astropy | Astronomy and astrophysics core library | Dataset library

The officially supported option is to use settings profiles to globally enable settings(database=None).

Alternatively, you could -p no:hypothesispytest, but note that this is not a documented and supported approach - it may be changed to the more obvious -p no:hypothesis, for example.

The error error: [Errno 2] No such file or directory: '/opt/anaconda3/lib/python3.8/site-packages/rtree/lib' was caused by py2app trying to build the program bundle using a non-existent interpreter. This means that even if you try to uninstall a manager like Anaconda, it still has option logs somewhere on your mac.

The fix:

1. Open the terminal and type the command type -a python.

• You will see similar lines

The method you're using frame_latlon_and_rates already returns latitudinal and longitudinal angular velocities which you're ignoring.

The answer was to use OpenCV to debayer the image and then normalise the data.

Here's a way to do what you want:

1. First after loading your adjacency matrices to pandas you can convert them to two different graphs with nx.convert_matrix.from_pandas_adjacency
2. You can then join the two graph into a single graph by using nx.disjoint_union. The nodes of both graphs are basically concatenated onto a single graph (see more here).
3. Once you have the full graph, you can randomly draw nodes from the cortex part of the full graph with a 0.01 probability.
4. Similarly you can draw the same number of nodes on the cerebellum part of the graph to act as recipients of the connection.
5. Finally you can create the edges between the chosen nodes on both sides.
6. And you can get your adjacency matrix from the final graph by using adj_matrix_full=nx.linalg.graphmatrix.adjacency_matrix(full_g,weight=None)

See full code below for more details:

It looks like, in calculate_community_modularity, you use greedy_modularity_communities to create a dict, modularity_dict, which maps a node in your graph to a community. If I understand correctly, you can take each subgraph community in modularity_dict and pass it into shannon_entropy to calculate the entropy for that community.

this is pseudo code, so there may be some errors. This should convey the principle, though.

after running calculate_community_modularity, you have a dict like this, where the key is each node, and the value is that which the community belongs to

There are two problems with your code. The first is that you are performing vector operation on list which gives you the first error in the line mean = sum(wavelengths*intensities)/n. Therefore, you should use np.array instead. The second is that you take math.exp on python list which again throws an error as it takes a real number, so you should use np.exp here instead.

The following code solves your problem:

In the example from the question, only one table column needs to be updated and with a different size. The Table class has a replace_column function to help with this.

You'll need to be using the direct astropy.io.fits interface instead of the high-level Table interface. From there something like this might work: