

.. _sphx_glr_gallery_api_collections.py:


=========================================================
Line, Poly and RegularPoly Collection with autoscaling
=========================================================

For the first two subplots, we will use spirals.  Their
size will be set in plot units, not data units.  Their positions
will be set in data units by using the "offsets" and "transOffset"
kwargs of the LineCollection and PolyCollection.

The third subplot will make regular polygons, with the same
type of scaling and positioning as in the first two.

The last subplot illustrates the use of "offsets=(xo,yo)",
that is, a single tuple instead of a list of tuples, to generate
successively offset curves, with the offset given in data
units.  This behavior is available only for the LineCollection.





.. image:: /gallery/api/images/sphx_glr_collections_001.png
    :align: center





.. code-block:: python


    import matplotlib.pyplot as plt
    from matplotlib import collections, colors, transforms
    import numpy as np

    nverts = 50
    npts = 100

    # Make some spirals
    r = np.arange(nverts)
    theta = np.linspace(0, 2*np.pi, nverts)
    xx = r * np.sin(theta)
    yy = r * np.cos(theta)
    spiral = list(zip(xx, yy))

    # Make some offsets
    # Fixing random state for reproducibility
    rs = np.random.RandomState(19680801)


    xo = rs.randn(npts)
    yo = rs.randn(npts)
    xyo = list(zip(xo, yo))

    # Make a list of colors cycling through the default series.
    colors = [colors.to_rgba(c)
              for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]

    fig, axes = plt.subplots(2, 2)
    fig.subplots_adjust(top=0.92, left=0.07, right=0.97,
                        hspace=0.3, wspace=0.3)
    ((ax1, ax2), (ax3, ax4)) = axes  # unpack the axes


    col = collections.LineCollection([spiral], offsets=xyo,
                                     transOffset=ax1.transData)
    trans = fig.dpi_scale_trans + transforms.Affine2D().scale(1.0/72.0)
    col.set_transform(trans)  # the points to pixels transform
    # Note: the first argument to the collection initializer
    # must be a list of sequences of x,y tuples; we have only
    # one sequence, but we still have to put it in a list.
    ax1.add_collection(col, autolim=True)
    # autolim=True enables autoscaling.  For collections with
    # offsets like this, it is neither efficient nor accurate,
    # but it is good enough to generate a plot that you can use
    # as a starting point.  If you know beforehand the range of
    # x and y that you want to show, it is better to set them
    # explicitly, leave out the autolim kwarg (or set it to False),
    # and omit the 'ax1.autoscale_view()' call below.

    # Make a transform for the line segments such that their size is
    # given in points:
    col.set_color(colors)

    ax1.autoscale_view()  # See comment above, after ax1.add_collection.
    ax1.set_title('LineCollection using offsets')


    # The same data as above, but fill the curves.
    col = collections.PolyCollection([spiral], offsets=xyo,
                                     transOffset=ax2.transData)
    trans = transforms.Affine2D().scale(fig.dpi/72.0)
    col.set_transform(trans)  # the points to pixels transform
    ax2.add_collection(col, autolim=True)
    col.set_color(colors)


    ax2.autoscale_view()
    ax2.set_title('PolyCollection using offsets')

    # 7-sided regular polygons

    col = collections.RegularPolyCollection(
        7, sizes=np.abs(xx) * 10.0, offsets=xyo, transOffset=ax3.transData)
    trans = transforms.Affine2D().scale(fig.dpi / 72.0)
    col.set_transform(trans)  # the points to pixels transform
    ax3.add_collection(col, autolim=True)
    col.set_color(colors)
    ax3.autoscale_view()
    ax3.set_title('RegularPolyCollection using offsets')


    # Simulate a series of ocean current profiles, successively
    # offset by 0.1 m/s so that they form what is sometimes called
    # a "waterfall" plot or a "stagger" plot.

    nverts = 60
    ncurves = 20
    offs = (0.1, 0.0)

    yy = np.linspace(0, 2*np.pi, nverts)
    ym = np.max(yy)
    xx = (0.2 + (ym - yy)/ym)**2 * np.cos(yy - 0.4)*0.5
    segs = []
    for i in range(ncurves):
        xxx = xx + 0.02*rs.randn(nverts)
        curve = list(zip(xxx, yy*100))
        segs.append(curve)

    col = collections.LineCollection(segs, offsets=offs)
    ax4.add_collection(col, autolim=True)
    col.set_color(colors)
    ax4.autoscale_view()
    ax4.set_title('Successive data offsets')
    ax4.set_xlabel('Zonal velocity component (m/s)')
    ax4.set_ylabel('Depth (m)')
    # Reverse the y-axis so depth increases downward
    ax4.set_ylim(ax4.get_ylim()[::-1])


    plt.show()

**Total running time of the script:** ( 0 minutes  0.142 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: collections.py <collections.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: collections.ipynb <collections.ipynb>`

.. rst-class:: sphx-glr-signature

    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
