Raw data are not always pretty. Data can have different patterns, be noisy, and vary from trial to trial. We usually collect data to measure, or more accurately, estimate an effect or phenomenon. At times, it is necessary to fit a mathematical expression to raw data in order to estimate the underlying effect or phenomenon. We can then use this
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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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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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Every scientist has, at one point or another in their career, plotted results using bar graphs or dot plots with error bars. As pointed in a previous post, these kinds of summary graphs can be misleading, especially since a depressingly large number of scientists plot their error bars as the standard error of the mean (SEM) rather than the standard
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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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Bar and line graphs with standard deviation (SD) or standard error (SE) error bars are often used to visually present continuous data from laboratory studies with small sample sizes. Last year the journal PloS Biology published a paper showing that presenting such data this way is problematic for the following reasons: bar and line graphs conceal how data are distributed
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