Comments (5)
BenKaehler
commented on September 6, 2024
1
Hi @LenaFloerl, is this issue solved? Mind if I close it?
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BenKaehler
commented on September 6, 2024
Sorry @LenaFloerl! I forgot to update the readme with FlashWeave installation instructions.
I will do that when I get a spare moment, but in the mean time these commands worked for me (in the appropriate conda/mamba environment):
conda install -c conda-forge julia
julia -e 'using Pkg; Pkg.add("FlashWeave")'
julia -e 'using Pkg; Pkg.add("ArgParse")'
julia -e 'using Pkg; Pkg.add("GraphIO")'
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LenaFloerl
commented on September 6, 2024
Hi - great thanks! Works like a gem now, FlashWeave is super fast! 💎
Quick follow up question regarding the visualisation: trying to visualise the network now I got the plugin error:
(PowerIterationFailedConvergence(...), 'power iteration failed to converge within 100 iterations')
That seems to mean that I have multiple multiple eigenvalues with the same magnitude - is that something I can fix by playing around with the FlashWeave parameters to (slightly) change the network?
Thanks again! I really hope it's okay that I'm already using FlashWeave! ✨
Cheers!
Lena
Whole error message:
Traceback (most recent call last):
File "/Users/lfloerl/miniconda3/envs/qiime2-2023.2/lib/python3.8/site-packages/q2cli/commands.py", line 352, in __call__
results = action(**arguments)
File "<decorator-gen-393>", line 2, in visualise_network
File "/Users/lfloerl/miniconda3/envs/qiime2-2023.2/lib/python3.8/site-packages/qiime2/sdk/action.py", line 234, in bound_callable
outputs = self._callable_executor_(scope, callable_args,
File "/Users/lfloerl/miniconda3/envs/qiime2-2023.2/lib/python3.8/site-packages/qiime2/sdk/action.py", line 443, in _callable_executor_
ret_val = self._callable(output_dir=temp_dir, **view_args)
File "/Users/lfloerl/miniconda3/envs/qiime2-2023.2/lib/python3.8/site-packages/q2_makarsa/_visualisation.py", line 310, in visualise_network
annotate_node_stats(network)
File "/Users/lfloerl/miniconda3/envs/qiime2-2023.2/lib/python3.8/site-packages/q2_makarsa/_visualisation.py", line 287, in annotate_node_stats
ee = nx.eigenvector_centrality(network)
File "/Users/lfloerl/miniconda3/envs/qiime2-2023.2/lib/python3.8/site-packages/networkx/classes/backends.py", line 145, in wrapper
return func(*args, **kwds)
File "/Users/lfloerl/miniconda3/envs/qiime2-2023.2/lib/python3.8/site-packages/networkx/utils/decorators.py", line 766, in func
return argmap._lazy_compile(__wrapper)(*args, **kwargs)
File "<class 'networkx.utils.decorators.argmap'> compilation 17", line 4, in argmap_eigenvector_centrality_14
import inspect
File "/Users/lfloerl/miniconda3/envs/qiime2-2023.2/lib/python3.8/site-packages/networkx/algorithms/centrality/eigenvector.py", line 138, in eigenvector_centrality
raise nx.PowerIterationFailedConvergence(max_iter)
networkx.exception.PowerIterationFailedConvergence: (PowerIterationFailedConvergence(...), 'power iteration failed to converge within 100 iterations')
Plugin error from makarsa:
(PowerIterationFailedConvergence(...), 'power iteration failed to converge within 100 iterations')
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BenKaehler
commented on September 6, 2024
Hi @LenaFloerl,
I really hope it's okay that I'm already using FlashWeave! ✨
It is definitely ok. Thanks very much for testing it!
I've made the statistics calculations more defensive, which should fix this problem (although I could not test with the data I've seen).
I suspect you may run into more problems as you progress your current data set, so please share any that you find!
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LenaFloerl
commented on September 6, 2024
Hi @BenKaehler - thanks for following up! Yes, it worked well! 👏
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Related Issues (20)
- Include FlashWeave
- Run time and memory requirements HOT 3
- Lint R and Julia code HOT 3
- Add FlashWeave unit test HOT 1
- Add metadata filtering for FlashWeave
- Add metadata columns list parameter to flashweave
- Add multithreaded Julia to FlashWeave execution.
- Include comparison to existing tools in JOSS paper HOT 1
- Add community detection to the JOSS paper HOT 2
- Fix Louvaine unit test
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- node IDs do not correspond to feature IDs HOT 1
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- All about weights
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