.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_gallery_images_contours_and_fields_irregulardatagrid.py:
=======================================
Contour plot of irregularly spaced data
=======================================
Comparison of a contour plot of irregularly spaced data interpolated
on a regular grid versus a tricontour plot for an unstructured triangular grid.
Since `~.axes.Axes.contour` and `~.axes.Axes.contourf` expect the data to live
on a regular grid, plotting a contour plot of irregularly spaced data requires
different methods. The two options are:
* Interpolate the data to a regular grid first. This can be done with on-board
means, e.g. via `~.tri.LinearTriInterpolator` or using external functionality
e.g. via `scipy.interpolate.griddata`. Then plot the interpolated data with
the usual `~.axes.Axes.contour`.
* Directly use `~.axes.Axes.tricontour` or `~.axes.Axes.tricontourf` which will
perform a triangulation internally.
This example shows both methods in action.
.. code-block:: default
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
np.random.seed(19680801)
npts = 200
ngridx = 100
ngridy = 200
x = np.random.uniform(-2, 2, npts)
y = np.random.uniform(-2, 2, npts)
z = x * np.exp(-x**2 - y**2)
fig, (ax1, ax2) = plt.subplots(nrows=2)
# -----------------------
# Interpolation on a grid
# -----------------------
# A contour plot of irregularly spaced data coordinates
# via interpolation on a grid.
# Create grid values first.
xi = np.linspace(-2.1, 2.1, ngridx)
yi = np.linspace(-2.1, 2.1, ngridy)
# Linearly interpolate the data (x, y) on a grid defined by (xi, yi).
triang = tri.Triangulation(x, y)
interpolator = tri.LinearTriInterpolator(triang, z)
Xi, Yi = np.meshgrid(xi, yi)
zi = interpolator(Xi, Yi)
# Note that scipy.interpolate provides means to interpolate data on a grid
# as well. The following would be an alternative to the four lines above:
#from scipy.interpolate import griddata
#zi = griddata((x, y), z, (xi[None, :], yi[:, None]), method='linear')
ax1.contour(xi, yi, zi, levels=14, linewidths=0.5, colors='k')
cntr1 = ax1.contourf(xi, yi, zi, levels=14, cmap="RdBu_r")
fig.colorbar(cntr1, ax=ax1)
ax1.plot(x, y, 'ko', ms=3)
ax1.set(xlim=(-2, 2), ylim=(-2, 2))
ax1.set_title('grid and contour (%d points, %d grid points)' %
(npts, ngridx * ngridy))
# ----------
# Tricontour
# ----------
# Directly supply the unordered, irregularly spaced coordinates
# to tricontour.
ax2.tricontour(x, y, z, levels=14, linewidths=0.5, colors='k')
cntr2 = ax2.tricontourf(x, y, z, levels=14, cmap="RdBu_r")
fig.colorbar(cntr2, ax=ax2)
ax2.plot(x, y, 'ko', ms=3)
ax2.set(xlim=(-2, 2), ylim=(-2, 2))
ax2.set_title('tricontour (%d points)' % npts)
plt.subplots_adjust(hspace=0.5)
plt.show()
.. image:: /gallery/images_contours_and_fields/images/sphx_glr_irregulardatagrid_001.png
:alt: grid and contour (200 points, 20000 grid points), tricontour (200 points)
:class: sphx-glr-single-img
------------
References
""""""""""
The use of the following functions and methods is shown in this example:
.. code-block:: default
import matplotlib
matplotlib.axes.Axes.contour
matplotlib.pyplot.contour
matplotlib.axes.Axes.contourf
matplotlib.pyplot.contourf
matplotlib.axes.Axes.tricontour
matplotlib.pyplot.tricontour
matplotlib.axes.Axes.tricontourf
matplotlib.pyplot.tricontourf
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
.. _sphx_glr_download_gallery_images_contours_and_fields_irregulardatagrid.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: irregulardatagrid.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: irregulardatagrid.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
Keywords: matplotlib code example, codex, python plot, pyplot
`Gallery generated by Sphinx-Gallery
`_