We have seen how Matplotlib’s ginput() function can be used to mark regions of interest on plots. Sometimes, the fine details of a plot can only be viewed by using the zoom and pan features to visualise the data. Zoom (magnifying glass icon) and pan buttons (square with 4 arrows icon) are available by default in the toolbar of a
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In a previous post we learned how to use matplotlib’s gridspec to make subplots of unequal size. gridspec is quite powerful, but can be a little complicated to use. This is especially true if you are coming to Python from Matlab. In the current post we learn how to make figures and subplots in a manner that will be more
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Sometimes when we plot data we want to indicate a relevant feature or have a user select a section of the data. For example, if we record electromyography (EMG) and force signals, we might want to have the user mark the baseline EMG level so that it can be removed, and determine the amount of EMG produced during a maximum
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Sometimes we would like to focus more on some data and less on others, but still provide a visual display. The matplotlib function gridspec allows subplots of unequal size to be plotted on the same figure. How this function can be applied will be demonstrated using simulated data. Let’s simulate some common probability distributions of different statistics using Python’s numpy.random
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In the short series on statistical inference, I ran simulations and plotted graphs to show distributions of raw data and statistics for repeated experiments. All simulations and plots were done with the programming language Python and its plotting library Matplotlib. What exactly are Python and Matplotlib, and how can these tools be used to visualise data? Python is an open
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