Inline labels in Matplotlib

问题:

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') 

回答1:

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] = 0) & (xy[:,1]  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:

回答2:

Pretty Picture:

In matplotlib it's pretty easy to label contour plots (either automatically or by manually placing labels with mouse clicks). There does not (yet) appear to be any equivalent capability to label data series in this fashion! There may be some semantic reason for not including this feature which I am missing.

Regardless, I have written the following module which takes any allows for semi-automatic plot labelling. It requires only numpy and a couple of functions from the standard math library.

Description

The default behaviour of the labelLines function is to space the labels evenly along the x axis (automatically placing at the correct y-value of course). If you want you can just pass an array of the x co-ordinates of each of the labels. You can even tweak the location of one label (as shown in the bottom right plot) and space the rest evenly if you like.

In addition, the label_lines function does not account for the lines which have not had a label assigned in the plot command (or more accurately if the label contains '_line').

Keyword arguments passed to labelLines or labelLine are passed on to the text function call (some keyword arguments are set if the calling code chooses not to specify).

Issues

• Annotation bounding boxes sometimes interfere undesirably with other curves. As shown by the 1 and 10 annotations in the top left plot. I'm not even sure this can be avoided.
• It would be nice to specify a y position instead sometimes.
• It's still an iterative process to get annotations in the right location
• It only works when the x-axis values are floats

Gotchas

• By default, the labelLines function assumes that all data series span the range specified by the axis limits. Take a look at the blue curve in the top left plot of the pretty picture. If there were only data available for the x range 0.5-1 then then we couldn't possibly place a label at the desired location (which is a little less than 0.2). See this question for a particularly nasty example. Right now, the code does not intelligently identify this scenario and re-arrange the labels, however there is a reasonable workaround. The labelLines function takes the xvals argument; a list of x-values specified by the user instead of the default linear distribution across the width. So the user can decide which x-values to use for the label placement of each data series.

Also, I believe this is the first answer to complete the bonus objective of aligning the labels with the curve they're on. :)

label_lines.py:

from math import atan2,degrees import numpy as np  #Label line with line2D label data def labelLine(line,x,label=None,align=True,**kwargs):      ax = line.get_axes()     xdata = line.get_xdata()     ydata = line.get_ydata()      if (x  xdata[-1]):         print('x label location is outside data range!')         return      #Find corresponding y co-ordinate and angle of the line     ip = 1     for i in range(len(xdata)):         if x 

Test code to generate the pretty picture above:

from matplotlib import pyplot as plt from scipy.stats import loglaplace,chi2  from label_lines import *  X = np.linspace(0,1,500) A = [1,2,5,10,20] funcs = [np.arctan,np.sin,loglaplace(4).pdf,chi2(5).pdf]  plt.subplot(221) for a in A:     plt.plot(X,np.arctan(a*X),label=str(a))  labelLines(plt.gca().get_lines(),zorder=2.5)  plt.subplot(222) for a in A:     plt.plot(X,np.sin(a*X),label=str(a))  labelLines(plt.gca().get_lines(),align=False,fontsize=14)  plt.subplot(223) for a in A:     plt.plot(X,loglaplace(4).pdf(a*X),label=str(a))  xvals = [0.8,0.55,0.22,0.104,0.045] labelLines(plt.gca().get_lines(),align=False,xvals=xvals,color='k')  plt.subplot(224) for a in A:     plt.plot(X,chi2(5).pdf(a*X),label=str(a))  lines = plt.gca().get_lines() l1=lines[-1] labelLine(l1,0.6,label=r'$Re=${}'.format(l1.get_label()),ha='left',va='bottom',align = False) labelLines(lines[:-1],align=False)  plt.show() 

回答3:

@Jan Kuiken's answer is certainly well-thought and thorough, but there are some caveats:

• it does not work in all cases
• it requires a fair amount of extra code
• it may vary considerably from one plot to the next

A much simpler approach is to annotate the last point of each plot. The point can also be circled, for emphasis. This can be accomplished with one extra line:

from matplotlib import pyplot as plt  for i, (x, y) in enumerate(samples):     plt.plot(x, y)     plt.text(x[-1], y[-1], 'sample {i}'.format(i=i)) 

A variant would be to use ax.annotate.

文章来源: Inline labels in Matplotlib