How to place inline labels in a line plot

labellines, line-plot, matplotlib, plot-annotations, python

Solution

Nice question, a while ago I've experimented a bit with this, but haven't used it a lot because it's still not bulletproof. I divided the plot area into a 32x32 grid and calculated a 'potential field' for the best position of a label for each line according the following rules:

- white space is a good place for a label

- Label should be near corresponding line

- Label should be away from the other lines

The code was something like this:

import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage


def my_legend(axis = None):

    if axis == None:
        axis = plt.gca()

    N = 32
    Nlines = len(axis.lines)
    print Nlines

    xmin, xmax = axis.get_xlim()
    ymin, ymax = axis.get_ylim()

    # the 'point of presence' matrix
    pop = np.zeros((Nlines, N, N), dtype=np.float)    

    for l in range(Nlines):
        # get xy data and scale it to the NxN squares
        xy = axis.lines[l].get_xydata()
        xy = (xy - [xmin,ymin]) / ([xmax-xmin, ymax-ymin]) * N
        xy = xy.astype(np.int32)
        # mask stuff outside plot        
        mask = (xy[:,0] >= 0) & (xy[:,0] < N) & (xy[:,1] >= 0) & (xy[:,1] < N)
        xy = xy[mask]
        # add to pop
        for p in xy:
            pop[l][tuple(p)] = 1.0

    # find whitespace, nice place for labels
    ws = 1.0 - (np.sum(pop, axis=0) > 0) * 1.0 
    # don't use the borders
    ws[:,0]   = 0
    ws[:,N-1] = 0
    ws[0,:]   = 0  
    ws[N-1,:] = 0  

    # blur the pop's
    for l in range(Nlines):
        pop[l] = ndimage.gaussian_filter(pop[l], sigma=N/5)

    for l in range(Nlines):
        # positive weights for current line, negative weight for others....
        w = -0.3 * np.ones(Nlines, dtype=np.float)
        w[l] = 0.5

        # calculate a field         
        p = ws + np.sum(w[:, np.newaxis, np.newaxis] * pop, axis=0)
        plt.figure()
        plt.imshow(p, interpolation='nearest')
        plt.title(axis.lines[l].get_label())

        pos = np.argmax(p)  # note, argmax flattens the array first 
        best_x, best_y =  (pos / N, pos % N) 
        x = xmin + (xmax-xmin) * best_x / N       
        y = ymin + (ymax-ymin) * best_y / N       


        axis.text(x, y, axis.lines[l].get_label(), 
                  horizontalalignment='center',
                  verticalalignment='center')


plt.close('all')

x = np.linspace(0, 1, 101)
y1 = np.sin(x * np.pi / 2)
y2 = np.cos(x * np.pi / 2)
y3 = x * x
plt.plot(x, y1, 'b', label='blue')
plt.plot(x, y2, 'r', label='red')
plt.plot(x, y3, 'g', label='green')
my_legend()
plt.show()

And the resulting plot:

Problem

In Matplotlib, it's not too tough to make a legend (`example_legend()`, below), but I think it's better style to put labels right on the curves being plotted (as in `example_inline()`, below). This can be very fiddly, because I have to specify coordinates by hand, and, if I re-format the plot, I probably have to reposition the labels. Is there a way to automatically generate labels on curves in Matplotlib? Bonus points for being able to orient the text at an angle corresponding to the angle of the curve. ``` import numpy as np import matplotlib.pyplot as plt def example_legend(): plt.clf() x = np.linspace(0, 1, 101) y1 = np.sin(x * np.pi / 2) y2 = np.cos(x * np.pi / 2) plt.plot(x, y1, label='sin') plt.plot(x, y2, label='cos') plt.legend() ``` ``` def example_inline(): plt.clf() x = np.linspace(0, 1, 101) y1 = np.sin(x * np.pi / 2) y2 = np.cos(x * np.pi / 2) plt.plot(x, y1, label='sin') plt.plot(x, y2, label='cos') plt.text(0.08, 0.2, 'sin') plt.text(0.9, 0.2, 'cos') ```

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