labelpoints

Automatically label points in your plot given x and y values & an array of labels.

[Major update with additional features coming soon; don't forget to follow this file.]

Given vectors of x-values, y-values, and a vector of label names, lablepoints.m will automatically place all labels appropriately in the plot and output their handles. This function streamlines matlab's builtin text() function and greatly increases its flexibility.
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xpos = [1,2,3,4]; ypos = [1,4,1,5]; labels = {'Lancaster', 'Cincinnati', 'Sofia', 'Rochester'}
h = labelpoints (xpos, ypos, labels)
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(optional) Choose where you want your label relative to the data point using initials of compass directions (N, S, E, W, NE, NW, SE, SW, Center).
h = labelpoints (xpos, ypos, labels, 'N')
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(optional) Add (or subtract) extra space between data points and labels by using a buffer between 0:1
h = labelpoints (xpos, ypos, labels, 'N', 0.15)
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(optional) Decide whether or not the axis limits should automatically adjust if a label falls out of the axis limits (0/1)
h = labelpoints (xpos, ypos, labels, 'N', 0.15, 1)
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(optional) Let labelpoints.m identify and label only the outliers in your data. Several outlier methods are available (see help labelpoints)
- Identify and label outliers that are N standard deviations from the mean
- Identify and label outliers that are greater than N times the interquartile range
- Identify and label outliers that are inside or outside of a given bound
- Identify and label outliers that are high residuals from a linear fit of the data.
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(optional) Easily place texts in a list stacked downward, upward, to the left or right.
h = labelpoints (xpos, ypos, labels, 'N, 'stacked', 'down')
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(optional) Rotate all labels
h = labelpoints (xpos, ypos, labels, 'rotation', 45)
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(optional) Specify the font size and color of all labels
h = labelpoints (xpos, ypos, labels, 'FontSize', 12, 'Color', 'r')
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The help section includes many versatile examples demonstrating labeling with numbers, strings, and symbols. This function is quite flexible as the examples show. There are only three required inputs, 3 optional inputs, and several optional parameters to increase flexibility. The outlier options may require stats toolbox.