I am looking for a way to save a matplotlib figure as an EMF file. Matplotlib allows me to save as either a PDF or SVG vector file but not as EMF.
After a long search I still cannot seem to find a way to do this with python. Hopefully anyone has an idea.
My workaround is to call inkscape using subprocess but this is far from ideal as I would like to avoid the use of external programs.
I'm running python 2.7.5 and matplotlib 1.3.0 using the wx backend.
For anyone who still needs this, I wrote a basic function that can let you save a file as an emf from matplotlib, as long as you have inkscape installed.
I know the op didn't want inkscape, but people who find this post later just want to make it work.
import matplotlib.pyplot as plt
import subprocess
import os
inkscapePath = r"path\to\inkscape.exe"
savePath= r"path\to\images\folder"
def exportEmf(savePath, plotName, fig=None, keepSVG=False):
"""Save a figure as an emf file
Parameters
----------
savePath : str, the path to the directory you want the image saved in
plotName : str, the name of the image
fig : matplotlib figure, (optional, default uses gca)
keepSVG : bool, whether to keep the interim svg file
"""
figFolder = savePath + r"\{}.{}"
svgFile = figFolder.format(plotName,"svg")
emfFile = figFolder.format(plotName,"emf")
if fig:
use=fig
else:
use=plt
use.savefig(svgFile)
subprocess.run([inkscapePath, svgFile, '-M', emfFile])
if not keepSVG:
os.system('del "{}"'.format(svgFile))
#Example Usage
import numpy as np
tt = np.linspace(0, 2*3.14159)
plt.plot(tt, np.sin(tt))
exportEmf(r"C:\Users\userName", 'FileName')
I think the function is cool but inkscape syntax seems not working in my case. I search in other post and find it as:
inkscape filename.svg --export-filename filename.emf
So if I replace -M by --export-filename within the subprocess argument, everything works fine.
Related
I was trying to read fits files from Kepler FITS files (Received from this URL https://archive.stsci.edu/pub/kepler/lightcurves/0007/000757076/) using astropy. Below are the set of commands I was trying to read the file:
from astropy.io import fits
fits_image_filename = fits.util.get_testdata_filepath(r'O:\MyWorks\keplar-test\kplr100000925-2009166043257_llc.fits')
But the above command produced this error:
I am not sure how to solve this error. My target is to read keplar data then plot this and/or convert this to CSV.
This: fits.util.get_testdata_filepath(r'O:\MyWorks\keplar-test\kplr100000925-2009166043257_llc.fits') is not the correct function for opening a file.
You should use fits.open('file.fits'), or if this is table data, as you imply, Table.read('file.fits')
See the note at the top of the FITS documentation
%matplotlib inline
from astropy.io import fits
import matplotlib
import matplotlib.pyplot as plt
#My required file has been downloaded in the following path of my HD,
"~/projects/eclipsing_binary/A/mastDownload/HLSP/hlsp_qlp_tess_ffi_s0018-0000000346784049_tess_v01_llc/hlsp_qlp_tess_ffi_s0018-000000346784049_tess_v01_llc.fits". Using linux command open and see the list
of the files in the directory.
%cd ~/projects/eclipsing_binary/A/mastDownload/HLSP/
hlsp_qlp_tess_ffi_s0018-0000000346784049_tess_v01_llc/
%ls
#Now plot the required file in simple way,
import lightkurve as lk
file_r = 'hlsp_qlp_tess_ffi_s0018-0000000346784049_tess_v01_llc.fits'
lr = lk.read(file_r)
lr.plot()
Is there a way to save a Matplotlib figure such that it can be re-opened and have typical interaction restored? (Like the .fig format in MATLAB?)
I find myself running the same scripts many times to generate these interactive figures. Or I'm sending my colleagues multiple static PNG files to show different aspects of a plot. I'd rather send the figure object and have them interact with it themselves.
I just found out how to do this. The "experimental pickle support" mentioned by #pelson works quite well.
Try this:
# Plot something
import matplotlib.pyplot as plt
fig,ax = plt.subplots()
ax.plot([1,2,3],[10,-10,30])
After your interactive tweaking, save the figure object as a binary file:
import pickle
pickle.dump(fig, open('FigureObject.fig.pickle', 'wb')) # This is for Python 3 - py2 may need `file` instead of `open`
Later, open the figure and the tweaks should be saved and GUI interactivity should be present:
import pickle
figx = pickle.load(open('FigureObject.fig.pickle', 'rb'))
figx.show() # Show the figure, edit it, etc.!
You can even extract the data from the plots:
data = figx.axes[0].lines[0].get_data()
(It works for lines, pcolor & imshow - pcolormesh works with some tricks to reconstruct the flattened data.)
I got the excellent tip from Saving Matplotlib Figures Using Pickle.
As of Matplotlib 1.2, we now have experimental pickle support. Give that a go and see if it works well for your case. If you have any issues, please let us know on the Matplotlib mailing list or by opening an issue on github.com/matplotlib/matplotlib.
This would be a great feature, but AFAIK it isn't implemented in Matplotlib and likely would be difficult to implement yourself due to the way figures are stored.
I'd suggest either (a) separate processing the data from generating the figure (which saves data with a unique name) and write a figure generating script (loading a specified file of the saved data) and editing as you see fit or (b) save as PDF/SVG/PostScript format and edit in some fancy figure editor like Adobe Illustrator (or Inkscape).
EDIT post Fall 2012: As others pointed out below (though mentioning here as this is the accepted answer), Matplotlib since version 1.2 allowed you to pickle figures. As the release notes state, it is an experimental feature and does not support saving a figure in one matplotlib version and opening in another. It's also generally unsecure to restore a pickle from an untrusted source.
For sharing/later editing plots (that require significant data processing first and may need to be tweaked months later say during peer review for a scientific publication), I still recommend the workflow of (1) have a data processing script that before generating a plot saves the processed data (that goes into your plot) into a file, and (2) have a separate plot generation script (that you adjust as necessary) to recreate the plot. This way for each plot you can quickly run a script and re-generate it (and quickly copy over your plot settings with new data). That said, pickling a figure could be convenient for short term/interactive/exploratory data analysis.
Why not just send the Python script? MATLAB's .fig files require the recipient to have MATLAB to display them, so that's about equivalent to sending a Python script that requires Matplotlib to display.
Alternatively (disclaimer: I haven't tried this yet), you could try pickling the figure:
import pickle
output = open('interactive figure.pickle', 'wb')
pickle.dump(gcf(), output)
output.close()
Good question. Here is the doc text from pylab.save:
pylab no longer provides a save function, though the old pylab
function is still available as matplotlib.mlab.save (you can still
refer to it in pylab as "mlab.save"). However, for plain text
files, we recommend numpy.savetxt. For saving numpy arrays,
we recommend numpy.save, and its analog numpy.load, which are
available in pylab as np.save and np.load.
I figured out a relatively simple way (yet slightly unconventional) to save my matplotlib figures. It works like this:
import libscript
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2*np.pi*t)
#<plot>
plt.plot(t, s)
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.title('About as simple as it gets, folks')
plt.grid(True)
plt.show()
#</plot>
save_plot(fileName='plot_01.py',obj=sys.argv[0],sel='plot',ctx=libscript.get_ctx(ctx_global=globals(),ctx_local=locals()))
with function save_plot defined like this (simple version to understand the logic):
def save_plot(fileName='',obj=None,sel='',ctx={}):
"""
Save of matplolib plot to a stand alone python script containing all the data and configuration instructions to regenerate the interactive matplotlib figure.
Parameters
----------
fileName : [string] Path of the python script file to be created.
obj : [object] Function or python object containing the lines of code to create and configure the plot to be saved.
sel : [string] Name of the tag enclosing the lines of code to create and configure the plot to be saved.
ctx : [dict] Dictionary containing the execution context. Values for variables not defined in the lines of code for the plot will be fetched from the context.
Returns
-------
Return ``'done'`` once the plot has been saved to a python script file. This file contains all the input data and configuration to re-create the original interactive matplotlib figure.
"""
import os
import libscript
N_indent=4
src=libscript.get_src(obj=obj,sel=sel)
src=libscript.prepend_ctx(src=src,ctx=ctx,debug=False)
src='\n'.join([' '*N_indent+line for line in src.split('\n')])
if(os.path.isfile(fileName)): os.remove(fileName)
with open(fileName,'w') as f:
f.write('import sys\n')
f.write('sys.dont_write_bytecode=True\n')
f.write('def main():\n')
f.write(src+'\n')
f.write('if(__name__=="__main__"):\n')
f.write(' '*N_indent+'main()\n')
return 'done'
or defining function save_plot like this (better version using zip compression to produce lighter figure files):
def save_plot(fileName='',obj=None,sel='',ctx={}):
import os
import json
import zlib
import base64
import libscript
N_indent=4
level=9#0 to 9, default: 6
src=libscript.get_src(obj=obj,sel=sel)
obj=libscript.load_obj(src=src,ctx=ctx,debug=False)
bin=base64.b64encode(zlib.compress(json.dumps(obj),level))
if(os.path.isfile(fileName)): os.remove(fileName)
with open(fileName,'w') as f:
f.write('import sys\n')
f.write('sys.dont_write_bytecode=True\n')
f.write('def main():\n')
f.write(' '*N_indent+'import base64\n')
f.write(' '*N_indent+'import zlib\n')
f.write(' '*N_indent+'import json\n')
f.write(' '*N_indent+'import libscript\n')
f.write(' '*N_indent+'bin="'+str(bin)+'"\n')
f.write(' '*N_indent+'obj=json.loads(zlib.decompress(base64.b64decode(bin)))\n')
f.write(' '*N_indent+'libscript.exec_obj(obj=obj,tempfile=False)\n')
f.write('if(__name__=="__main__"):\n')
f.write(' '*N_indent+'main()\n')
return 'done'
This makes use a module libscript of my own, which mostly relies on modules inspect and ast. I can try to share it on Github if interest is expressed (it would first require some cleanup and me to get started with Github).
The idea behind this save_plot function and libscript module is to fetch the python instructions that create the figure (using module inspect), analyze them (using module ast) to extract all variables, functions and modules import it relies on, extract these from the execution context and serialize them as python instructions (code for variables will be like t=[0.0,2.0,0.01] ... and code for modules will be like import matplotlib.pyplot as plt ...) prepended to the figure instructions. The resulting python instructions are saved as a python script whose execution will re-build the original matplotlib figure.
As you can imagine, this works well for most (if not all) matplotlib figures.
If you are looking to save python plots as an interactive figure to modify and share with others like MATLAB .fig file then you can try to use the following code. Here z_data.values is just a numpy ndarray and so you can use the same code to plot and save your own data. No need of using pandas then.
The file generated here can be opened and interactively modified by anyone with or without python just by clicking on it and opening in browsers like Chrome/Firefox/Edge etc.
import plotly.graph_objects as go
import pandas as pd
z_data=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv')
fig = go.Figure(data=[go.Surface(z=z_data.values)])
fig.update_layout(title='Mt Bruno Elevation', autosize=False,
width=500, height=500,
margin=dict(l=65, r=50, b=65, t=90))
fig.show()
fig.write_html("testfile.html")
Hello I am using the Ubuntu Server 14.04 LTS (HVM), SSD Volume Type instance from amazon aws and I am running python 2.7.9 and the latest version of matplotlib. I am trying to plot the sine function and then save the figure to a png in the home directory. Below is my code:
import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0,2*np.pi,100)
y = np.sin(x)
plt.plot(x,y)
plt.savefig('Sine')
after I save the figure I use WinSCP to move the png file to my local desktop so that I can open it. But when I open the file I only see the black box with x and y tick marks.
Am I just using the wrong backend, or is the problem egregiously more severe?
I believe your problem comes from the fact that you are actually plotting nothing to your plot because x is empty. The step you use is too large in your np.arange. The third argument to np.arange is the step or increment use to build the array, unlike matlab linspace function, for which the third argument is the number of points generated.
Try this instead:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0,2*np.pi,0.01)
y = np.sin(x)
plt.plot(x,y)
plt.savefig('Sine.png')
which results in this png (in Ubuntu 15.04, Python 2.7.9, matplotlib 1.4.2):
update(2015-07-28):
Regarding the backend, as suggested in the pyplot documentation:
If format is None and fname is a string, the output format is deduced from the extension of the filename. If the filename has no extension, the value of the rc parameter savefig.format is used.
If fname is not a string, remember to specify format to ensure that the correct backend is used.
So maybe to explicitly specifying an extension to the file will help solving the issue regarding the backend (I've updated the code accordingly). By default, the backend TkAgg is used on my machine, so there was no problem plotting with the default settings.
I've been trying to throw together a python program that will align, crop and create an RGB image from HST and VLA .fits data. Unfortunately I've run into a bit of a problem with it continually opening a past file that does not exist in the folder and neither is it opening in the code itself. I've googled and googled and haven't found anything like it, so perhaps it's just common sense to most, but I can't figure it out. Here's the error message:
You can see at the top that the program I'm running has the filename rgbhstvla.py. I'm not sure what the error message means. Here's the python program as well:
import pyfits
import numpy as np
import pylab as py
import img_scale
from pyraf import iraf as ir
fits.open('3c68.fits', readonly)
j_img = pyfits.getdata('230UVIS.fits')
h_img = pyfits.getdata('230IR.fits')
k_img = pyfits.getdata('5GHZ.fits')
jmin,jmax = j_img.mean()+0.75*j_img.std(),j_img.mean()+5*j_img.std()
hmin,hmax = h_img.mean()+0.75*h_img.std(),h_img.mean()+5*h_img.std()
kmin,kmax = k_img.mean()+0.75*k_img.std(),k_img.mean()+5*k_img.std()
img = numpy.zeros((1024,1024,3))
img[:,:,0] = img_scale.asinh(j_img,scale_min=jmin,scale_max=jmax)
img[:,:,1] = img_scale.asinh(h_img,scale_min=hmin,scale_max=hmax)
img[:,:,2] = img_scale.asinh(k_img,scale_min=kmin,scale_max=kmax)
pylab.clf()
pylab.imshow(img)
pylab.show()
(I'm still working on the program since I'm new to python, tips here would be nice as well but they're mostly unnecessary as I'm sure I'll figure it out eventually).
Python cannot find the file 3c68.fits, which is expected to be in the current working directory, C:\Users\Brandon\Desktop\Research. Either make sure the file is in that directory, or provide an absolute path in your code.
Is there a way to save a Matplotlib figure such that it can be re-opened and have typical interaction restored? (Like the .fig format in MATLAB?)
I find myself running the same scripts many times to generate these interactive figures. Or I'm sending my colleagues multiple static PNG files to show different aspects of a plot. I'd rather send the figure object and have them interact with it themselves.
I just found out how to do this. The "experimental pickle support" mentioned by #pelson works quite well.
Try this:
# Plot something
import matplotlib.pyplot as plt
fig,ax = plt.subplots()
ax.plot([1,2,3],[10,-10,30])
After your interactive tweaking, save the figure object as a binary file:
import pickle
pickle.dump(fig, open('FigureObject.fig.pickle', 'wb')) # This is for Python 3 - py2 may need `file` instead of `open`
Later, open the figure and the tweaks should be saved and GUI interactivity should be present:
import pickle
figx = pickle.load(open('FigureObject.fig.pickle', 'rb'))
figx.show() # Show the figure, edit it, etc.!
You can even extract the data from the plots:
data = figx.axes[0].lines[0].get_data()
(It works for lines, pcolor & imshow - pcolormesh works with some tricks to reconstruct the flattened data.)
I got the excellent tip from Saving Matplotlib Figures Using Pickle.
As of Matplotlib 1.2, we now have experimental pickle support. Give that a go and see if it works well for your case. If you have any issues, please let us know on the Matplotlib mailing list or by opening an issue on github.com/matplotlib/matplotlib.
This would be a great feature, but AFAIK it isn't implemented in Matplotlib and likely would be difficult to implement yourself due to the way figures are stored.
I'd suggest either (a) separate processing the data from generating the figure (which saves data with a unique name) and write a figure generating script (loading a specified file of the saved data) and editing as you see fit or (b) save as PDF/SVG/PostScript format and edit in some fancy figure editor like Adobe Illustrator (or Inkscape).
EDIT post Fall 2012: As others pointed out below (though mentioning here as this is the accepted answer), Matplotlib since version 1.2 allowed you to pickle figures. As the release notes state, it is an experimental feature and does not support saving a figure in one matplotlib version and opening in another. It's also generally unsecure to restore a pickle from an untrusted source.
For sharing/later editing plots (that require significant data processing first and may need to be tweaked months later say during peer review for a scientific publication), I still recommend the workflow of (1) have a data processing script that before generating a plot saves the processed data (that goes into your plot) into a file, and (2) have a separate plot generation script (that you adjust as necessary) to recreate the plot. This way for each plot you can quickly run a script and re-generate it (and quickly copy over your plot settings with new data). That said, pickling a figure could be convenient for short term/interactive/exploratory data analysis.
Why not just send the Python script? MATLAB's .fig files require the recipient to have MATLAB to display them, so that's about equivalent to sending a Python script that requires Matplotlib to display.
Alternatively (disclaimer: I haven't tried this yet), you could try pickling the figure:
import pickle
output = open('interactive figure.pickle', 'wb')
pickle.dump(gcf(), output)
output.close()
Good question. Here is the doc text from pylab.save:
pylab no longer provides a save function, though the old pylab
function is still available as matplotlib.mlab.save (you can still
refer to it in pylab as "mlab.save"). However, for plain text
files, we recommend numpy.savetxt. For saving numpy arrays,
we recommend numpy.save, and its analog numpy.load, which are
available in pylab as np.save and np.load.
I figured out a relatively simple way (yet slightly unconventional) to save my matplotlib figures. It works like this:
import libscript
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2*np.pi*t)
#<plot>
plt.plot(t, s)
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.title('About as simple as it gets, folks')
plt.grid(True)
plt.show()
#</plot>
save_plot(fileName='plot_01.py',obj=sys.argv[0],sel='plot',ctx=libscript.get_ctx(ctx_global=globals(),ctx_local=locals()))
with function save_plot defined like this (simple version to understand the logic):
def save_plot(fileName='',obj=None,sel='',ctx={}):
"""
Save of matplolib plot to a stand alone python script containing all the data and configuration instructions to regenerate the interactive matplotlib figure.
Parameters
----------
fileName : [string] Path of the python script file to be created.
obj : [object] Function or python object containing the lines of code to create and configure the plot to be saved.
sel : [string] Name of the tag enclosing the lines of code to create and configure the plot to be saved.
ctx : [dict] Dictionary containing the execution context. Values for variables not defined in the lines of code for the plot will be fetched from the context.
Returns
-------
Return ``'done'`` once the plot has been saved to a python script file. This file contains all the input data and configuration to re-create the original interactive matplotlib figure.
"""
import os
import libscript
N_indent=4
src=libscript.get_src(obj=obj,sel=sel)
src=libscript.prepend_ctx(src=src,ctx=ctx,debug=False)
src='\n'.join([' '*N_indent+line for line in src.split('\n')])
if(os.path.isfile(fileName)): os.remove(fileName)
with open(fileName,'w') as f:
f.write('import sys\n')
f.write('sys.dont_write_bytecode=True\n')
f.write('def main():\n')
f.write(src+'\n')
f.write('if(__name__=="__main__"):\n')
f.write(' '*N_indent+'main()\n')
return 'done'
or defining function save_plot like this (better version using zip compression to produce lighter figure files):
def save_plot(fileName='',obj=None,sel='',ctx={}):
import os
import json
import zlib
import base64
import libscript
N_indent=4
level=9#0 to 9, default: 6
src=libscript.get_src(obj=obj,sel=sel)
obj=libscript.load_obj(src=src,ctx=ctx,debug=False)
bin=base64.b64encode(zlib.compress(json.dumps(obj),level))
if(os.path.isfile(fileName)): os.remove(fileName)
with open(fileName,'w') as f:
f.write('import sys\n')
f.write('sys.dont_write_bytecode=True\n')
f.write('def main():\n')
f.write(' '*N_indent+'import base64\n')
f.write(' '*N_indent+'import zlib\n')
f.write(' '*N_indent+'import json\n')
f.write(' '*N_indent+'import libscript\n')
f.write(' '*N_indent+'bin="'+str(bin)+'"\n')
f.write(' '*N_indent+'obj=json.loads(zlib.decompress(base64.b64decode(bin)))\n')
f.write(' '*N_indent+'libscript.exec_obj(obj=obj,tempfile=False)\n')
f.write('if(__name__=="__main__"):\n')
f.write(' '*N_indent+'main()\n')
return 'done'
This makes use a module libscript of my own, which mostly relies on modules inspect and ast. I can try to share it on Github if interest is expressed (it would first require some cleanup and me to get started with Github).
The idea behind this save_plot function and libscript module is to fetch the python instructions that create the figure (using module inspect), analyze them (using module ast) to extract all variables, functions and modules import it relies on, extract these from the execution context and serialize them as python instructions (code for variables will be like t=[0.0,2.0,0.01] ... and code for modules will be like import matplotlib.pyplot as plt ...) prepended to the figure instructions. The resulting python instructions are saved as a python script whose execution will re-build the original matplotlib figure.
As you can imagine, this works well for most (if not all) matplotlib figures.
If you are looking to save python plots as an interactive figure to modify and share with others like MATLAB .fig file then you can try to use the following code. Here z_data.values is just a numpy ndarray and so you can use the same code to plot and save your own data. No need of using pandas then.
The file generated here can be opened and interactively modified by anyone with or without python just by clicking on it and opening in browsers like Chrome/Firefox/Edge etc.
import plotly.graph_objects as go
import pandas as pd
z_data=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv')
fig = go.Figure(data=[go.Surface(z=z_data.values)])
fig.update_layout(title='Mt Bruno Elevation', autosize=False,
width=500, height=500,
margin=dict(l=65, r=50, b=65, t=90))
fig.show()
fig.write_html("testfile.html")