Related
Instead of words or numbers being the tick labels of the x axis, I want to draw a simple drawing (made of lines and circles) as the label for each x tick. Is this possible? If so, what is the best way to go about it in matplotlib?
I would remove the tick labels and replace the text with patches. Here is a brief example of performing this task:
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# define where to put symbols vertically
TICKYPOS = -.6
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(range(10))
# set ticks where your images will be
ax.get_xaxis().set_ticks([2,4,6,8])
# remove tick labels
ax.get_xaxis().set_ticklabels([])
# add a series of patches to serve as tick labels
ax.add_patch(patches.Circle((2,TICKYPOS),radius=.2,
fill=True,clip_on=False))
ax.add_patch(patches.Circle((4,TICKYPOS),radius=.2,
fill=False,clip_on=False))
ax.add_patch(patches.Rectangle((6-.1,TICKYPOS-.05),.2,.2,
fill=True,clip_on=False))
ax.add_patch(patches.Rectangle((8-.1,TICKYPOS-.05),.2,.2,
fill=False,clip_on=False))
This results in the following figure:
It is key to set clip_on to False, otherwise patches outside the axes will not be shown. The coordinates and sizes (radius, width, height, etc.) of the patches will depend on where your axes is in the figure. For example, if you are considering doing this with subplots, you will need to be sensitive of the patches placement so as to not overlap any other axes. It may be worth your time investigating Transformations, and defining the positions and sizes in an other unit (Axes, Figure or display).
If you have specific image files that you want to use for the symbols, you can use the BboxImage class to create artists to be added to the axes instead of patches. For example I made a simple icon with the following script:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(1,1),dpi=400)
ax = fig.add_axes([0,0,1,1],frameon=False)
ax.set_axis_off()
ax.plot(range(10),linewidth=32)
ax.plot(range(9,-1,-1),linewidth=32)
fig.savefig('thumb.png')
producing this image:
Then I created a BboxImage at the location I want the tick label and of the size I want:
lowerCorner = ax.transData.transform((.8,TICKYPOS-.2))
upperCorner = ax.transData.transform((1.2,TICKYPOS+.2))
bbox_image = BboxImage(Bbox([lowerCorner[0],
lowerCorner[1],
upperCorner[0],
upperCorner[1],
]),
norm = None,
origin=None,
clip_on=False,
)
Noticed how I used the transData transformation to convert from data units to display units, which are required in the definition of the Bbox.
Now I read in the image using the imread routine, and set it's results (a numpy array) to the data of bbox_image and add the artist to the axes:
bbox_image.set_data(imread('thumb.png'))
ax.add_artist(bbox_image)
This results in an updated figure:
If you do directly use images, make sure to import the required classes and methods:
from matplotlib.image import BboxImage,imread
from matplotlib.transforms import Bbox
The other answer has some drawbacks because it uses static coordinates. It will hence not work when changing the figure size or zooming and panning the plot.
A better option is to directly define the positions in the coordinate systems of choice. For the xaxis it makes sense to use data coordinates for the x position and axes coordinates for y position.
Using matplotlib.offsetboxes makes this rather simple. The following would position a box with a circle and a box with an image at coordinates (-5,0) and (5,0) respectively and offsets them a bit to the lower such that they'll look as if they were ticklabels.
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.offsetbox import (DrawingArea, OffsetImage,AnnotationBbox)
fig, ax = plt.subplots()
ax.plot([-10,10], [1,3])
# Annotate the 1st position with a circle patch
da = DrawingArea(20, 20, 10, 10)
p = mpatches.Circle((0, 0), 10)
da.add_artist(p)
ab = AnnotationBbox(da, (-5,0),
xybox=(0, -7),
xycoords=("data", "axes fraction"),
box_alignment=(.5, 1),
boxcoords="offset points",
bboxprops={"edgecolor" : "none"})
ax.add_artist(ab)
# Annotate the 2nd position with an image
arr_img = plt.imread("https://i.stack.imgur.com/FmX9n.png", format='png')
imagebox = OffsetImage(arr_img, zoom=0.2)
imagebox.image.axes = ax
ab = AnnotationBbox(imagebox, (5,0),
xybox=(0, -7),
xycoords=("data", "axes fraction"),
boxcoords="offset points",
box_alignment=(.5, 1),
bboxprops={"edgecolor" : "none"})
ax.add_artist(ab)
plt.show()
Note that many shapes exist as unicode symbols, such that one can simply set the ticklabels with those symbols. For such a solution, see How to use a colored shape as yticks in matplotlib or seaborn?
Instead of words or numbers being the tick labels of the x axis, I want to draw a simple drawing (made of lines and circles) as the label for each x tick. Is this possible? If so, what is the best way to go about it in matplotlib?
I would remove the tick labels and replace the text with patches. Here is a brief example of performing this task:
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# define where to put symbols vertically
TICKYPOS = -.6
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(range(10))
# set ticks where your images will be
ax.get_xaxis().set_ticks([2,4,6,8])
# remove tick labels
ax.get_xaxis().set_ticklabels([])
# add a series of patches to serve as tick labels
ax.add_patch(patches.Circle((2,TICKYPOS),radius=.2,
fill=True,clip_on=False))
ax.add_patch(patches.Circle((4,TICKYPOS),radius=.2,
fill=False,clip_on=False))
ax.add_patch(patches.Rectangle((6-.1,TICKYPOS-.05),.2,.2,
fill=True,clip_on=False))
ax.add_patch(patches.Rectangle((8-.1,TICKYPOS-.05),.2,.2,
fill=False,clip_on=False))
This results in the following figure:
It is key to set clip_on to False, otherwise patches outside the axes will not be shown. The coordinates and sizes (radius, width, height, etc.) of the patches will depend on where your axes is in the figure. For example, if you are considering doing this with subplots, you will need to be sensitive of the patches placement so as to not overlap any other axes. It may be worth your time investigating Transformations, and defining the positions and sizes in an other unit (Axes, Figure or display).
If you have specific image files that you want to use for the symbols, you can use the BboxImage class to create artists to be added to the axes instead of patches. For example I made a simple icon with the following script:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(1,1),dpi=400)
ax = fig.add_axes([0,0,1,1],frameon=False)
ax.set_axis_off()
ax.plot(range(10),linewidth=32)
ax.plot(range(9,-1,-1),linewidth=32)
fig.savefig('thumb.png')
producing this image:
Then I created a BboxImage at the location I want the tick label and of the size I want:
lowerCorner = ax.transData.transform((.8,TICKYPOS-.2))
upperCorner = ax.transData.transform((1.2,TICKYPOS+.2))
bbox_image = BboxImage(Bbox([lowerCorner[0],
lowerCorner[1],
upperCorner[0],
upperCorner[1],
]),
norm = None,
origin=None,
clip_on=False,
)
Noticed how I used the transData transformation to convert from data units to display units, which are required in the definition of the Bbox.
Now I read in the image using the imread routine, and set it's results (a numpy array) to the data of bbox_image and add the artist to the axes:
bbox_image.set_data(imread('thumb.png'))
ax.add_artist(bbox_image)
This results in an updated figure:
If you do directly use images, make sure to import the required classes and methods:
from matplotlib.image import BboxImage,imread
from matplotlib.transforms import Bbox
The other answer has some drawbacks because it uses static coordinates. It will hence not work when changing the figure size or zooming and panning the plot.
A better option is to directly define the positions in the coordinate systems of choice. For the xaxis it makes sense to use data coordinates for the x position and axes coordinates for y position.
Using matplotlib.offsetboxes makes this rather simple. The following would position a box with a circle and a box with an image at coordinates (-5,0) and (5,0) respectively and offsets them a bit to the lower such that they'll look as if they were ticklabels.
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.offsetbox import (DrawingArea, OffsetImage,AnnotationBbox)
fig, ax = plt.subplots()
ax.plot([-10,10], [1,3])
# Annotate the 1st position with a circle patch
da = DrawingArea(20, 20, 10, 10)
p = mpatches.Circle((0, 0), 10)
da.add_artist(p)
ab = AnnotationBbox(da, (-5,0),
xybox=(0, -7),
xycoords=("data", "axes fraction"),
box_alignment=(.5, 1),
boxcoords="offset points",
bboxprops={"edgecolor" : "none"})
ax.add_artist(ab)
# Annotate the 2nd position with an image
arr_img = plt.imread("https://i.stack.imgur.com/FmX9n.png", format='png')
imagebox = OffsetImage(arr_img, zoom=0.2)
imagebox.image.axes = ax
ab = AnnotationBbox(imagebox, (5,0),
xybox=(0, -7),
xycoords=("data", "axes fraction"),
boxcoords="offset points",
box_alignment=(.5, 1),
bboxprops={"edgecolor" : "none"})
ax.add_artist(ab)
plt.show()
Note that many shapes exist as unicode symbols, such that one can simply set the ticklabels with those symbols. For such a solution, see How to use a colored shape as yticks in matplotlib or seaborn?
I am plotting a pie chart making background in the png image looks transparent. How can I make the center circle also looks transparent instead of the white color?
import matplotlib.pyplot as plt
# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = 'Correct', 'Wrong'
sizes = [20, 80]
fig1, ax1 = plt.subplots()
ax1.pie(sizes,colors=['green','red'], labels=labels,autopct='%1.1f%%',
shadow=True, startangle=90)
centre_circle = plt.Circle((0,0),0.75,edgecolor='black',
facecolor='white',fill=True,linewidth=0.25)
fig1 = plt.gcf()
fig1.gca().add_artist(centre_circle)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
fig1.savefig('foo.png', transparent=True)
The way you create the white middle part in the above code is by obfuscating the center of the pie by a circle. This can of course not procude a transparent interior.
A solution to this would also be found in the more sophisticated question Double donut chart in matplotlib. Let me go into detail:
In order to produce a true donut chart with a hole in the middle, one would need to cut the wedges such that they become partial rings. Fortunately, matplotlib provides the tools to do so. A pie chart consists of several wedges.
From the
matplotlib.patches.Wedge documentation we learn
class matplotlib.patches.Wedge(center, r, theta1, theta2, width=None, **kwargs)
Wedge shaped patch.
[...] If width is given, then a partial wedge is drawn from inner radius r - width to outer radius r.
In order to give set the width to all wedges, an easy method is to use plt.setp
wedges, _ = ax.pie([20,80], ...)
plt.setp( wedges, width=0.25)
Complete example:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
fig.set_facecolor("#fff9c9") # set yellow background color to see effect
wedges, text, autotext = ax.pie([25, 40], colors=['limegreen','crimson'],
labels=['Correct', 'Wrong'], autopct='%1.1f%%')
plt.setp( wedges, width=0.25)
ax.set_aspect("equal")
# the produced png will have a transparent background
plt.savefig(__file__+".png", transparent=True)
plt.show()
The following would be a way to tackle the problem if the Wedge did not have a width argument.
Since the pie chart is centered at (0,0), copying the outer path coordinates, reverting them and multiplying by some number smaller 1 (called r for radius in below code), gives the coordinates of the inner ring. Joining those two list of coordinates and taking care of the proper path codes allows to create a ring shape as desired.
import matplotlib.pyplot as plt
import matplotlib.path as mpath
import matplotlib.patches as mpatches
import numpy as np
def cutwedge(wedge, r=0.8):
path = wedge.get_path()
verts = path.vertices[:-3]
codes = path.codes[:-3]
new_verts = np.vstack((verts , verts[::-1]*r, verts[0,:]))
new_codes = np.concatenate((codes , codes[::-1], np.array([79])) )
new_codes[len(codes)] = 2
new_path = mpath.Path(new_verts, new_codes)
new_patch = mpatches.PathPatch(new_path)
new_patch.update_from(wedge)
wedge.set_visible(False)
wedge.axes.add_patch(new_patch)
return new_patch
fig, ax = plt.subplots()
fig.set_facecolor("#fff9c9") # set yellow background color to see effect
wedges, text, autotext = ax.pie([25, 75], colors=['limegreen','indigo'],
labels=['Correct', 'Wrong'], autopct='%1.1f%%')
for w in wedges:
cutwedge(w)
# or try cutwedge(w, r=0.4)
ax.set_aspect("equal")
# the produced png will have a transparent background
plt.savefig(__file__+".png", transparent=True)
plt.show()
The problem is that you didnt really make a real donut chart. With this part of the code
centre_circle = plt.Circle((0,0),0.75,edgecolor='black',
facecolor='white',fill=True,linewidth=0.25)
you drew a circle in the middle of a pie chart. The problem is if you make this circle transparent you will once again see the middle of the pie chart. I recommend using a free photo editing program like pixlr to just make it transparent. Unless you can find a way to make a true donut chart which I unfortunantly do not know how to do it.
Similarly to the Double donut chart in matplotlib solution referenced by importanceofbeingearnest, you will need to use plt.setp(pie, width=width) to set the width of your pie chart, which will make it a true donut instead of a pie chart with a solid circle drawn on top.
import matplotlib.pyplot as plt
fig1, ax1 = plt.subplots()
ax1.axis('equal')
# Set the width of the pie slices;
# this is equivalent to (1.0-0.75), or
# (the radius of the pie chart - the radius of the inner circle)
width=0.25
# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = ['Correct', 'Wrong']
sizes = [20., 80.]
# ax1.pie will return three values:
# 1. pie (the dimensions of each wedge of the pie),
# 2. labtext (the coordinates and text for the labels)
# 3. labpct (the coordinates and text of the "XX.X%"" labels)
pie, labtext, labpct = ax1.pie(x=sizes,
labels=labels,
colors=['green','red'],
startangle=90,
shadow=True,
autopct='%1.1f%%'
)
# apply "plt.setp" to set width property
plt.setp(pie, width=width)
# save the figure as transparent
fig1.savefig('foo.png', transparent=True)
I would like to utilize customer markers in both scatter and line charts. How can I make custom marker out of a PNG file?
I don't believe matplotlib can customize markers like that. See here for the level of customization, which falls way short of what you need.
As an alternative, I've coded up this kludge which uses matplotlib.image to place images at the line point locations.
import matplotlib.pyplot as plt
from matplotlib import image
# constant
dpi = 72
path = 'smile.png'
# read in our png file
im = image.imread(path)
image_size = im.shape[1], im.shape[0]
fig = plt.figure(dpi=dpi)
ax = fig.add_subplot(111)
# plot our line with transparent markers, and markersize the size of our image
line, = ax.plot((1,2,3,4),(1,2,3,4),"bo",mfc="None",mec="None",markersize=image_size[0] * (dpi/ 96))
# we need to make the frame transparent so the image can be seen
# only in trunk can you put the image on top of the plot, see this link:
# http://www.mail-archive.com/matplotlib-users#lists.sourceforge.net/msg14534.html
ax.patch.set_alpha(0)
ax.set_xlim((0,5))
ax.set_ylim((0,5))
# translate point positions to pixel positions
# figimage needs pixels not points
line._transform_path()
path, affine = line._transformed_path.get_transformed_points_and_affine()
path = affine.transform_path(path)
for pixelPoint in path.vertices:
# place image at point, centering it
fig.figimage(im,pixelPoint[0]-image_size[0]/2,pixelPoint[1]-image_size[1]/2,origin="upper")
plt.show()
Produces:
Following on from Mark's answer. I just thought I would add to this a bit because I tried to run this and it does what I want with the exception of actually displaying the icons on the graph. Maybe something has changed with matplotlib. It has been 4 years.
The line of code that reads:
ax.get_frame().set_alpha(0)
does not seem to work, however
ax.patch.set_alpha(0)
does work.
The other answer may lead to problems when resizing the figure. Here is a different approach, positionning the images inside annotation boxes, which are anchored in data coordinates.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
path = "https://upload.wikimedia.org/wikipedia/commons/b/b5/Tango-example_icons.png"
image = plt.imread(path)[116:116+30, 236:236+30]
x = np.arange(10)
y = np.random.rand(10)
fig, ax = plt.subplots()
ax.plot(x,y)
def plot_images(x, y, image, ax=None):
ax = ax or plt.gca()
for xi, yi in zip(x,y):
im = OffsetImage(image, zoom=72/ax.figure.dpi)
im.image.axes = ax
ab = AnnotationBbox(im, (xi,yi), frameon=False, pad=0.0,)
ax.add_artist(ab)
plot_images(x, y, image, ax=ax)
plt.show()
Instead of words or numbers being the tick labels of the x axis, I want to draw a simple drawing (made of lines and circles) as the label for each x tick. Is this possible? If so, what is the best way to go about it in matplotlib?
I would remove the tick labels and replace the text with patches. Here is a brief example of performing this task:
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# define where to put symbols vertically
TICKYPOS = -.6
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(range(10))
# set ticks where your images will be
ax.get_xaxis().set_ticks([2,4,6,8])
# remove tick labels
ax.get_xaxis().set_ticklabels([])
# add a series of patches to serve as tick labels
ax.add_patch(patches.Circle((2,TICKYPOS),radius=.2,
fill=True,clip_on=False))
ax.add_patch(patches.Circle((4,TICKYPOS),radius=.2,
fill=False,clip_on=False))
ax.add_patch(patches.Rectangle((6-.1,TICKYPOS-.05),.2,.2,
fill=True,clip_on=False))
ax.add_patch(patches.Rectangle((8-.1,TICKYPOS-.05),.2,.2,
fill=False,clip_on=False))
This results in the following figure:
It is key to set clip_on to False, otherwise patches outside the axes will not be shown. The coordinates and sizes (radius, width, height, etc.) of the patches will depend on where your axes is in the figure. For example, if you are considering doing this with subplots, you will need to be sensitive of the patches placement so as to not overlap any other axes. It may be worth your time investigating Transformations, and defining the positions and sizes in an other unit (Axes, Figure or display).
If you have specific image files that you want to use for the symbols, you can use the BboxImage class to create artists to be added to the axes instead of patches. For example I made a simple icon with the following script:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(1,1),dpi=400)
ax = fig.add_axes([0,0,1,1],frameon=False)
ax.set_axis_off()
ax.plot(range(10),linewidth=32)
ax.plot(range(9,-1,-1),linewidth=32)
fig.savefig('thumb.png')
producing this image:
Then I created a BboxImage at the location I want the tick label and of the size I want:
lowerCorner = ax.transData.transform((.8,TICKYPOS-.2))
upperCorner = ax.transData.transform((1.2,TICKYPOS+.2))
bbox_image = BboxImage(Bbox([lowerCorner[0],
lowerCorner[1],
upperCorner[0],
upperCorner[1],
]),
norm = None,
origin=None,
clip_on=False,
)
Noticed how I used the transData transformation to convert from data units to display units, which are required in the definition of the Bbox.
Now I read in the image using the imread routine, and set it's results (a numpy array) to the data of bbox_image and add the artist to the axes:
bbox_image.set_data(imread('thumb.png'))
ax.add_artist(bbox_image)
This results in an updated figure:
If you do directly use images, make sure to import the required classes and methods:
from matplotlib.image import BboxImage,imread
from matplotlib.transforms import Bbox
The other answer has some drawbacks because it uses static coordinates. It will hence not work when changing the figure size or zooming and panning the plot.
A better option is to directly define the positions in the coordinate systems of choice. For the xaxis it makes sense to use data coordinates for the x position and axes coordinates for y position.
Using matplotlib.offsetboxes makes this rather simple. The following would position a box with a circle and a box with an image at coordinates (-5,0) and (5,0) respectively and offsets them a bit to the lower such that they'll look as if they were ticklabels.
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.offsetbox import (DrawingArea, OffsetImage,AnnotationBbox)
fig, ax = plt.subplots()
ax.plot([-10,10], [1,3])
# Annotate the 1st position with a circle patch
da = DrawingArea(20, 20, 10, 10)
p = mpatches.Circle((0, 0), 10)
da.add_artist(p)
ab = AnnotationBbox(da, (-5,0),
xybox=(0, -7),
xycoords=("data", "axes fraction"),
box_alignment=(.5, 1),
boxcoords="offset points",
bboxprops={"edgecolor" : "none"})
ax.add_artist(ab)
# Annotate the 2nd position with an image
arr_img = plt.imread("https://i.stack.imgur.com/FmX9n.png", format='png')
imagebox = OffsetImage(arr_img, zoom=0.2)
imagebox.image.axes = ax
ab = AnnotationBbox(imagebox, (5,0),
xybox=(0, -7),
xycoords=("data", "axes fraction"),
boxcoords="offset points",
box_alignment=(.5, 1),
bboxprops={"edgecolor" : "none"})
ax.add_artist(ab)
plt.show()
Note that many shapes exist as unicode symbols, such that one can simply set the ticklabels with those symbols. For such a solution, see How to use a colored shape as yticks in matplotlib or seaborn?