# mpldatacursor

**Repository Path**: fhaohhao/mpldatacursor

## Basic Information

- **Project Name**: mpldatacursor
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-22
- **Last Updated**: 2024-04-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

mpldatacursor
=============
``mpldatacursor`` provides interactive "data cursors" (clickable annotation
boxes) for matplotlib. 

Basic Usage
-----------
``mpldatacursor`` offers a few different styles of interaction through the 
``datacursor`` function. 

As an example, this displays the x, y coordinates of the selected artist in an
annotation box::

        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(lines)

        plt.show()

.. image:: http://joferkington.github.com/mpldatacursor/images/basic.png
    :align: center
    :target: https://github.com/joferkington/mpldatacursor/blob/master/examples/basic.py

If no artist or sequence of artists is specified, all manually plotted artists
in all axes in all figures will be activated. (This can be limited to only
certain axes by passing in an axes object or a sequence of axes to the ``axes``
kwarg.)

As an example (the output is identical to the first example)::

        import matplotlib.pyplot as plt
        import numpy as np
        from mpldatacursor import datacursor

        data = np.outer(range(10), range(1, 5))

        plt.plot(data)
        plt.title('Click somewhere on a line')

        datacursor()

        plt.show()

Hiding Annotation Boxes and Keyboard Shortcuts
----------------------------------------------
To hide a specific annotation box, right-click on it (Customizable through the
``hide_button`` kwarg).  To hide all annotation boxes, press "d" on the
keyboard.  (Think of "delete".  "h" was taken by matplotlib's default key for
"home".) Hitting "d" again will re-display all of the datacursors that were
hidde. To disable or re-enable interactive datacursors, press "t" (for
"toggle").  Pressing "t" will prevent clicks from creating datacursors until
"t" is pressed again. ``<shift> + <right arrow>`` and ``<shift> + <left
arrow>`` will move the datacursor to the next or previous item in the sequence
for artists that support it.  At present, this is more-or-less limited to
artists created with ``plot`` and ``scatter``. These keys can be customized
through the ``keybindings`` kwarg.

Controlling the Displayed Text
------------------------------
The displayed text can be controlled either by using the ``formatter`` kwarg, 
which expects a function that accepts an arbitrary sequence of kwargs and
returns the string to be displayed. Often, it's convenient to pass in the
``format`` method of a template string (e.g. 
``formatter="longitude:{x:.2f}\nlatitude{y:.2f}".format``).

As an example of using the ``formatter`` kwarg to display only the label of the
artist instead of the x, y coordinates::

        import numpy as np
        import matplotlib.pyplot as plt
        from mpldatacursor import datacursor

        x = np.linspace(0, 10, 100)

        fig, ax = plt.subplots()
        ax.set_title('Click on a line to display its label')

        # Plot a series of lines with increasing slopes...
        for i in range(1, 20):
            ax.plot(x, i * x, label='$y = {}x$'.format(i))

        # Use a DataCursor to interactively display the label for a selected line...
        datacursor(formatter='{label}'.format)

        plt.show()

.. image:: http://joferkington.github.com/mpldatacursor/images/show_artist_labels.png
    :align: center
    :target: https://github.com/joferkington/mpldatacursor/blob/master/examples/show_artist_labels.py

Working with Images
-------------------
``datacursor`` will also display the array value at the selected point in an
image. This example also demonstrates using the ``display="single"`` option to
display only one data cursor instead of one-per-axes.::

        import matplotlib.pyplot as plt
        import numpy as np
        from mpldatacursor import datacursor

        data = np.arange(100).reshape((10,10))

        fig, axes = plt.subplots(ncols=2)
        axes[0].imshow(data, interpolation='nearest', origin='lower')
        axes[1].imshow(data, interpolation='nearest', origin='upper',
                             extent=[200, 300, 400, 500])
        datacursor(display='single')

        fig.suptitle('Click anywhere on the image')

        plt.show()

.. image:: http://joferkington.github.com/mpldatacursor/images/image_example.png
    :align: center
    :target: https://github.com/joferkington/mpldatacursor/blob/master/examples/image_example.py

Draggable Boxes
---------------
If ``draggable=True`` is specified, the annotation box can be interactively
dragged to a new position after creation.

As an example (This also demonstrates using the ``display='multiple'`` kwarg)::

        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()
        ax.set_title('Try dragging the annotation boxes')
        ax.plot(data)

        datacursor(display='multiple', draggable=True)

        plt.show()

.. image:: http://joferkington.github.com/mpldatacursor/images/draggable_example.png
    :align: center
    :target: https://github.com/joferkington/mpldatacursor/blob/master/examples/draggable_example.py

Further Customization
---------------------
Additional keyword arguments to ``datacursor`` are passed on to ``annotate``.
This allows one to control the appearance and location of the "popup box",
arrow, etc.  Note that properties passed in for the ``bbox`` and ``arrowprops``
kwargs will be merged with the default style.  Therefore, specifying things
like ``bbox=dict(alpha=1)`` will yield an opaque, yellow, rounded box, instead
of matplotlib's default blue, square box. As a basic example::

        import matplotlib.pyplot as plt
        import numpy as np
        from mpldatacursor import datacursor

        fig, axes = plt.subplots(ncols=2)

        left_artist = axes[0].plot(range(11))
        axes[0].set(title='No box, different position', aspect=1.0)

        right_artist = axes[1].imshow(np.arange(100).reshape(10,10))
        axes[1].set(title='Fancy white background')

        # Make the text pop up "underneath" the line and remove the box...
        dc1 = datacursor(left_artist, xytext=(15, -15), bbox=None)

        # Make the box have a white background with a fancier connecting arrow
        dc2 = datacursor(right_artist, bbox=dict(fc='white'),
                         arrowprops=dict(arrowstyle='simple', fc='white', alpha=0.5))

        plt.show()

.. image:: http://joferkington.github.com/mpldatacursor/images/change_popup_color.png
    :align: center
    :target: https://github.com/joferkington/mpldatacursor/blob/master/examples/change_popup_color.py

Highlighting Selected Lines
---------------------------
``HighlightingDataCursor`` highlights a ``Line2D`` artist in addition to
displaying the selected coordinates.::

        import numpy as np
        import matplotlib.pyplot as plt
        from mpldatacursor import HighlightingDataCursor

        x = np.linspace(0, 10, 100)

        fig, ax = plt.subplots()

        # Plot a series of lines with increasing slopes...
        lines = []
        for i in range(1, 20):
            line, = ax.plot(x, i * x, label='$y = {}x$'.format(i))
            lines.append(line)

        HighlightingDataCursor(lines)

        plt.show()

.. image:: http://joferkington.github.com/mpldatacursor/images/highlighting_example.png
    :align: center
    :target: https://github.com/joferkington/mpldatacursor/blob/master/examples/highlighting_example.py

Installation
------------
``mpldatacursor`` can be installed from PyPi using
``easy_install``/``pip``/etc. (e.g. ``pip install mpldatacursor``) or you may
download the source and install it directly with ``python setup.py install``.