I am having an issue trying to get my date ticks rotated in matplotlib. A small sample program is below. If I try to rotate the ticks at the end, the ticks do not get rotated. If I try to rotate the ticks as shown under the comment ‘crashes’, then matplot lib crashes.

This only happens if the x-values are dates. If I replaces the variable dates with the variable t in the call to avail_plot, the xticks(rotation=70) call works just fine inside avail_plot.

Any ideas?

import numpy as np
import matplotlib.pyplot as plt
import datetime as dt

def avail_plot(ax, x, y, label, lcolor):
    ax.plot(x,y,'b')
    ax.set_ylabel(label, rotation='horizontal', color=lcolor)
    ax.get_yaxis().set_ticks([])

    #crashes
    #plt.xticks(rotation=70)

    ax2 = ax.twinx()
    ax2.plot(x, [1 for a in y], 'b')
    ax2.get_yaxis().set_ticks([])
    ax2.set_ylabel('testing')

f, axs = plt.subplots(2, sharex=True, sharey=True)
t = np.arange(0.01, 5, 1)
s1 = np.exp(t)
start = dt.datetime.now()
dates=[]
for val in t:
    next_val = start + dt.timedelta(0,val)
    dates.append(next_val)
    start = next_val

avail_plot(axs[0], dates, s1, 'testing', 'green')
avail_plot(axs[1], dates, s1, 'testing2', 'red')
plt.subplots_adjust(hspace=0, bottom=0.3)
plt.yticks([0.5,],("",""))
#doesn't crash, but does not rotate the xticks
#plt.xticks(rotation=70)
plt.show()

If you prefer a non-object-oriented approach, move plt.xticks(rotation=70) to right before the two avail_plot calls, eg

plt.xticks(rotation=70)
avail_plot(axs[0], dates, s1, 'testing', 'green')
avail_plot(axs[1], dates, s1, 'testing2', 'red')

This sets the rotation property before setting up the labels. Since you have two axes here, plt.xticks gets confused after you’ve made the two plots. At the point when plt.xticks doesn’t do anything, plt.gca() does not give you the axes you want to modify, and so plt.xticks, which acts on the current axes, is not going to work.

For an object-oriented approach not using plt.xticks, you can use

plt.setp( axs[1].xaxis.get_majorticklabels(), rotation=70 )

after the two avail_plot calls. This sets the rotation on the correct axes specifically.

Solution works for matplotlib 2.1+

There exists an axes method tick_params that can change tick properties. It also exists as an axis method as set_tick_params

ax.tick_params(axis='x', rotation=45)

Or

ax.xaxis.set_tick_params(rotation=45)

As a side note, the current solution mixes the stateful interface (using pyplot) with the object-oriented interface by using the command plt.xticks(rotation=70). Since the code in the question uses the object-oriented approach, it’s best to stick to that approach throughout. The solution does give a good explicit solution with plt.setp( axs[1].xaxis.get_majorticklabels(), rotation=70 )

An easy solution which avoids looping over the ticklabes is to just use

fig.autofmt_xdate()

This command automatically rotates the xaxis labels and adjusts their position. The default values are a rotation angle 30° and horizontal alignment “right”. But they can be changed in the function call

fig.autofmt_xdate(bottom=0.2, rotation=30, ha='right')

The additional bottom argument is equivalent to setting plt.subplots_adjust(bottom=bottom), which allows to set the bottom axes padding to a larger value to host the rotated ticklabels.

So basically here you have all the settings you need to have a nice date axis in a single command.

A good example can be found on the matplotlib page.

Another way to applyhorizontalalignment and rotation to each tick label is doing a for loop over the tick labels you want to change:

import numpy as np
import matplotlib.pyplot as plt
import datetime as dt

now = dt.datetime.now()
hours = [now + dt.timedelta(minutes=x) for x in range(0,24*60,10)]
days = [now + dt.timedelta(days=x) for x in np.arange(0,30,1/4.)]
hours_value = np.random.random(len(hours))
days_value = np.random.random(len(days))

fig, axs = plt.subplots(2)
fig.subplots_adjust(hspace=0.75)
axs[0].plot(hours,hours_value)
axs[1].plot(days,days_value)

for label in axs[0].get_xmajorticklabels() + axs[1].get_xmajorticklabels():
    label.set_rotation(30)
    label.set_horizontalalignment("right")

And here is an example if you want to control the location of major and minor ticks:

import numpy as np
import matplotlib.pyplot as plt
import datetime as dt

fig, axs = plt.subplots(2)
fig.subplots_adjust(hspace=0.75)
now = dt.datetime.now()
hours = [now + dt.timedelta(minutes=x) for x in range(0,24*60,10)]
days = [now + dt.timedelta(days=x) for x in np.arange(0,30,1/4.)]

axs[0].plot(hours,np.random.random(len(hours)))
x_major_lct = mpl.dates.AutoDateLocator(minticks=2,maxticks=10, interval_multiples=True)
x_minor_lct = matplotlib.dates.HourLocator(byhour = range(0,25,1))
x_fmt = matplotlib.dates.AutoDateFormatter(x_major_lct)
axs[0].xaxis.set_major_locator(x_major_lct)
axs[0].xaxis.set_minor_locator(x_minor_lct)
axs[0].xaxis.set_major_formatter(x_fmt)
axs[0].set_xlabel("minor ticks set to every hour, major ticks start with 00:00")

axs[1].plot(days,np.random.random(len(days)))
x_major_lct = mpl.dates.AutoDateLocator(minticks=2,maxticks=10, interval_multiples=True)
x_minor_lct = matplotlib.dates.DayLocator(bymonthday = range(0,32,1))
x_fmt = matplotlib.dates.AutoDateFormatter(x_major_lct)
axs[1].xaxis.set_major_locator(x_major_lct)
axs[1].xaxis.set_minor_locator(x_minor_lct)
axs[1].xaxis.set_major_formatter(x_fmt)
axs[1].set_xlabel("minor ticks set to every day, major ticks show first day of month")
for label in axs[0].get_xmajorticklabels() + axs[1].get_xmajorticklabels():
    label.set_rotation(30)
    label.set_horizontalalignment("right")

Simply use

ax.set_xticklabels(label_list, rotation=45)

I have used Matplotlib to plot lines on a figure. Now I would now like to set the style, specifically the marker, for individual points on the line. How do I do this?

To clarify my question, I want to be able to set the style for individual markers on a line, not every marker on said line.

Specify the keyword args linestyle and/or marker in your call to plot.

For example, using a dashed line and blue circle markers:

plt.plot(range(10), linestyle='--', marker='o', color='b')

A shortcut call for the same thing:

plt.plot(range(10), '--bo')

Here is a list of the possible line and marker styles:

================    ===============================
character           description
================    ===============================
   -                solid line style
   --               dashed line style
   -.               dash-dot line style
   :                dotted line style
   .                point marker
   ,                pixel marker
   o                circle marker
   v                triangle_down marker
   ^                triangle_up marker
   <                triangle_left marker
   >                triangle_right marker
   1                tri_down marker
   2                tri_up marker
   3                tri_left marker
   4                tri_right marker
   s                square marker
   p                pentagon marker
   *                star marker
   h                hexagon1 marker
   H                hexagon2 marker
   +                plus marker
   x                x marker
   D                diamond marker
   d                thin_diamond marker
   |                vline marker
   _                hline marker
================    ===============================

edit: with an example of marking an arbitrary subset of points, as requested in the comments:

import numpy as np
import matplotlib.pyplot as plt

xs = np.linspace(-np.pi, np.pi, 30)
ys = np.sin(xs)
markers_on = [12, 17, 18, 19]
plt.plot(xs, ys, '-gD', markevery=markers_on)
plt.show()

This last example using the markevery kwarg is possible in since 1.4+, due to the merge of this feature branch. If you are stuck on an older version of matplotlib, you can still achieve the result by overlaying a scatterplot on the line plot. See the edit history for more details.

There is a picture show all markers’ name and description, i hope it will help you.

import matplotlib.pylab as plt
markers=['.',',','o','v','^','<','>','1','2','3','4','8','s','p','P','*','h','H','+','x','X','D','d','|','_']
descriptions=['point', 'pixel', 'circle', 'triangle_down', 'triangle_up','triangle_left', 'triangle_right', 'tri_down', 'tri_up', 'tri_left','tri_right', 'octagon', 'square', 'pentagon', 'plus (filled)','star', 'hexagon1', 'hexagon2', 'plus', 'x', 'x (filled)','diamond', 'thin_diamond', 'vline', 'hline']
x=[]
y=[]
for i in range(5):
    for j in range(5):
        x.append(i)
        y.append(j)
plt.figure()
for i,j,m,l in zip(x,y,markers,descriptions):
    plt.scatter(i,j,marker=m)
    plt.text(i-0.15,j+0.15,s=m+' : '+l)
plt.axis([-0.1,4.8,-0.1,4.5])
plt.tight_layout()
plt.axis('off')
plt.show()  

For future reference – the Line2D artist returned by plot() also has a set_markevery() method which allows you to only set markers on certain points – see https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D.set_markevery

A simple trick to change a particular point marker shape, size… is to first plot it with all the other data then plot one more plot only with that point(or set of points if you want to change the style of multiple points). Suppose we want to change the marker shape of second point:

x = [1,2,3,4,5]
y = [2,1,3,6,7]

plt.plot(x, y, "-o")
x0 = [2]
y0 = [1]
plt.plot(x0, y0, "s")

plt.show()

Result is: Plot with multiple markers

I need to take an image and save it after some process. The figure looks fine when I display it, but after saving the figure, I got some white space around the saved image. I have tried the 'tight' option for savefig method, did not work either. The code:

  import matplotlib.image as mpimg
  import matplotlib.pyplot as plt

  fig = plt.figure(1)
  img = mpimg.imread(path)
  plt.imshow(img)
  ax=fig.add_subplot(1,1,1)

  extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
  plt.savefig('1.png', bbox_inches=extent)

  plt.axis('off') 
  plt.show()

I am trying to draw a basic graph by using NetworkX on a figure and save it. I realized that without graph it works, but when added a graph I get white space around the saved image;

import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import networkx as nx

G = nx.Graph()
G.add_node(1)
G.add_node(2)
G.add_node(3)
G.add_edge(1,3)
G.add_edge(1,2)
pos = {1:[100,120], 2:[200,300], 3:[50,75]}

fig = plt.figure(1)
img = mpimg.imread("C:\\images\\1.jpg")
plt.imshow(img)
ax=fig.add_subplot(1,1,1)

nx.draw(G, pos=pos)

extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
plt.savefig('1.png', bbox_inches = extent)

plt.axis('off') 
plt.show()

I cannot claim I know exactly why or how my “solution” works, but this is what I had to do when I wanted to plot the outline of a couple of aerofoil sections — without white margins — to a PDF file. (Note that I used matplotlib inside an IPython notebook, with the -pylab flag.)

plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, 
            hspace = 0, wspace = 0)
plt.margins(0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.savefig("filename.pdf", bbox_inches = 'tight',
    pad_inches = 0)

I have tried to deactivate different parts of this, but this always lead to a white margin somewhere. You may even have modify this to keep fat lines near the limits of the figure from being shaved by the lack of margins.

You can remove the white space padding by setting bbox_inches="tight" in savefig:

plt.savefig("test.png",bbox_inches='tight')

You’ll have to put the argument to bbox_inches as a string, perhaps this is why it didn’t work earlier for you.

Possible duplicates:

Matplotlib plots: removing axis, legends and white spaces

How to set the margins for a matplotlib figure?

Reduce left and right margins in matplotlib plot

After trying the above answers with no success (and a slew of other stack posts) what finally worked for me was just

plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, 
            hspace = 0, wspace = 0)
plt.margins(0,0)
plt.savefig("myfig.pdf")

Importantly this does not include the bbox or padding arguments.

I found something from Arvind Pereira (http://robotics.usc.edu/~ampereir/wordpress/?p=626) and seemed to work for me:

plt.savefig(filename, transparent = True, bbox_inches = 'tight', pad_inches = 0)

The following function incorporates johannes-s answer above. I have tested it with plt.figure and plt.subplots() with multiple axes, and it works nicely.

def save(filepath, fig=None):
    '''Save the current image with no whitespace
    Example filepath: "myfig.png" or r"C:\myfig.pdf" 
    '''
    import matplotlib.pyplot as plt
    if not fig:
        fig = plt.gcf()

    plt.subplots_adjust(0,0,1,1,0,0)
    for ax in fig.axes:
        ax.axis('off')
        ax.margins(0,0)
        ax.xaxis.set_major_locator(plt.NullLocator())
        ax.yaxis.set_major_locator(plt.NullLocator())
    fig.savefig(filepath, pad_inches = 0, bbox_inches='tight')

I found the following codes work perfectly for the job.

fig = plt.figure(figsize=[6,6])
ax = fig.add_subplot(111)
ax.imshow(data)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
ax.set_frame_on(False)
plt.savefig('data.png', dpi=400, bbox_inches='tight',pad_inches=0)

i followed this sequence and it worked like a charm.

plt.axis("off")
fig=plt.imshow(image array,interpolation='nearest')
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.savefig('destination_path.pdf',
    bbox_inches='tight', pad_inches=0, format='pdf', dpi=1200)

For anyone who wants to work in pixels rather than inches this will work.

Plus the usual you will also need

from matplotlib.transforms import Bbox

Then you can use the following:

my_dpi = 100 # Good default - doesn't really matter

# Size of output in pixels
h = 224
w = 224

fig, ax = plt.subplots(1, figsize=(w/my_dpi, h/my_dpi), dpi=my_dpi)

ax.set_position([0, 0, 1, 1]) # Critical!

# Do some stuff
ax.imshow(img)
ax.imshow(heatmap) # 4-channel RGBA
ax.plot([50, 100, 150], [50, 100, 150], color="red")

ax.axis("off")

fig.savefig("saved_img.png",
            bbox_inches=Bbox([[0, 0], [w/my_dpi, h/my_dpi]]),
            dpi=my_dpi)

A much simpler approach I found is to use plt.imsave :

    import matplotlib.pyplot as plt
    arr = plt.imread(path)
    plt.imsave('test.png', arr)

You may try this. It solved my issue.

import matplotlib.image as mpimg
img = mpimg.imread("src.png")
mpimg.imsave("out.png", img, cmap=cmap)

The most straightforward method is to use plt.tight_layout transformation which is actually more preferable as it doesn’t do unnecessary cropping when using plt.savefig

import matplotlib as plt    
plt.plot([1,2,3], [1,2,3])
plt.tight_layout(pad=0)
plt.savefig('plot.png')

However, this may not be preferable for complex plots that modifies the figure. Refer to top answers that uses plt.subplots_adjust if that’s the case.

This works for me saving a numpy array plotted with imshow to file

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(10,10))
plt.imshow(img) # your image here
plt.axis("off")
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, 
        hspace = 0, wspace = 0)
plt.savefig("example2.png", box_inches='tight', dpi=100)
plt.show()