.. _ch_examples: ========================================================== wxmplot Examples ========================================================== The :ref:`ch_overview` showed a few illustrative examples using `wxmplot`. Here we show a few more examples. These and more are given in the *examples* directory in the source distribution kit. .. _wxmplot examples: https://github.com/newville/wxmplot/tree/master/examples Dynamic examples not shown here ---------------------------------- Several examples that can be found at `wxmplot examples`_ are not shown here either because they show many plots or are otherwise more complex. They are worth trying out. *demo.py* will show several Line plot examples, including a plot which uses a timer to simulate a dynamic plot, updating the plot as fast as it can - typically 20 to 30 times per second, depending on your machine. *stripchart.py* also shows dynamic, time-based plot. *scope_mode_function.py* and *scope_mode_generator.py* both show dynamic plots with data uddated with a user-supplied function that either returns or yields datasets to update plot traces. *theme_compare.py* renders the same plot with a selection of different themes. *image_scroll.py* shows an updating set of images on a single display. Perhaps surprisingly, this can be faster than updating the line plots. Scatterplot Example ------------------------- An example scatterplot can be produced with a script like this: .. literalinclude:: ../examples/scatterplot.py and gives a plot (after having selected by "lasso"ing) that looks like this: .. image:: images/scatterplot.png :width: 85 % Plotting with errorbars ---------------------------- An example plotting with error bars: .. literalinclude:: ../examples/errorbar.py gives: .. image:: images/errorbar.png :width: 85 % Plotting data from a datafile ----------------------------------------- Reading data with `numpy.loadtext` and plotting: .. literalinclude:: ../examples/plot_fromdatafile.py gives: .. image:: images/datafile_plot.png :width: 85 % Using Left and Right Axes ---------------------------- An example using both right and left axes with different scales can be created with: .. literalinclude:: ../examples/leftright.py and gives a plot that looks like this: .. image:: images/two_axes.png :width: 85 % Plotting with alpha-fill to show area under a curve ----------------------------------------------------- It is sometimes desirable to fill the area below a curve, typically to 0. Using the `alpha` value can be especially helpful for this, so that .. literalinclude:: ../examples/fill_to_zero.py will give: .. image:: images/plot_alphafill_to_zero.png :width: 85 % Plotting with alpha-fill to show uncertainty ----------------------------------------------------- Another use of a filled band is to fill between two traces. An important use of this is to show uncertainties in a function, similar to showing errorbars above. If `dy` and `fill=True` are both given, then a band between `y-dy` and `y+dy` will be filled, as with: .. literalinclude:: ../examples/fill_uncertainties.py which gives: .. image:: images/plot_alphafill_to_dy.png :width: 85 % Of course, you can use that to recast showing a band between any two curves by assigning the average of the 2 curves to `y` and half the difference to `dy`, and perhaps setting `linewidth=0` to suppress showing the mean value. Using `set_data_generator` for user-controlled, dynamic plotting --------------------------------------------------------------------- There are three examples that use :func:`set_data_generator` to specify how to update a plot from a user-supplied function. As seen in these examples, the function definied can either return data to update the data, or it can use a Python geneator to yield the data. In both cases, you first create a plot (it can be empty), and then set the function for that plot window to call to grab new data. The plot window will then periodically call the function you supply, with a time interval (in milliseconds) given by the `polltime` argument. With a simple function, it might look like .. literalinclude:: ../examples/scope_mode_function.py This will generate a continuously updating plot adding data as it goes: .. video:: _static/scope_mode_function.mp4 :alt: capture of images generated from scope_mode_function.py :muted: As a second example, this time using a generator, you might do something like this: .. literalinclude:: ../examples/scope_mode_generator.py which will generate a plot like this: .. video:: _static/scope_mode_generator.mp4 :alt: capture of images generated from scope_mode_generator.py :muted: Note that your function should return or yield a list of (x, y) pairs. As a third example, and by way of comparison with the matplotlib example at https://matplotlib.org/stable/gallery/animation/strip_chart.html, a similar result can be generated with the somewhat shorter and less involved code example .. literalinclude:: ../examples/scope_mode_mpl_compare.py Unlike with the matplotlib example, which mixes data generation and management with plotting code, the :class:`Scope` here only generates the code, and wxmplot functions handles all the plotting. This code is both shorter and better designed than the standard matplotlib example. Displaying and image of a TIFF file -------------------------------------- Reading a TIFF file and showing the image: .. literalinclude:: ../examples/tiff_display.py gives: .. image:: images/tifffile_image.png :width: 85 % 3-Color Image ----------------- If the data array has three dimensions, and has a shape of (NY, NX, 3), it is assumed to be a 3 color map, holding Red, Green, and Blue intensities. In this case, the Image Frame will show sliders and min/max controls for each of the three colors. .. literalinclude:: ../examples/rgb_image.py giving a plot that would look like this: .. image:: images/image_3color.png :width: 85% Note that there is also an Image->Toggle Background Color (Black/White) menu selection that can switch the zero intensity color between black and white. The same image with a white background looks like: .. image:: images/image_3color_white.png :width: 85% This gives a slightly different view of the same data, with results that may be more suitable for printed documents and presentations.