。datacursor
函数。查看源码可知,mpldatacursor
包的结构如下:
mpldatacursor convenience.py datacursor.py pick_info.py __init__.py
datacursor
函数定义在convenience.py
中,datacursor
函数的返回值是DataCursor
类实例。
DataCursor
类定义在datacursor.py
中。
pick_info.py
定义了一系列和弹出文本框相关的函数,供DataCursor
类调用。
由 datacursor
函数定义可知:
datacursor
函数可以不提供参数,这样图像内所有数据元素都会应用交互式数据光标。datacursor
函数可以指定哪些数据元素应用交互式数据光标。def datacursor(artists=None, axes=None, **kwargs): """ Create an interactive data cursor for the specified artists or specified axes. The data cursor displays information about a selected artist in a "popup" annotation box. If a specific sequence of artists is given, only the specified artists will be interactively selectable. Otherwise, all manually-plotted artists in *axes* will be used (*axes* defaults to all axes in all figures). Parameters ----------- artists : a matplotlib artist or sequence of artists, optional The artists to make selectable and display information for. If this is not specified, then all manually plotted artists in `axes` will be used. axes : a matplotlib axes of sequence of axes, optional The axes to selected artists from if a sequence of artists is not specified. If `axes` is not specified, then all available axes in all figures will be used. tolerance : number, optional The radius (in points) that the mouse click must be within to select the artist. Default: 5 points. formatter : callable, optional A function that accepts arbitrary kwargs and returns a string that will be displayed with annotate. Often, it is convienent to pass in the format method of a template string, e.g. ``formatter="{label}".format``. Keyword arguments passed in to the `formatter` function: `x`, `y` : floats The x and y data coordinates of the clicked point `event` : a matplotlib ``PickEvent`` The pick event that was fired (note that the selected artist can be accessed through ``event.artist``). `label` : string or None The legend label of the selected artist. `ind` : list of ints or None If the artist has "subitems" (e.g. points in a scatter or line plot), this will be a list of the item(s) that were clicked on. If the artist does not have "subitems", this will be None. Note that this is always a list, even when a single item is selected. Some selected artists may supply additional keyword arguments that are not always present, for example: `z` : number The "z" (usually color or array) value, if present. For an ``AxesImage`` (as created by ``imshow``), this will be the uninterpolated array value at the point clicked. For a ``PathCollection`` (as created by ``scatter``) this will be the "c" value if an array was passed to "c". `i`, `j` : ints The row, column indicies of the selected point for an ``AxesImage`` (as created by ``imshow``) `s` : number The size of the selected item in a ``PathCollection`` if a size array is specified. `c` : number The array value displayed as color for a ``PathCollection`` if a "c" array is specified (identical to "z"). `point_label` : list If `point_labels` is given when the data cursor is initialized and the artist has "subitems", this will be a list of the items of `point_labels` that correspond to the selected artists. Note that this is always a list, even when a single artist is selected. `width`, `height`, `top`, `bottom` : numbers The parameters for ``Rectangle`` artists (e.g. bar plots). point_labels : sequence or dict, optional For artists with "subitems" (e.g. Line2D's), the item(s) of `point_labels` corresponding to the selected "subitems" of the artist will be passed into the formatter function as the "point_label" kwarg. If a single sequence is given, it will be used for all artists with "subitems". Alternatively, a dict of artist:sequence pairs may be given to match an artist to the correct series of point labels. display : {"one-per-axes", "single", "multiple"}, optional Controls whether more than one annotation box will be shown. Default: "one-per-axes" draggable : boolean, optional Controls whether or not the annotation box will be interactively draggable to a new location after being displayed. Defaults to False. hover : boolean, optional If True, the datacursor will "pop up" when the mouse hovers over an artist. Defaults to False. Enabling hover also sets `display="single"` and `draggable=False`. props_override : function, optional If specified, this function customizes the parameters passed into the formatter function and the x, y location that the datacursor "pop up" "points" to. This is often useful to make the annotation "point" to a specific side or corner of an artist, regardless of the position clicked. The function is passed the same kwargs as the `formatter` function and is expected to return a dict with at least the keys "x" and "y" (and probably several others). Expected call signature: `props_dict = props_override(**kwargs)` keybindings : boolean or dict, optional By default, the keys "d" and "t" will be bound to deleting/hiding all annotation boxes and toggling interactivity for datacursors, respectively. If keybindings is False, the ability to hide/toggle datacursors interactively will be disabled. Alternatively, a dict of the form {'hide':'somekey', 'toggle':'somekey'} may specified to customize the keyboard shortcuts. date_format : string, optional The strftime-style formatting string for dates. Used only if the x or y axes have been set to display dates. Defaults to "%x %X". display_button: int, optional The mouse button that will triggers displaying an annotation box. Defaults to 1, for left-clicking. (Common options are 1:left-click, 2:middle-click, 3:right-click) hide_button: int or None, optional The mouse button that triggers hiding the selected annotation box. Defaults to 3, for right-clicking. (Common options are 1:left-click, 2:middle-click, 3:right-click, None:hiding disabled) keep_inside : boolean, optional Whether or not to adjust the x,y offset to keep the text box inside the figure. This option has no effect on draggable datacursors. Defaults to True. Note: Currently disabled on OSX and NbAgg/notebook backends. **kwargs : additional keyword arguments, optional Additional keyword arguments are passed on to annotate. Returns ------- dc : A ``mpldatacursor.DataCursor`` instance """
import matplotlib.pyplot as plt import numpy as np from mpldatacursor import datacursor data = np.outer(range(10), range(1, 5)) fig, ax = plt.subplots() lines = ax.plot(data) ax.set_title('Click somewhere on a line') datacursor() plt.show()
本实例中,有两个数据元素(artist
):line1
和line2
,datacursor(line1)
函数提供了参数line1
,因此只有line1
可以使用交互式数据光标,line2
则没有效果。
import matplotlib.pyplot as plt import numpy as np from mpldatacursor import datacursor fig, ax = plt.subplots() line1 = ax.plot([1,3]) line2 = ax.plot([1,2]) ax.set_title('Click somewhere on a line') datacursor(line1) plt.show()
mpldatacursor
提供了大量实际案例,详见https://github.com/joferkington/mpldatacursor/tree/master/examples。不再一一分析,仅简单说明功能。
basic_single_annotation.py
:在多子图情况下,默认每个子图的数据光标是独立的,即每个子图都可以显示数据光标,相互不影响。使用datacursor(display='single')
参数后,仅在当前子图显示数据光标,其余子图显示的数据光标自动关闭。change_popup_color.py
:提供了两个案例,一个取消了提示框的边框,一个将提示框的背景色改为白色。hover_example.py
:将数据光标的触发方式由鼠标左键单击改为鼠标悬浮。show_artist_labels.py
:将数据光标默认显示的坐标值改为数据元素的label
。highlighting_example.py
:点击数据元素时,数据元素会高亮(黄色)显示。draggable_example.py
:在一个子图中,同时显示多个数据光标。customize_keyboard_shortcuts.py
:重新绑定数据光标快捷键。labeled_points_example.py
:自定义数据点标签。date_example.py
:日期数据显示。bar_example.py
:在柱状图中,在每个柱上方鼠标悬浮触发数据光标。mpldatacursor
历史悠久,但是迟迟没有发布支持matplotlib3.3
的稳定版,建议源码安装开发版,或者使用mplcursors
包https://github.com/anntzer/mplcursors。
mpldatacursor
功能上还是挺丰富的,可以作为深入学习matplotlib
交互的案例。