Inline labels in Matplotlib

Question

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

User · Answer

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

User · Answer

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   gt   0   amp   xy   0   lt  N   amp   xy   1   gt   0   amp   xy   1   lt  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   gt  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

User · Answer

Update  User cphyc has kindly created a Github repository for the code in this answer  see here   and bundled the code into a package which may be installed using pip install matplotlib-label-lines     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 axes     xdata   line get xdata       ydata   line get ydata        if  x  lt  xdata 0   or  x  gt  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  lt  xdata i               ip   i             break      y   ydata ip-1     ydata ip -ydata ip-1    x-xdata ip-1    xdata ip -xdata ip-1        if not label          label   line get label        if align           Compute the slope         dx   xdata ip  - xdata ip-1          dy   ydata ip  - ydata ip-1          ang   degrees atan2 dy dx             Transform to screen co-ordinates         pt   np array  x y   reshape  1 2           trans angle   ax transData transform angles np array  ang    pt  0       else          trans angle   0       Set a bunch of keyword arguments     if  color  not in kwargs          kwargs  color     line get color        if   horizontalalignment  not in kwargs  and   ha  not in kwargs           kwargs  ha      center       if   verticalalignment  not in kwargs  and   va  not in kwargs           kwargs  va      center       if  backgroundcolor  not in kwargs          kwargs  backgroundcolor     ax get facecolor        if  clip on  not in kwargs          kwargs  clip on     True      if  zorder  not in kwargs          kwargs  zorder     2 5      ax text x y label rotation trans angle   kwargs   def labelLines lines align True xvals None   kwargs        ax   lines 0  axes     labLines          labels            Take only the lines which have labels other than the default ones     for line in lines          label   line get label           if   line  not in label              labLines append line              labels append label       if xvals is None          xmin xmax   ax get xlim           xvals   np linspace xmin xmax len labLines  2  1 -1       for line x label in zip labLines xvals labels           labelLine line x label align   kwargs    Test code to generate the pretty picture above   from matplotlib import pyplot as plt from scipy stats import loglaplace chi2  from labellines 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

User · Answer

A  simpler approach like the one  Ioannis Filippidis do   import matplotlib pyplot as plt import numpy as np    evenly sampled time at 200ms intervals tMin -1  tMax 10 t   np arange tMin  tMax  0 1     red dashes  blue points default plt plot t  22 t   r--   t  t  2   b    factor 3 4  offset 20    text position in view   textPosition   tMax tMin  factor 22  tMax tMin  factor  plt text textPosition 0  textPosition 1  offset  22  t  color  red  fontsize 20  textPosition   tMax tMin  factor   tMax tMin  factor   2 20  plt text textPosition 0  textPosition 1  offset   t 2   bbox dict facecolor  blue   alpha 0 5  fontsize 20  plt show    code python 3 on sageCell

[matplotlib] Inline labels in Matplotlib

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