I'd like to make a graph that looks similar to this.
The docs make it clear how to make the 2d version but I'm struggling to figure out how to make this type of graph.
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I am making interactive worksheets in Jupyter Notebook, introducing people to NetworkX. Suddenly, the nx.draw() command is not creating an output of a graph. It was working fine before, and I haven't changed anything about the code I was using. For example, one block of code that was working fine before was:
import networkx as nx
G2 = nx.Graph()
G2.add_nodes_from([1,2,3,4,5,6,7])
edgelist = [(1,4),(1,6),(1,7),(2,5),(2,6),(3,7),(4,6),(4,7),(5,7),(6,7)]
G2.add_edges_from(edgelist)
nx.draw_networkx(G2, with_labels=True, node_color = 'y')
Now, no errors come up, but neither does a graph. I have looked and seen that other people fixed this issue bu using plt.show() but I don't understand why I suddenly need to include that when I didn't have to before. Does anyone know a way I can avoid importing matplotlib and using the plt.show() command?
Perhaps the issue is to do with some other package I have or something to do with jupyter notebook?
EDIT: It has been pointed out to me that matplotlib is a package dependency so technically there is no way of drawing a graph in networkx without using matplotlib. I understand that, but is there a way to do it without explicitly calling matplotlib?
Im using a Sklearn for my machine learning and my question is how can i see my process of my taining?
If i use Tensoflow i can see my loading process with Tensorboard. But does Sklearn have something like this?
As pointed out in the comments, you can use matplotlib. There are plenty of tutorials of how to create a plot updating in real-time during your training.
However, personally I found these options pretty cumbersome. I instead chose to use the PyTorch interface to tensorboard.
That works like a charm and you can just pass in numpy loss values.
Here's how to get started: https://pytorch.org/docs/stable/tensorboard.html
I know spectrogram can be plotted using different functions of the different libraries in python. In matplotlib, plyplot plots spectrogram directly using time-series audio data but librosa first applies short Fourier transform on data before plotting spectrogram.
But I am still confused between two.
Please tell me the detailed difference between
1.librosa.dispay.specshow()
2.matplotlib.pyplot.specgram()
I have searched the internet a lot but couldn't find any relevant information though.
According to librosa documentation, all librosa plotting functions are depends on matplotlib.
All of librosa’s plotting functions rely on matplotlib. To demonstrate everything we can do, it will help to import matplotlib’s pyplot API here.
What are the different alternatives available for 3D plotting in python matplotlib? I am using scatterplot and would like to know more about other types of 3d plots. I checked the site http://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html but could not get effective results as I was not able to interpret results effectively. Please let me know of some good 3D plots. Also I would like to try multidimensional plotting and have heard that scatterplot matrix is very effective. Please give some good examples demonstrating the same.
I have a lot of data that comes from some experiment, just one column "measure" and would like to calculate ROC/AUC curves. I use matplotlib and had a look, but found no way to do with it. I wonder if anyone can share experiences with matplotlib or with any other open source easy to use library for ROC/AUC curves.
Matplotlib is mostly used for plotting things, so you'd need to calculate the curves first and then plot them with matplotlib. There seem to be multiple relevant Python modules:
scipy roc_curve
PyROC
CROC
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