ValueError: operands could not be broadcast

[AlbertoA3]

I try to run the PastML protocol using a tree with 562 tips, however when i start the run this notification comes to my shell

Traceback (most recent call last):
File "/opt/miniconda3/envs/pastmlenv/bin/pastml", line 8, in
sys.exit(main())
^^^^^^
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/acr.py", line 1124, in main
pastml_pipeline(**vars(params))
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/acr.py", line 543, in pastml_pipeline
acr_results = acr(forest=roots, columns=columns, column2states=column2states,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/acr.py", line 230, in acr
pool.map(func=_work, iterable=character2settings.keys())
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/multiprocessing/pool.py", line 367, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/multiprocessing/pool.py", line 774, in get
raise self._value
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
^^^^^^^^^^^^^^^^
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/acr.py", line 219, in _work
return ml_acr(forest=forest, character=character, prediction_method=prediction_method,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/ml.py", line 659, in ml_acr
optimise_likelihood(forest=forest, character=character, model=model,
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/ml.py", line 895, in optimise_likelihood
likelihood = optimize_likelihood_params(forest=forest, character=character, model=model,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/ml.py", line 220, in optimize_likelihood_params
log_lh_JC = -get_v(x0_JC)
^^^^^^^^^^^^
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/ml.py", line 207, in get_v
res = sum(get_bottom_up_loglikelihood(tree=tree, character=character, is_marginal=True, model=model, alter=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/ml.py", line 207, in
res = sum(get_bottom_up_loglikelihood(tree=tree, character=character, is_marginal=True, model=model, alter=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/ml.py", line 111, in get_bottom_up_loglikelihood
calc_node_bu_likelihood(node, allowed_state_feature, lh_feature, lh_sf_feature, lh_joint_state_feature,
File "/opt/miniconda3/envs/pastmlenv/lib/python3.12/site-packages/pastml/ml.py", line 131, in calc_node_bu_likelihood
child_likelihoods = get_pij(child.dist) * getattr(child, lh_feature)
~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~
ValueError: operands could not be broadcast together with shapes (10,10) (562,)

I currently use a MacOS terminal in the conda environment whit the comand line
pastml --tree AY3.1_test_rooted_raxml --data Database_test_AY3.1.csv --columns SequenceID.1 PHZ --html_compressed AY3.1_map.html --data_sep , -v

Im very new using this program hope you can help me

[annazhukova]

Dear @AlbertoA3 ,

Thank you for reporting this issue. Could you please tell me which PastML version you are using (you can check that with "pastml --version" command)?

[AlbertoA3]

Many thanks for your replay

I currently use the pastml 1.9.46 version

[annazhukova]

Dear @AlbertoA3 ,

Is there any chance you could give me an example of data for which this problem occurs (you can send it by email to anna.zhukova@pasteur.fr if you prefer)? It is hard for me to reproduce the problem and see its source otrherwise.

Thank you