Related
I have a Python program that generates graphs using matplotlib. I am trying to get the program to generate a bunch of plots in one program run (the user is asked if they want to generate another graph) all in separate windows. Any way I can do this?
To generate a new figure, you can add plt.figure() before any plotting that your program does.
import matplotlib.pyplot as plt
import numpy as np
def make_plot(slope):
x = np.arange(1,10)
y = slope*x+3
plt.figure()
plt.plot(x,y)
make_plot(2)
make_plot(3)
Using the latest matlibplot, I found the following to work for my purposes:
# create figure (will only create new window if needed)
plt.figure()
# Generate plot1
plt.plot(range(10, 20))
# Show the plot in non-blocking mode
plt.show(block=False)
# create figure (will only create new window if needed)
plt.figure()
# Generate plot2
plt.plot(range(10, 20))
# Show the plot in non-blocking mode
plt.show(block=False)
...
# Finally block main thread until all plots are closed
plt.show()
The easiest way to ensure all of your lines go to the correct figure window is something like:
from six.moves import input
import matplotlib.pyplot as plt
another = True
while another:
fig, ax = plt.subplots()
ax.plot(range(5))
fig.canvas.manager.show()
# this makes sure that the gui window gets shown
# if this is needed depends on rcparams, this is just to be safe
fig.canvas.flush_events()
# this make sure that if the event loop integration is not
# set up by the gui framework the plot will update
another = bool(input("would you like another? "))
If you want to run this with a non-gui backend you will need to drop the flush_events call or wrap it in a try: ... except NotImplementedError. Much of this complication is defensive programming because GUIs can be difficult and the behavior of this code may be dependent on many factors which are not obvious from the code shown.
Using the implicit axes of pyplot can cause problems as the 'current axes' is set by the last axes the user clicked on. You should really only use pyplot when interactively typing at the rpel and almost never (other than plt.subplots) in scripts/programs.
Use the .figure() function to create a new window, the following code makes two windows:
import matplotlib.pyplot as plt
plt.plot(range(10)) # Creates the plot. No need to save the current figure.
plt.draw() # Draws, but does not block
plt.figure() # New window, if needed. No need to save it, as pyplot uses the concept of current figure
plt.plot(range(10, 20))
plt.draw()
You can repeat this as many times as you want
This displays the figure in a GUI:
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [1, 4, 9])
plt.show()
But how do I instead save the figure to a file (e.g. foo.png)?
When using matplotlib.pyplot.savefig, the file format can be specified by the extension:
from matplotlib import pyplot as plt
plt.savefig('foo.png')
plt.savefig('foo.pdf')
That gives a rasterized or vectorized output respectively.
In addition, there is sometimes undesirable whitespace around the image, which can be removed with:
plt.savefig('foo.png', bbox_inches='tight')
Note that if showing the plot, plt.show() should follow plt.savefig(); otherwise, the file image will be blank.
As others have said, plt.savefig() or fig1.savefig() is indeed the way to save an image.
However I've found that in certain cases the figure is always shown. (eg. with Spyder having plt.ion(): interactive mode = On.) I work around this by
forcing the the figure window to close with:
plt.close(figure_object)
(see documentation). This way I don't have a million open figures during a large loop. Example usage:
import matplotlib.pyplot as plt
fig, ax = plt.subplots( nrows=1, ncols=1 ) # create figure & 1 axis
ax.plot([0,1,2], [10,20,3])
fig.savefig('path/to/save/image/to.png') # save the figure to file
plt.close(fig) # close the figure window
You should be able to re-open the figure later if needed to with fig.show() (didn't test myself).
The solution is:
pylab.savefig('foo.png')
Just found this link on the MatPlotLib documentation addressing exactly this issue:
http://matplotlib.org/faq/howto_faq.html#generate-images-without-having-a-window-appear
They say that the easiest way to prevent the figure from popping up is to use a non-interactive backend (eg. Agg), via matplotib.use(<backend>), eg:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.plot([1,2,3])
plt.savefig('myfig')
I still personally prefer using plt.close( fig ), since then you have the option to hide certain figures (during a loop), but still display figures for post-loop data processing. It is probably slower than choosing a non-interactive backend though - would be interesting if someone tested that.
UPDATE: for Spyder, you usually can't set the backend in this way (Because Spyder usually loads matplotlib early, preventing you from using matplotlib.use()).
Instead, use plt.switch_backend('Agg'), or Turn off "enable support" in the Spyder prefs and run the matplotlib.use('Agg') command yourself.
From these two hints: one, two
If you don't like the concept of the "current" figure, do:
import matplotlib.image as mpimg
img = mpimg.imread("src.png")
mpimg.imsave("out.png", img)
import datetime
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
# Create the PdfPages object to which we will save the pages:
# The with statement makes sure that the PdfPages object is closed properly at
# the end of the block, even if an Exception occurs.
with PdfPages('multipage_pdf.pdf') as pdf:
plt.figure(figsize=(3, 3))
plt.plot(range(7), [3, 1, 4, 1, 5, 9, 2], 'r-o')
plt.title('Page One')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
plt.rc('text', usetex=True)
plt.figure(figsize=(8, 6))
x = np.arange(0, 5, 0.1)
plt.plot(x, np.sin(x), 'b-')
plt.title('Page Two')
pdf.savefig()
plt.close()
plt.rc('text', usetex=False)
fig = plt.figure(figsize=(4, 5))
plt.plot(x, x*x, 'ko')
plt.title('Page Three')
pdf.savefig(fig) # or you can pass a Figure object to pdf.savefig
plt.close()
# We can also set the file's metadata via the PdfPages object:
d = pdf.infodict()
d['Title'] = 'Multipage PDF Example'
d['Author'] = u'Jouni K. Sepp\xe4nen'
d['Subject'] = 'How to create a multipage pdf file and set its metadata'
d['Keywords'] = 'PdfPages multipage keywords author title subject'
d['CreationDate'] = datetime.datetime(2009, 11, 13)
d['ModDate'] = datetime.datetime.today()
I used the following:
import matplotlib.pyplot as plt
p1 = plt.plot(dates, temp, 'r-', label="Temperature (celsius)")
p2 = plt.plot(dates, psal, 'b-', label="Salinity (psu)")
plt.legend(loc='upper center', numpoints=1, bbox_to_anchor=(0.5, -0.05), ncol=2, fancybox=True, shadow=True)
plt.savefig('data.png')
plt.show()
plt.close()
I found very important to use plt.show after saving the figure, otherwise it won't work.figure exported in png
The other answers are correct. However, I sometimes find that I want to open the figure object later. For example, I might want to change the label sizes, add a grid, or do other processing. In a perfect world, I would simply rerun the code generating the plot, and adapt the settings. Alas, the world is not perfect. Therefore, in addition to saving to PDF or PNG, I add:
with open('some_file.pkl', "wb") as fp:
pickle.dump(fig, fp, protocol=4)
Like this, I can later load the figure object and manipulate the settings as I please.
I also write out the stack with the source-code and locals() dictionary for each function/method in the stack, so that I can later tell exactly what generated the figure.
NB: Be careful, as sometimes this method generates huge files.
After using the plot() and other functions to create the content you want, you could use a clause like this to select between plotting to the screen or to file:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(4, 5)) # size in inches
# use plot(), etc. to create your plot.
# Pick one of the following lines to uncomment
# save_file = None
# save_file = os.path.join(your_directory, your_file_name)
if save_file:
plt.savefig(save_file)
plt.close(fig)
else:
plt.show()
If, like me, you use Spyder IDE, you have to disable the interactive mode with :
plt.ioff()
(this command is automatically launched with the scientific startup)
If you want to enable it again, use :
plt.ion()
You can either do:
plt.show(hold=False)
plt.savefig('name.pdf')
and remember to let savefig finish before closing the GUI plot. This way you can see the image beforehand.
Alternatively, you can look at it with plt.show()
Then close the GUI and run the script again, but this time replace plt.show() with plt.savefig().
Alternatively, you can use
fig, ax = plt.figure(nrows=1, ncols=1)
plt.plot(...)
plt.show()
fig.savefig('out.pdf')
According to question Matplotlib (pyplot) savefig outputs blank image.
One thing should note: if you use plt.show and it should after plt.savefig, or you will give a blank image.
A detailed example:
import numpy as np
import matplotlib.pyplot as plt
def draw_result(lst_iter, lst_loss, lst_acc, title):
plt.plot(lst_iter, lst_loss, '-b', label='loss')
plt.plot(lst_iter, lst_acc, '-r', label='accuracy')
plt.xlabel("n iteration")
plt.legend(loc='upper left')
plt.title(title)
plt.savefig(title+".png") # should before plt.show method
plt.show()
def test_draw():
lst_iter = range(100)
lst_loss = [0.01 * i + 0.01 * i ** 2 for i in xrange(100)]
# lst_loss = np.random.randn(1, 100).reshape((100, ))
lst_acc = [0.01 * i - 0.01 * i ** 2 for i in xrange(100)]
# lst_acc = np.random.randn(1, 100).reshape((100, ))
draw_result(lst_iter, lst_loss, lst_acc, "sgd_method")
if __name__ == '__main__':
test_draw()
The Solution :
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
plt.figure()
ts.plot()
plt.savefig("foo.png", bbox_inches='tight')
If you do want to display the image as well as saving the image use:
%matplotlib inline
after
import matplotlib
When using matplotlib.pyplot, you must first save your plot and then close it using these 2 lines:
fig.savefig('plot.png') # save the plot, place the path you want to save the figure in quotation
plt.close(fig) # close the figure window
import matplotlib.pyplot as plt
plt.savefig("image.png")
In Jupyter Notebook you have to remove plt.show() and add plt.savefig(), together with the rest of the plt-code in one cell.
The image will still show up in your notebook.
Additionally to those above, I added __file__ for the name so the picture and Python file get the same names. I also added few arguments to make It look better:
# Saves a PNG file of the current graph to the folder and updates it every time
# (nameOfimage, dpi=(sizeOfimage),Keeps_Labels_From_Disappearing)
plt.savefig(__file__+".png",dpi=(250), bbox_inches='tight')
# Hard coded name: './test.png'
Just a extra note because I can't comment on posts yet.
If you are using plt.savefig('myfig') or something along these lines make sure to add a plt.clf() after your image is saved. This is because savefig does not close the plot and if you add to the plot after without a plt.clf() you'll be adding to the previous plot.
You may not notice if your plots are similar as it will plot over the previous plot, but if you are in a loop saving your figures the plot will slowly become massive and make your script very slow.
Given that today (was not available when this question was made) lots of people use Jupyter Notebook as python console, there is an extremely easy way to save the plots as .png, just call the matplotlib's pylab class from Jupyter Notebook, plot the figure 'inline' jupyter cells, and then drag that figure/image to a local directory. Don't forget
%matplotlib inline in the first line!
As suggested before, you can either use:
import matplotlib.pyplot as plt
plt.savefig("myfig.png")
For saving whatever IPhython image that you are displaying. Or on a different note (looking from a different angle), if you ever get to work with open cv, or if you have open cv imported, you can go for:
import cv2
cv2.imwrite("myfig.png",image)
But this is just in case if you need to work with Open CV. Otherwise plt.savefig() should be sufficient.
well, I do recommend using wrappers to render or control the plotting. examples can be mpltex (https://github.com/liuyxpp/mpltex) or prettyplotlib (https://github.com/olgabot/prettyplotlib).
import mpltex
#mpltex.acs_decorator
def myplot():
plt.figure()
plt.plot(x,y,'b-',lable='xxx')
plt.tight_layout(pad=0.5)
plt.savefig('xxxx') # the figure format was controlled by the decorator, it can be either eps, or pdf or png....
plt.close()
I basically use this decorator a lot for publishing academic papers in various journals at American Chemical Society, American Physics Society, Opticcal Society American, Elsivier and so on.
An example can be found as following image (https://github.com/MarkMa1990/gradientDescent):
You can do it like this:
def plotAFig():
plt.figure()
plt.plot(x,y,'b-')
plt.savefig("figurename.png")
plt.close()
Nothing was working for me. The problem is that the saved imaged was very small and I could not find how the hell make it bigger.
This seems to make it bigger, but still not full screen.
https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.set_size_inches
fig.set_size_inches((w, h))
Hope that helps somebody.
You can save your image with any extension(png, jpg,etc.) and with the resolution you want. Here's a function to save your figure.
import os
def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format=fig_extension, dpi=resolution)
'fig_id' is the name by which you want to save your figure. Hope it helps:)
using 'agg' due to no gui on server.
Debugging on ubuntu 21.10 with gui and VSC.
In debug, trying to both display a plot and then saving to file for web UI.
Found out that saving before showing is required, otherwise saved plot is blank. I suppose that showing will clear the plot for some reason. Do this:
plt.savefig(imagePath)
plt.show()
plt.close(fig)
Instead of this:
plt.show()
plt.savefig(imagePath)
plt.close(fig)
I am using Matplotlib on MacOS with Sulime Text.
I use Python 3.5 and Matplotlib 2.0.
When I work on a figure, I usually have a script that plot the data, and save the figure in a .pdf file with plt.savefig(). Then I use Skim (a pdf viewer) in order to refresh the file each time I modify and run the script. This allows me to set my working layout as clean as: there is one window for the script, and one window for the figure which is automatically refreshing.
I would like to do keep the same layout, but using the Matplotlib figures (because they are interactive). I am looking for a way to use plt.show() but always in the same figure that has been created the first time I've run the script.
For instance:
1. First run
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.figure()
noise = np.random.rand(1, 100)
ax(noise)
plt.show()
2. Following runs
import matplotlib.pyplot as plt
import numpy as np
# This is the super command I am looking for
fig = plt.get_previous_run_figure()
ax = fig.axes
noise = np.random.rand(1, 100)
ax.plot(noise)
plt.draw()
In that case of course, I would have to do a first-run script separately from the main script. Does anyone know if it is possible ?
You want to have multiple consecutive python sessions share a common Matplotlib window. I see no way to share this windows from separate processes, especially when the original owner may terminate at any point in time.
However, you could do something similar to your current workflow in which you have an external pdf viewer to view a output file which you update from multiple python instances.
See this question/answer on how to pickle a matplotlib figure:
Store and reload matplotlib.pyplot object
In every script, output your matplotlib figure as a pickled object, rather than calling plt.show():
import matplotlib.pyplot as plt
import numpy as np
import pickle
ax = plt.subplot(111)
x = np.linspace(0, 10)
y = np.exp(x)
plt.plot(x, y)
pickle.dump(ax, file('myplot.pickle', 'w'))
Then, start a dedicated python session which loads this pickled object and calls plt.show(). Have this script run in a loop, checking for updates of the pickled file on disk, and reloading when necessary:
import matplotlib.pyplot as plt
import pickle
while True:
ax = pickle.load(file('myplot.pickle'))
plt.show()
Alternative
Instead of having separate python sessions, I usually have a single Ipython session in which I run different script. By selecting the same figure windows, I end up with a mostly similar setup as you describe, in which the same figure window is reused throughout the day.
import matplotlib.pyplot as plt
fig = plt.figure(0)
fig.clf()
plt.show()
In principle establishing a connection between two different scripts could be done using a system-wide clipboard. (As far as I know the clipboard in windows and macos are system-wide.)
So the idea can be to set up an application using tk or pyqt, and implement a generic FigureCanvas. This application could have an event listener for changes in the clipboard.
The other main workflow script would then call some function that wraps the current figure into a pickle object and sends it to the clipboard, from where it gets caught by the GUI application, is unpickled and shown in the canvas.
This sounds like a little bit of work, but should meet the very restrictive requirements from the question.
The alternative from Daan worked for me. Here's a bit more code. I used this in a Tkinter interactive GUI for reusing/updating a matplotlib figure window:
fig1 = None
if fig1:
#if exists, clear figure 1
plt.figure(1).clf()
plt.suptitle("New Fig Title", fontsize=18, fontweight='bold')
#reuse window of figure 1 for new figure
fig1 = plt.scatter(points.T[0], points.T[1], color='red', **plot_kwds)
else:
#initialize
fig1 = plt.figure(num=1,figsize=(7, int(7*imgRatio)), dpi=80)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.tick_params(axis='both', which='minor', labelsize=14)
plt.suptitle("Title", fontsize=18, fontweight='bold')
fig1 = plt.scatter(points.T[0], points.T[1], color='red', **plot_kwds)
The figure is reusing the (interactive) plt window. For this to work, I had to set interactive : True in the matplotlibrc file (see my comment here)
I'm brand new to Python, I just switched from Matlab. The distro is Anaconda 2.1.0 and I'm using the Spyder IDE that came with it.
I'm trying to make a scatter plot with equal ratios on the x and y axes, so that this code prints a square figure with the vertices of a regular hexagon plotted inside.
import numpy
import cmath
import matplotlib
coeff = [1,0,0,0,0,0,-1]
x = numpy.roots(coeff)
zeroplot = plot(real(x),imag(x), 'ro')
plt.gca(aspect='equal')
plt.show()
But plt.gca(aspect='equal') returns a blank figure with axes [0,1,0,1], and plt.show() returns nothing.
I think the main problem is that plt.gca(aspect='equal') doesn't just grab the current axis and set its aspect ratio. From the documentation, (help(plt.gca)) it appears to create a new axis if the current one doesn't have the correct aspect ratio, so the immediate fix for this should be to replace plt.gca(aspect='equal') with:
ax = plt.gca()
ax.set_aspect('equal')
I should also mention that I had a little bit of trouble getting your code running because you're using pylab to automatically load numpy and matplotlib functions: I had to change my version to:
import numpy
import cmath
from matplotlib import pyplot as plt
coeff = [1,0,0,0,0,0,-1]
x = numpy.roots(coeff)
zeroplot = plt.plot(numpy.real(x), numpy.imag(x), 'ro')
ax = plt.gca()
ax.set_aspect('equal')
plt.show()
People who are already comfortable with Python don't generally use Pylab, from my experience. In future you might find it hard to get help on things if people don't realise that you're using Pylab or aren't familiar with how it works. I'd recommend disabling it and trying to get used to accessing the functions you need through their respective modules (e.g. using numpy.real instead of just real)
This displays the figure in a GUI:
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [1, 4, 9])
plt.show()
But how do I instead save the figure to a file (e.g. foo.png)?
When using matplotlib.pyplot.savefig, the file format can be specified by the extension:
from matplotlib import pyplot as plt
plt.savefig('foo.png')
plt.savefig('foo.pdf')
That gives a rasterized or vectorized output respectively.
In addition, there is sometimes undesirable whitespace around the image, which can be removed with:
plt.savefig('foo.png', bbox_inches='tight')
Note that if showing the plot, plt.show() should follow plt.savefig(); otherwise, the file image will be blank.
As others have said, plt.savefig() or fig1.savefig() is indeed the way to save an image.
However I've found that in certain cases the figure is always shown. (eg. with Spyder having plt.ion(): interactive mode = On.) I work around this by
forcing the the figure window to close with:
plt.close(figure_object)
(see documentation). This way I don't have a million open figures during a large loop. Example usage:
import matplotlib.pyplot as plt
fig, ax = plt.subplots( nrows=1, ncols=1 ) # create figure & 1 axis
ax.plot([0,1,2], [10,20,3])
fig.savefig('path/to/save/image/to.png') # save the figure to file
plt.close(fig) # close the figure window
You should be able to re-open the figure later if needed to with fig.show() (didn't test myself).
The solution is:
pylab.savefig('foo.png')
Just found this link on the MatPlotLib documentation addressing exactly this issue:
http://matplotlib.org/faq/howto_faq.html#generate-images-without-having-a-window-appear
They say that the easiest way to prevent the figure from popping up is to use a non-interactive backend (eg. Agg), via matplotib.use(<backend>), eg:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.plot([1,2,3])
plt.savefig('myfig')
I still personally prefer using plt.close( fig ), since then you have the option to hide certain figures (during a loop), but still display figures for post-loop data processing. It is probably slower than choosing a non-interactive backend though - would be interesting if someone tested that.
UPDATE: for Spyder, you usually can't set the backend in this way (Because Spyder usually loads matplotlib early, preventing you from using matplotlib.use()).
Instead, use plt.switch_backend('Agg'), or Turn off "enable support" in the Spyder prefs and run the matplotlib.use('Agg') command yourself.
From these two hints: one, two
If you don't like the concept of the "current" figure, do:
import matplotlib.image as mpimg
img = mpimg.imread("src.png")
mpimg.imsave("out.png", img)
import datetime
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
# Create the PdfPages object to which we will save the pages:
# The with statement makes sure that the PdfPages object is closed properly at
# the end of the block, even if an Exception occurs.
with PdfPages('multipage_pdf.pdf') as pdf:
plt.figure(figsize=(3, 3))
plt.plot(range(7), [3, 1, 4, 1, 5, 9, 2], 'r-o')
plt.title('Page One')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
plt.rc('text', usetex=True)
plt.figure(figsize=(8, 6))
x = np.arange(0, 5, 0.1)
plt.plot(x, np.sin(x), 'b-')
plt.title('Page Two')
pdf.savefig()
plt.close()
plt.rc('text', usetex=False)
fig = plt.figure(figsize=(4, 5))
plt.plot(x, x*x, 'ko')
plt.title('Page Three')
pdf.savefig(fig) # or you can pass a Figure object to pdf.savefig
plt.close()
# We can also set the file's metadata via the PdfPages object:
d = pdf.infodict()
d['Title'] = 'Multipage PDF Example'
d['Author'] = u'Jouni K. Sepp\xe4nen'
d['Subject'] = 'How to create a multipage pdf file and set its metadata'
d['Keywords'] = 'PdfPages multipage keywords author title subject'
d['CreationDate'] = datetime.datetime(2009, 11, 13)
d['ModDate'] = datetime.datetime.today()
I used the following:
import matplotlib.pyplot as plt
p1 = plt.plot(dates, temp, 'r-', label="Temperature (celsius)")
p2 = plt.plot(dates, psal, 'b-', label="Salinity (psu)")
plt.legend(loc='upper center', numpoints=1, bbox_to_anchor=(0.5, -0.05), ncol=2, fancybox=True, shadow=True)
plt.savefig('data.png')
plt.show()
plt.close()
I found very important to use plt.show after saving the figure, otherwise it won't work.figure exported in png
The other answers are correct. However, I sometimes find that I want to open the figure object later. For example, I might want to change the label sizes, add a grid, or do other processing. In a perfect world, I would simply rerun the code generating the plot, and adapt the settings. Alas, the world is not perfect. Therefore, in addition to saving to PDF or PNG, I add:
with open('some_file.pkl', "wb") as fp:
pickle.dump(fig, fp, protocol=4)
Like this, I can later load the figure object and manipulate the settings as I please.
I also write out the stack with the source-code and locals() dictionary for each function/method in the stack, so that I can later tell exactly what generated the figure.
NB: Be careful, as sometimes this method generates huge files.
After using the plot() and other functions to create the content you want, you could use a clause like this to select between plotting to the screen or to file:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(4, 5)) # size in inches
# use plot(), etc. to create your plot.
# Pick one of the following lines to uncomment
# save_file = None
# save_file = os.path.join(your_directory, your_file_name)
if save_file:
plt.savefig(save_file)
plt.close(fig)
else:
plt.show()
If, like me, you use Spyder IDE, you have to disable the interactive mode with :
plt.ioff()
(this command is automatically launched with the scientific startup)
If you want to enable it again, use :
plt.ion()
You can either do:
plt.show(hold=False)
plt.savefig('name.pdf')
and remember to let savefig finish before closing the GUI plot. This way you can see the image beforehand.
Alternatively, you can look at it with plt.show()
Then close the GUI and run the script again, but this time replace plt.show() with plt.savefig().
Alternatively, you can use
fig, ax = plt.figure(nrows=1, ncols=1)
plt.plot(...)
plt.show()
fig.savefig('out.pdf')
According to question Matplotlib (pyplot) savefig outputs blank image.
One thing should note: if you use plt.show and it should after plt.savefig, or you will give a blank image.
A detailed example:
import numpy as np
import matplotlib.pyplot as plt
def draw_result(lst_iter, lst_loss, lst_acc, title):
plt.plot(lst_iter, lst_loss, '-b', label='loss')
plt.plot(lst_iter, lst_acc, '-r', label='accuracy')
plt.xlabel("n iteration")
plt.legend(loc='upper left')
plt.title(title)
plt.savefig(title+".png") # should before plt.show method
plt.show()
def test_draw():
lst_iter = range(100)
lst_loss = [0.01 * i + 0.01 * i ** 2 for i in xrange(100)]
# lst_loss = np.random.randn(1, 100).reshape((100, ))
lst_acc = [0.01 * i - 0.01 * i ** 2 for i in xrange(100)]
# lst_acc = np.random.randn(1, 100).reshape((100, ))
draw_result(lst_iter, lst_loss, lst_acc, "sgd_method")
if __name__ == '__main__':
test_draw()
The Solution :
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
plt.figure()
ts.plot()
plt.savefig("foo.png", bbox_inches='tight')
If you do want to display the image as well as saving the image use:
%matplotlib inline
after
import matplotlib
When using matplotlib.pyplot, you must first save your plot and then close it using these 2 lines:
fig.savefig('plot.png') # save the plot, place the path you want to save the figure in quotation
plt.close(fig) # close the figure window
import matplotlib.pyplot as plt
plt.savefig("image.png")
In Jupyter Notebook you have to remove plt.show() and add plt.savefig(), together with the rest of the plt-code in one cell.
The image will still show up in your notebook.
Additionally to those above, I added __file__ for the name so the picture and Python file get the same names. I also added few arguments to make It look better:
# Saves a PNG file of the current graph to the folder and updates it every time
# (nameOfimage, dpi=(sizeOfimage),Keeps_Labels_From_Disappearing)
plt.savefig(__file__+".png",dpi=(250), bbox_inches='tight')
# Hard coded name: './test.png'
Just a extra note because I can't comment on posts yet.
If you are using plt.savefig('myfig') or something along these lines make sure to add a plt.clf() after your image is saved. This is because savefig does not close the plot and if you add to the plot after without a plt.clf() you'll be adding to the previous plot.
You may not notice if your plots are similar as it will plot over the previous plot, but if you are in a loop saving your figures the plot will slowly become massive and make your script very slow.
Given that today (was not available when this question was made) lots of people use Jupyter Notebook as python console, there is an extremely easy way to save the plots as .png, just call the matplotlib's pylab class from Jupyter Notebook, plot the figure 'inline' jupyter cells, and then drag that figure/image to a local directory. Don't forget
%matplotlib inline in the first line!
As suggested before, you can either use:
import matplotlib.pyplot as plt
plt.savefig("myfig.png")
For saving whatever IPhython image that you are displaying. Or on a different note (looking from a different angle), if you ever get to work with open cv, or if you have open cv imported, you can go for:
import cv2
cv2.imwrite("myfig.png",image)
But this is just in case if you need to work with Open CV. Otherwise plt.savefig() should be sufficient.
well, I do recommend using wrappers to render or control the plotting. examples can be mpltex (https://github.com/liuyxpp/mpltex) or prettyplotlib (https://github.com/olgabot/prettyplotlib).
import mpltex
#mpltex.acs_decorator
def myplot():
plt.figure()
plt.plot(x,y,'b-',lable='xxx')
plt.tight_layout(pad=0.5)
plt.savefig('xxxx') # the figure format was controlled by the decorator, it can be either eps, or pdf or png....
plt.close()
I basically use this decorator a lot for publishing academic papers in various journals at American Chemical Society, American Physics Society, Opticcal Society American, Elsivier and so on.
An example can be found as following image (https://github.com/MarkMa1990/gradientDescent):
You can do it like this:
def plotAFig():
plt.figure()
plt.plot(x,y,'b-')
plt.savefig("figurename.png")
plt.close()
Nothing was working for me. The problem is that the saved imaged was very small and I could not find how the hell make it bigger.
This seems to make it bigger, but still not full screen.
https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.set_size_inches
fig.set_size_inches((w, h))
Hope that helps somebody.
You can save your image with any extension(png, jpg,etc.) and with the resolution you want. Here's a function to save your figure.
import os
def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format=fig_extension, dpi=resolution)
'fig_id' is the name by which you want to save your figure. Hope it helps:)
using 'agg' due to no gui on server.
Debugging on ubuntu 21.10 with gui and VSC.
In debug, trying to both display a plot and then saving to file for web UI.
Found out that saving before showing is required, otherwise saved plot is blank. I suppose that showing will clear the plot for some reason. Do this:
plt.savefig(imagePath)
plt.show()
plt.close(fig)
Instead of this:
plt.show()
plt.savefig(imagePath)
plt.close(fig)