This runs fine without any warnings or errors,
from matplotlib import pyplot as plt
plt.plot(range(10))
This,
from matplotlib import pyplot as plt
import sympy
plt.plot(range(10))
Produces,
/home/username/.local/lib/python3.8/site-packages/matplotlib/backends/backend_gtk3.py:327:
DeprecationWarning: Gtk.Window.set_wmclass is deprecated
self.window.set_wmclass("matplotlib", "Matplotlib")
Note that if you import them both but don't call the plot function, you don't get the deprecation warning.
I have no idea what is going on. Googling that warning does not quite find anything related to Sympy. Python is at 3.8.5, Sympy at 1.8 and Matplotlib at 3.4.0.
The message you received is a warning and not an error. The difference being that your code has been executed normally, the message provides extra information that in some point in the future Gtk.Window.set_wmclass will not be available, it's a note to programmers (in this case the matplotlib devs) that they should change their code to use the new way of doing something. If you use a combination of old/new packages you should expect to see these warnings.
You can either ignore/suppress this warning message, or update your packages.
It's recommended that you use a virtual environment to isolate the Python that you use to from the Python that your system uses. It will allow you to use up-to-date versions of packages and you will no longer risk accidentally breaking system utilities. There are many options and since Python ~3.6 there's the venv module included in the standard library.
Related
Trying to plot data using the matplotlib.pyplot.plot_date function with datetime objects originating from the netCDF4.num2date function I get the following error:
In [1]: from netCDF4 import num2date
In [2]: from matplotlib.pyplot import plot_date
In [3]: d=num2date((86400,2*86400),"seconds since 2000-01-01")
In [4]: gca().plot_date(d,(0,1))
...
AttributeError: 'cftime._cftime.DatetimeGregorian' object has no attribute 'toordinal'
The above exception was the direct cause of the following exception:
ConversionError
...
ConversionError: Failed to convert value(s) to axis units: array([cftime.DatetimeGregorian(2000-01-02 00:00:00),
cftime.DatetimeGregorian(2000-01-03 00:00:00)], dtype=object)
The following package versions are installed:
pandas 1.0.3
matplotlib 3.2.1
netcdf4 1.5.1.2
cftime 1.1.1.2
As the same thing perfectly works on a different machine with older package versions, I assume a version issue.
Also, I've tried the solution suggested in this thread and other related threads which seemed like a similar issue, but
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
didn't help neither.
Any suggestions welcome;)
Found the answer myself, from version 1.1.0 the num2date function in the cftime package changed its default behaviour to return cftime datetime instances instead of python datetime instances where possible (the option argument only_use_cftime_datetimes is now True by default, instead of False). The plot_date function however, at least currently, doesn't handle these.
To avoid the issue, use the arguments only_use_cftime_dateimes=False and only_use_python_datetime=True or use the convenience function num2pydate.
I am using matplotlib (within pylab) to display figures. And I want to save them in .jpg format. When I simply use the savefig command with jpg extension this returns :
ValueError: Format "jpg" is not supported.
Supported formats: emf, eps, pdf, png, ps, raw, rgba, svg, svgz.
Is there a way to perform this ?
You can save an image as 'png' and use the python imaging library (PIL) to convert this file to 'jpg':
import Image
import matplotlib.pyplot as plt
plt.plot(range(10))
plt.savefig('testplot.png')
Image.open('testplot.png').save('testplot.jpg','JPEG')
The original:
The JPEG image:
To clarify and update #neo useful answer and the original question. A clean solution consists of installing Pillow, which is an updated version of the Python Imaging Library (PIL). This is done using
pip install pillow
Once Pillow is installed, the standard Matplotlib commands
import matplotlib.pyplot as plt
plt.plot([1, 2])
plt.savefig('image.jpg')
will save the figure into a JPEG file and will not generate a ValueError any more.
Contrary to #amillerrhodes answer, as of Matplotlib 3.1, JPEG files are still not supported. If I remove the Pillow package I still receive a ValueError about an unsupported file type.
Just install pillow with pip install pillow and it will work.
I just updated matplotlib to 1.1.0 on my system and it now allows me to save to jpg with savefig.
To upgrade to matplotlib 1.1.0 with pip, use this command:
pip install -U 'http://sourceforge.net/projects/matplotlib/files/matplotlib/matplotlib-1.1.0/matplotlib-1.1.0.tar.gz/download'
EDIT (to respond to comment):
pylab is simply an aggregation of the matplotlib.pyplot and numpy namespaces (as well as a few others) jinto a single namespace.
On my system, pylab is just this:
from matplotlib.pylab import *
import matplotlib.pylab
__doc__ = matplotlib.pylab.__doc__
You can see that pylab is just another namespace in your matplotlib installation. Therefore, it doesn't matter whether or not you import it with pylab or with matplotlib.pyplot.
If you are still running into problem, then I'm guessing the macosx backend doesn't support saving plots to jpg. You could try using a different backend. See here for more information.
Matplotlib can handle directly and transparently jpg if you have installed PIL. You don't need to call it, it will do it by itself. If Python cannot find PIL, it will raise an error.
I'm not sure about all versions of Matplotlib, but in the official documentation for v3.5.0 savfig allows you to pass settings through to the underlying Pillow library which anyway does the image saving. So if you want a jpg with specific compression settings for example:
import matplotlib.pyplot as plt
plt.plot(...) # Plot stuff
plt.savefig('filename.jpg', pil_kwargs={
'quality': 20,
'subsampling': 10
})
This should give you a highly compressed jpg as the output.
Just for completeness, if you also want to control the quality (i.e. compression level) of the saved result, it seems to get a bit more complicated, as directly passing plt.savefig(..., quality=5) does not seem to have an effect on the output size and quality. So, on the one hand, one could either go the way of saving the result as a png first, then reloading it with PIL, then saving it again as a jpeg, using PIL's quality parameter – similar to what is suggested in Yann's answer.
On the other hand, one can avoid this deviation of loading and saving, by using BytesIO (following the answer to this question):
from io import BytesIO
import matplotlib.pyplot as plt
from PIL import Image
buf = BytesIO()
plt.plot(...) # Plot something here
plt.savefig(buf)
Image.open(buf).convert("RGB").save("testplot.jpg", quality=5)
I'm trying to do the Udacity mini project and I've got the latest version of the SKLearn library installed (20.2).
When I run:
from sklearn.decomposition import RandomizedPCA
I get the error:
ImportError: cannot import name 'RandomizedPCA' from 'sklearn.decomposition' (/Users/kintesh/Documents/udacity_ml/python3/venv/lib/python3.7/site-packages/sklearn/decomposition/__init__.py)
I actually even upgraded the version using:
pip3 install -U scikit-learn
Which upgraded from 0.20.0 to 0.20.2, which also uninstalled and reinstalled... so I'm not sure why it can't initialise sklearn.decomposition.
Are there any solutions here that might not result in completely uninstalling python3 from my machine?! Would ideally like to avoid that.
Any help would be thoroughly appreciated!
Edit:
I'm doing some digging and trying to fix this, and it appears as though the __init__.py file in the decomposition library on the SKLearn GitHub doesn't reference RandomizedPCA... has it been removed or something?
Link to the GitHub page
As it turns out, RandomizePCA() was depreciated in an older version of SKLearn and is simply a parameter in PCA().
You can fix this by changing the import statement to:
from sklearn.decomposition import PCA as RandomizedPCA
... and then your classifier looks like this:
pca = RandomizedPCA(n_components=n_components, svd_solver='randomized', whiten=True).fit(X_train)
However, if you're here because you're doing the Udacity Machine Learning course on Eigenfaces.py, you'll notice that the PIL library is also deprecated.
Unfortunately I don't have a solution for that one, but here's the GitHub issue page, and here's a kind hearted soul that used a Jupyter Notebook to solve their mini-project back when these repositories worked.
I hope this helps, and gives enough information for the next person to get into Machine Learning. If I get some time I might take a crack at recoding eigenfaces.py for SKLearn 0.20.2, but for now I'm just going to crack on with the rest of this course.
In addition to what #Aaraeus said, the PIL library has been forked to Pillow.
You can fix the PIL import error using
pip3 install pillow
Full disclosure: I am a total beginner when it comes to Python in particular and programming in general. So please bear with me.
Today I tried for the first time to play around some datasets on my own, outside of the sandboxed environment of online courses.
I downloaded both Anaconda and Rodeo (which somehow I feel more akin to than, say, Spyder or Jupyter).
Wrote down this code. It works in Spyder.
import numpy as np
import pandas as pd
myexcel="C:/Users/myname/folder/subfolder/file.xlsx"
xl=pd.ExcelFile(myexcel)
mydf=xl.parse(0)
print(mydf.head())
However, if I try to run the same code in Rodeo I get the following error message. Here, I am showing just a part.
----> 4 xl=pd.ExcelFile(myexcel)
ImportError: No module named 'xlrd'
I am getting that in Rodeo the script fail because it is missing the xlrd package, which admittedly after checking with help("modules") is not there. But I don't fully get the problem: if xlrd was quintessential to the correct execution of this code, then why doesn't it fail in Spyder?
I am trying to draw a graph networkx using python 3.6 with Jupyter notebook and the network package with anaconda. But the graph is not drawing per the documentation, I am just getting a deprecated message.
CODE:
import networkx as nx
import csv
import matplotlib as plt
G = nx.read_pajek('Hi-tech.net')
nx.draw(G)
MESSAGE:
MatplotlibDeprecationWarning: pyplot.hold is deprecated.
Future behavior will be consistent with the long-time default:
plot commands add elements without first clearing the
Axes and/or Figure.
b = plt.ishold()
Future behavior will be consistent with the long-time default:
plot commands add elements without first clearing the
Axes and/or Figure.
plt.hold(b)
warnings.warn("axes.hold is deprecated, will be removed in 3.0")
To avoid this warning, I just simply replace
nx.draw(G)
by
nx.draw_networkx(G)
My Python is 3.4, Jupyter '1.0.0' and networkx '1.11'.
I was able to get rid of the message by going into the networkx library and simply placing # in front of the lines which produced the error.
I would infer the .hold() function is no longer necessary, nor does it need ot be replaced
I could get nx.draw(G) to work by adding the following line of command:
%matplotlib inline
As error suggest ... I change nx_pylab.py at 611
# if cb.is_numlike(alpha):
if isinstance(alpha,numbers.Number):
I just commented out the line 365 of file __init__.py in Lib\site-packages\matplotlib\cbook which reads
#deprecated('3.0', 'isinstance(..., numbers.Number)')