.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/images_contours_and_fields/irregulardatagrid.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. meta:: :keywords: codex .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` 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. .. GENERATED FROM PYTHON SOURCE LINES 22-85 .. code-block:: Python import matplotlib.pyplot as plt import numpy as np import matplotlib.tri as tri 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-sg:: /gallery/images_contours_and_fields/images/sphx_glr_irregulardatagrid_001.png :alt: grid and contour (200 points, 20000 grid points), tricontour (200 points) :srcset: /gallery/images_contours_and_fields/images/sphx_glr_irregulardatagrid_001.png, /gallery/images_contours_and_fields/images/sphx_glr_irregulardatagrid_001_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 86-95 .. admonition:: References The use of the following functions, methods, classes and modules is shown in this example: - `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` .. _sphx_glr_download_gallery_images_contours_and_fields_irregulardatagrid.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: irregulardatagrid.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: irregulardatagrid.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_