Have a plot with several diagonal lines with different slopes. I would like to annotate these lines with a text label that matches the slope of the lines.
Something like this:
Is there a robust way to do this?
I've tried both text's and annotate's rotation parameters, but those are in screen coordinates, not data coordinates (i.e. it's always x degrees on the screen no matter the xy ranges). My x and y ranges differ by orders of magnitude, and obviously the apparent slope is affected by viewport size among other variables, so a fixed-degree rotation doesn't do the trick. Any other ideas?
This is the exact same process and basic code as given by #Adam --- it's just restructured to be (hopefully) a little more convenient.
def label_line(line, label, x, y, color='0.5', size=12):
"""Add a label to a line, at the proper angle.
Arguments
---------
line : matplotlib.lines.Line2D object,
label : str
x : float
x-position to place center of text (in data coordinated
y : float
y-position to place center of text (in data coordinates)
color : str
size : float
"""
xdata, ydata = line.get_data()
x1 = xdata[0]
x2 = xdata[-1]
y1 = ydata[0]
y2 = ydata[-1]
ax = line.get_axes()
text = ax.annotate(label, xy=(x, y), xytext=(-10, 0),
textcoords='offset points',
size=size, color=color,
horizontalalignment='left',
verticalalignment='bottom')
sp1 = ax.transData.transform_point((x1, y1))
sp2 = ax.transData.transform_point((x2, y2))
rise = (sp2[1] - sp1[1])
run = (sp2[0] - sp1[0])
slope_degrees = np.degrees(np.arctan2(rise, run))
text.set_rotation(slope_degrees)
return text
Used like:
import numpy as np
import matplotlib.pyplot as plt
...
fig, axes = plt.subplots()
color = 'blue'
line, = axes.plot(xdata, ydata, '--', color=color)
...
label_line(line, "Some Label", x, y, color=color)
Edit: note that this method still needs to be called after the figure layout is finalized, otherwise things will be altered.
See: https://gist.github.com/lzkelley/0de9e8bf2a4fe96d2018f1b1bd5a0d3c
I came up with something that works for me. Note the grey dashed lines:
The rotation must be set manually, but this must be done AFTER draw() or layout. So my solution is to associate lines with annotations, then iterate through them and do this:
get line's data transform (i.e. goes from data coordinates to display coordinates)
transform two points along the line to display coordinates
find slope of displayed line
set text rotation to match this slope
This isn't perfect, because matplotlib's handling of rotated text is all wrong. It aligns by the bounding box and not by the text baseline.
Some font basics if you're interested about text rendering: http://docs.oracle.com/javase/tutorial/2d/text/fontconcepts.html
This example shows what matplotlib does: http://matplotlib.org/examples/pylab_examples/text_rotation.html
The only way I found to have a label properly next to the line is to align by center in both vertical and horizontal. I then offset the label by 10 points to the left to make it not overlap. Good enough for my application.
Here is my code. I draw the line however I want, then draw the annotation, then bind them with a helper function:
line, = fig.plot(xdata, ydata, '--', color=color)
# x,y appear on the midpoint of the line
t = fig.annotate("text", xy=(x, y), xytext=(-10, 0), textcoords='offset points', horizontalalignment='left', verticalalignment='bottom', color=color)
text_slope_match_line(t, x, y, line)
Then call another helper function after layout but before savefig (For interactive images I think you'll have to register for draw events and call update_text_slopes in the handler)
plt.tight_layout()
update_text_slopes()
The helpers:
rotated_labels = []
def text_slope_match_line(text, x, y, line):
global rotated_labels
# find the slope
xdata, ydata = line.get_data()
x1 = xdata[0]
x2 = xdata[-1]
y1 = ydata[0]
y2 = ydata[-1]
rotated_labels.append({"text":text, "line":line, "p1":numpy.array((x1, y1)), "p2":numpy.array((x2, y2))})
def update_text_slopes():
global rotated_labels
for label in rotated_labels:
# slope_degrees is in data coordinates, the text() and annotate() functions need it in screen coordinates
text, line = label["text"], label["line"]
p1, p2 = label["p1"], label["p2"]
# get the line's data transform
ax = line.get_axes()
sp1 = ax.transData.transform_point(p1)
sp2 = ax.transData.transform_point(p2)
rise = (sp2[1] - sp1[1])
run = (sp2[0] - sp1[0])
slope_degrees = math.degrees(math.atan(rise/run))
text.set_rotation(slope_degrees)
Even though this question is old, I keep coming across it and get frustrated, that it does not quite work. I reworked it into a class LineAnnotation and helper line_annotate such that it
uses the slope at a specific point x,
works with re-layouting and resizing, and
accepts a relative offset perpendicular to the slope.
x = np.linspace(np.pi, 2*np.pi)
line, = plt.plot(x, np.sin(x))
for x in [3.5, 4.0, 4.5, 5.0, 5.5, 6.0]:
line_annotate(str(x), line, x)
I originally put it into a public gist, but #Adam asked me to include it here.
import numpy as np
from matplotlib.text import Annotation
from matplotlib.transforms import Affine2D
class LineAnnotation(Annotation):
"""A sloped annotation to *line* at position *x* with *text*
Optionally an arrow pointing from the text to the graph at *x* can be drawn.
Usage
-----
fig, ax = subplots()
x = linspace(0, 2*pi)
line, = ax.plot(x, sin(x))
ax.add_artist(LineAnnotation("text", line, 1.5))
"""
def __init__(
self, text, line, x, xytext=(0, 5), textcoords="offset points", **kwargs
):
"""Annotate the point at *x* of the graph *line* with text *text*.
By default, the text is displayed with the same rotation as the slope of the
graph at a relative position *xytext* above it (perpendicularly above).
An arrow pointing from the text to the annotated point *xy* can
be added by defining *arrowprops*.
Parameters
----------
text : str
The text of the annotation.
line : Line2D
Matplotlib line object to annotate
x : float
The point *x* to annotate. y is calculated from the points on the line.
xytext : (float, float), default: (0, 5)
The position *(x, y)* relative to the point *x* on the *line* to place the
text at. The coordinate system is determined by *textcoords*.
**kwargs
Additional keyword arguments are passed on to `Annotation`.
See also
--------
`Annotation`
`line_annotate`
"""
assert textcoords.startswith(
"offset "
), "*textcoords* must be 'offset points' or 'offset pixels'"
self.line = line
self.xytext = xytext
# Determine points of line immediately to the left and right of x
xs, ys = line.get_data()
def neighbours(x, xs, ys, try_invert=True):
inds, = np.where((xs <= x)[:-1] & (xs > x)[1:])
if len(inds) == 0:
assert try_invert, "line must cross x"
return neighbours(x, xs[::-1], ys[::-1], try_invert=False)
i = inds[0]
return np.asarray([(xs[i], ys[i]), (xs[i+1], ys[i+1])])
self.neighbours = n1, n2 = neighbours(x, xs, ys)
# Calculate y by interpolating neighbouring points
y = n1[1] + ((x - n1[0]) * (n2[1] - n1[1]) / (n2[0] - n1[0]))
kwargs = {
"horizontalalignment": "center",
"rotation_mode": "anchor",
**kwargs,
}
super().__init__(text, (x, y), xytext=xytext, textcoords=textcoords, **kwargs)
def get_rotation(self):
"""Determines angle of the slope of the neighbours in display coordinate system
"""
transData = self.line.get_transform()
dx, dy = np.diff(transData.transform(self.neighbours), axis=0).squeeze()
return np.rad2deg(np.arctan2(dy, dx))
def update_positions(self, renderer):
"""Updates relative position of annotation text
Note
----
Called during annotation `draw` call
"""
xytext = Affine2D().rotate_deg(self.get_rotation()).transform(self.xytext)
self.set_position(xytext)
super().update_positions(renderer)
def line_annotate(text, line, x, *args, **kwargs):
"""Add a sloped annotation to *line* at position *x* with *text*
Optionally an arrow pointing from the text to the graph at *x* can be drawn.
Usage
-----
x = linspace(0, 2*pi)
line, = ax.plot(x, sin(x))
line_annotate("sin(x)", line, 1.5)
See also
--------
`LineAnnotation`
`plt.annotate`
"""
ax = line.axes
a = LineAnnotation(text, line, x, *args, **kwargs)
if "clip_on" in kwargs:
a.set_clip_path(ax.patch)
ax.add_artist(a)
return a
New in matplotlib 3.4.0
There is now a built-in parameter transform_rotates_text for rotating text relative to a line:
To rotate text with respect to a line, the correct angle won't be the angle of that line in the plot coordinate system, but the angle that the line appears in the screen coordinate system. This angle can be determined automatically by setting the new parameter transform_rotates_text.
So now we can just pass the raw data angle to plt.text and let matplotlib automatically transform it to the correct visual angle by setting transform_rotates_text=True:
# plot line from (1, 4) to (6, 10)
x = [1, 6]
y = [4, 10]
plt.plot(x, y, 'r.-')
# compute angle in raw data coordinates (no manual transforms)
dy = y[1] - y[0]
dx = x[1] - x[0]
angle = np.rad2deg(np.arctan2(dy, dx))
# annotate with transform_rotates_text to align text and line
plt.text(x[0], y[0], f'rotation={angle:.2f}', ha='left', va='bottom',
transform_rotates_text=True, rotation=angle, rotation_mode='anchor')
This approach is robust against the figure and axes scales. Even if we modify the figsize or xlim after placing the text, the rotation stays properly aligned:
# resizing the figure won't mess up the rotation
plt.gcf().set_size_inches(9, 4)
# rescaling the axes won't mess up the rotation
plt.xlim(0, 12)
Related
I need help customizing my plots. I want the canvas to look approximately like the default 2D-graph template from MacOS's Grapher (see screenshot).
To clarify - I need
a centered axis
a grid (preferably with an additional darker grid every 1 unit)
axislines with arrows
only one zero at the origo (when I tried my best, I got one zero from the x-axis and a second one from the y-axis.), slightly moved to the left so it's not behind the y-axis
I really appreciate your help!
This definitely falls under the category of more trouble than it's worth with matplotlib, but here you go. Also, for the basic case, have a look at the centering spines demo in the documentation.
You can do this in a few different ways, but for the best visual effect, consider something along the lines of the following. It's far from perfect, but it's reasonably flexible:
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.patheffects
import numpy as np
def center_spines(ax=None, centerx=0, centery=0):
"""Centers the axis spines at <centerx, centery> on the axis "ax", and
places arrows at the end of the axis spines."""
if ax is None:
ax = plt.gca()
# Set the axis's spines to be centered at the given point
# (Setting all 4 spines so that the tick marks go in both directions)
ax.spines['left'].set_position(('data', centerx))
ax.spines['bottom'].set_position(('data', centery))
ax.spines['right'].set_position(('data', centerx - 1))
ax.spines['top'].set_position(('data', centery - 1))
# Draw an arrow at the end of the spines
ax.spines['left'].set_path_effects([EndArrow()])
ax.spines['bottom'].set_path_effects([EndArrow()])
# Hide the line (but not ticks) for "extra" spines
for side in ['right', 'top']:
ax.spines[side].set_color('none')
# On both the x and y axes...
for axis, center in zip([ax.xaxis, ax.yaxis], [centerx, centery]):
# Turn on minor and major gridlines and ticks
axis.set_ticks_position('both')
axis.grid(True, 'major', ls='solid', lw=0.5, color='gray')
axis.grid(True, 'minor', ls='solid', lw=0.1, color='gray')
axis.set_minor_locator(mpl.ticker.AutoMinorLocator())
# Hide the ticklabels at <centerx, centery>
formatter = CenteredFormatter()
formatter.center = center
axis.set_major_formatter(formatter)
# Add offset ticklabels at <centerx, centery> using annotation
# (Should probably make these update when the plot is redrawn...)
xlabel, ylabel = map(formatter.format_data, [centerx, centery])
ax.annotate('(%s, %s)' % (xlabel, ylabel), (centerx, centery),
xytext=(-4, -4), textcoords='offset points',
ha='right', va='top')
# Note: I'm implementing the arrows as a path effect rather than a custom
# Spines class. In the long run, a custom Spines class would be a better
# way to go. One of the side effects of this is that the arrows aren't
# reversed when the axes are reversed!
class EndArrow(mpl.patheffects._Base):
"""A matplotlib patheffect to add arrows at the end of a path."""
def __init__(self, headwidth=5, headheight=5, facecolor=(0,0,0), **kwargs):
super(mpl.patheffects._Base, self).__init__()
self.width, self.height = headwidth, headheight
self._gc_args = kwargs
self.facecolor = facecolor
self.trans = mpl.transforms.Affine2D()
self.arrowpath = mpl.path.Path(
np.array([[-0.5, -0.2], [0.0, 0.0], [0.5, -0.2],
[0.0, 1.0], [-0.5, -0.2]]),
np.array([1, 2, 2, 2, 79]))
def draw_path(self, renderer, gc, tpath, affine, rgbFace):
scalex = renderer.points_to_pixels(self.width)
scaley = renderer.points_to_pixels(self.height)
x0, y0 = tpath.vertices[-1]
dx, dy = tpath.vertices[-1] - tpath.vertices[-2]
azi = np.arctan2(dy, dx) - np.pi / 2.0
trans = affine + self.trans.clear(
).scale(scalex, scaley
).rotate(azi
).translate(x0, y0)
gc0 = renderer.new_gc()
gc0.copy_properties(gc)
self._update_gc(gc0, self._gc_args)
if self.facecolor is None:
color = rgbFace
else:
color = self.facecolor
renderer.draw_path(gc0, self.arrowpath, trans, color)
renderer.draw_path(gc, tpath, affine, rgbFace)
gc0.restore()
class CenteredFormatter(mpl.ticker.ScalarFormatter):
"""Acts exactly like the default Scalar Formatter, but yields an empty
label for ticks at "center"."""
center = 0
def __call__(self, value, pos=None):
if value == self.center:
return ''
else:
return mpl.ticker.ScalarFormatter.__call__(self, value, pos)
I deliberately didn't set the x and y major tick intervals to 1, but that's easy to do. ax.xaxis.set_major_locator(MultipleLocator(1))
Now you can just call center_spines to do something like this:
x = np.arange(-5, 5)
y = x
line, = plt.plot(x, y)
center_spines()
plt.axis('equal')
plt.show()
Centered axes, see an example here (look for "zeroed spines"):
http://matplotlib.sourceforge.net/examples/pylab_examples/spine_placement_demo.html
grid: ax.grid(True)
Get rid of the zero at the origin: see set_ticks(...) here: http://matplotlib.sourceforge.net/api/axis_api.html
What else did you look for?
I need to do an animation in matplotlib where I want to plot a moving particle (as a point) and a line (corresponding to the magnitude and direction of its speed)
I have taken a look at this website,were there are ifferent examples of animations.
https://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial/
Basically what they do in the examples is initializing line as a plot with certain markers:
line, = ax.plot([], [], 'o') # for points
line2, = ax.plot([], [], '-') # for lines
and in the animate function they just use set_data to update the position of the particle for the next frame
def animate(i):
line.set_data(x, y)
return line,
However in the examples, they either use lines (eg: the pendulum example) OR dots (the particles in a box example), but not both.
How can I first plot the particle as a dot and then add a speed vector as a line?
The animate function has to return line so if I use line and line2 I need a way to merge them.
Something like:
plot(x, y, 'o') # position
plot([x0, x1], [y0, y1], '-') # speed
Thanks
You can define two "lines", one which consists of marked points, but no lines in between (ls="") and one with only a line but no markers (marker="")
points, = ax.plot([1,2], [3,4], marker='o', ls="" ) # for points
line, = ax.plot([2,1], [4,3], marker="" , ls="-") # for lines
The animate function would then return both "lines":
def animate(i):
# x = ...; y = ...
line.set_data(x, y)
# x2 = ...; y2 = ...
points.set_data(x2, y2)
return points, line
I am new to programming in Python. I am working on a project that involves 3D surface plots, I want to get the coordinates of a point on the 3D surface in the event of a mouse click. I have tried checking online for something similar and I have found a neat way to do this for 2D plots. (data tip for 2D plots)
My question is can this be extended for 3D plots? My intuition is that I can obtain the x, y coordinates from the surface plot similar to the solution given in the link, which depending on the current view can be mapped to a 3D coordinate point (does it sound right?). But, I am not able to find a way to do it.
OK, I figured out the solution! might be useful for someone so posting it here. It is combination of two solutions from stack Overflow.
Solution 1
Solution 2
I have used ax.format_coord(x,y) to obtain the z value. With ax.format_coord(mouseevent.xdata,mouseevent.ydata) you get the x, y, z values in a string ('x=0.222, y=0.452, z=0.826') from which you can extract the values.(2)
def getz(x,y,ax):
s = ax.format_coord(x,y)
out = ""
for i in range(s.find('z')+2,len(s)-1):
out = out+s[i]
return float(out)
class DataCursor(object):
"""A simple data cursor widget that displays the x,y location of a
matplotlib artist when it is selected."""
def __init__(self, artists, tolerance=5, offsets=(-20, 20),
template='z: %0.2f', display_all=False):
"""Create the data cursor and connect it to the relevant figure.
"artists" is the matplotlib artist or sequence of artists that will be
selected.
"tolerance" is the radius (in points) that the mouse click must be
within to select the artist.
"offsets" is a tuple of (x,y) offsets in points from the selected
point to the displayed annotation box
"template" is the format string to be used. Note: For compatibility
with older versions of python, this uses the old-style (%)
formatting specification.
"display_all" controls whether more than one annotation box will
be shown if there are multiple axes. Only one will be shown
per-axis, regardless.
"""
self.template = template
self.offsets = offsets
self.display_all = display_all
if not cbook.iterable(artists):
artists = [artists]
self.artists = artists
self.axes = tuple(set(art.axes for art in self.artists))
self.figures = tuple(set(ax.figure for ax in self.axes))
self.annotations = {}
for ax in self.axes:
self.annotations[ax] = self.annotate(ax)
for artist in self.artists:
artist.set_picker(tolerance)
for fig in self.figures:
fig.canvas.mpl_connect('pick_event', self)
def annotate(self, ax):
"""Draws and hides the annotation box for the given axis "ax"."""
annotation = ax.annotate(self.template, xy=(0, 0), ha='right',
xytext=self.offsets, textcoords='offset points', va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')
)
annotation.set_visible(False)
return annotation
def __call__(self, event):
"""Intended to be called through "mpl_connect"."""
# Rather than trying to interpolate, just display the clicked coords
# This will only be called if it's within "tolerance", anyway.
x, y = event.mouseevent.xdata, event.mouseevent.ydata
z = getz(x,y,self.axes[0]) # Just a small change here.
# print str(x) + ' ' + str(y) + ' ' + str(z)
annotation = self.annotations[event.artist.axes]
if x is not None:
if not self.display_all:
# Hide any other annotation boxes...
for ann in self.annotations.values():
ann.set_visible(False)
# Update the annotation in the current axis..
annotation.xy = x, y
annotation.set_text(self.template % (z))
annotation.set_visible(True)
event.canvas.draw()
if __name__ == '__main__':
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
plt.figure()
ax = Axes3D(fig)
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1)
DataCursor([surf])
plt.show()
Hope this helps. Thanks!
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')
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 < xdata[0]) or (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 < 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()
#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:
import matplotlib.pyplot as plt
for i, (x, y) in enumerate(samples):
plt.plot(x, y)
plt.text(x[-1], y[-1], f'sample {i}')
A variant would be to use the method matplotlib.axes.Axes.annotate.
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:
matplotx (which I wrote) has line_labels() which plots the labels to the right of the lines. It's also smart enough to avoid overlaps when too many lines are concentrated in one spot. (See stargraph for examples.) It does that by solving a particular non-negative-least-squares problem on the target positions of the labels. Anyway, in many cases where there's no overlap to begin with, such as the example below, that's not even necessary.
import matplotlib.pyplot as plt
import matplotx
import numpy as np
# create data
rng = np.random.default_rng(0)
offsets = [1.0, 1.50, 1.60]
labels = ["no balancing", "CRV-27", "CRV-27*"]
x0 = np.linspace(0.0, 3.0, 100)
y = [offset * x0 / (x0 + 1) + 0.1 * rng.random(len(x0)) for offset in offsets]
# plot
with plt.style.context(matplotx.styles.dufte):
for yy, label in zip(y, labels):
plt.plot(x0, yy, label=label)
plt.xlabel("distance [m]")
matplotx.ylabel_top("voltage [V]") # move ylabel to the top, rotate
matplotx.line_labels() # line labels to the right
plt.show()
# plt.savefig("out.png", bbox_inches="tight")
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
Is there a way to get matplotlib to plot a perfect circle? They look more like ovals.
Just to expand on DSM's correct answer. By default, plots have more pixels along one axis over the other. When you add a circle, it's traditionally added in data units. If your axes have a symmetric range, that means one step along the x axis will involve a different number of pixels than one step along your y axis. So a symmetric circle in data units is asymmetric in your Pixel units (what you actually see).
As DSM correctly pointed out, you can force the x and y axes to have equal number of pixels per data unit. This is done using the plt.axis("equal") or ax.axis("equal") methods (where ax is an instance of an Axes).
You can also draw an Ellipse such that it is appropriately scaled to look like a circle on your plot. Here's an example of such a case:
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Circle
fig = plt.figure()
ax1 = fig.add_subplot(211)
# calculate asymmetry of x and y axes:
x0, y0 = ax1.transAxes.transform((0, 0)) # lower left in pixels
x1, y1 = ax1.transAxes.transform((1, 1)) # upper right in pixes
dx = x1 - x0
dy = y1 - y0
maxd = max(dx, dy)
width = .15 * maxd / dx
height = .15 * maxd / dy
# a circle you expect to be a circle, but it is not
ax1.add_artist(Circle((.5, .5), .15))
# an ellipse you expect to be an ellipse, but it's a circle
ax1.add_artist(Ellipse((.75, .75), width, height))
ax2 = fig.add_subplot(212)
ax2.axis('equal')
# a circle you expect to be a circle, and it is
ax2.add_artist(Circle((.5, .5), .15))
# an ellipse you expect to be an ellipse, and it is
ax2.add_artist(Ellipse((.75, .75), width, height))
fig.savefig('perfectCircle1.png')
resulting in this figure:
Alternatively, you can adjust your figure so that the Axes are square:
# calculate dimensions of axes 1 in figure units
x0, y0, dx, dy = ax1.get_position().bounds
maxd = max(dx, dy)
width = 6 * maxd / dx
height = 6 * maxd / dy
fig.set_size_inches((width, height))
fig.savefig('perfectCircle2.png')
resulting in:
Notice how the second axes, which has the axis("equal") option, now has the same range for the x and y axes. The figure has been scaled so that the date units of each are represented by the same number of pixels.
You can also adjust your axes to be square, even if the figure is not. Or you can change the default transform for the Circle to None, which means the units used are pixels. I'm having difficulty successfully doing this at the moment (the circle is a circle, but not where I want it to be).
I believe the simpler thing to do is adding the following:
ax.set_aspect('equal')
I've encounter the same problem today and I think I might have a more flexible solution. Two main problems remain with the previous answer (if you don't use the equal aspect function). First if you resize your whole graph, the proportion will not be the same since the number of pixels will change. Second point, this trick do not work if you don't have the same lim for the xaxis and the yaxis.
This solution tricks mpl using a custom object. Indeed, whenever you change one of your axis lim or your graph size, mpl will call an internal function which will take the width and height value of the ellipse multiplied by the transform function value. Since the width and height value is stored in the ellipse object, one way is to create a custom object with a value updated whenever the function is called, based on the current ax properties :
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
class GraphDist() :
def __init__(self, size, ax, x=True) :
self.size = size
self.ax = ax
self.x = x
#property
def dist_real(self) :
x0, y0 = self.ax.transAxes.transform((0, 0)) # lower left in pixels
x1, y1 = self.ax.transAxes.transform((1, 1)) # upper right in pixes
value = x1 - x0 if self.x else y1 - y0
return value
#property
def dist_abs(self) :
bounds = self.ax.get_xlim() if self.x else self.ax.get_ylim()
return bounds[0] - bounds[1]
#property
def value(self) :
return (self.size / self.dist_real) * self.dist_abs
def __mul__(self, obj) :
return self.value * obj
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim((0,10))
ax.set_ylim((0,5))
width = GraphDist(10, ax, True)
height = GraphDist(10, ax, False)
ax.add_artist(Ellipse((1, 3), width, height))
plt.show()