not worked example

[katkoffff]

with the command 'asap gen_desc -f sps_all.xyz -p B-RSS soap' you can generate a file B-RSS.xyz
then we run the command: 'asap map -f B-RSS.xyz -dm '[*]' -nbs pca' and a message pops up:
load xyz file: B-RSS.xyz , a total of 5038 frames , a total of 97554 atoms , with elements: [5] .
Find the following descriptor names that match the specifications: ['SOAP-n4-l3-c2.4-g0.3']
Cannot find the specified descriptors from xyz
0
Remove raw desciptors..
removing the global descriptors from output xyz with the names: ['SOAP-n4-l3-c2.4-g0.3']
removing the atomic descriptors from output xyz with the names: []
Perform standard scaling of the design matrix. To turn it off use --no-scale
Using PCA ...
Start PCA for a design matrix with shape (0,)
Traceback (most recent call last):
File "", line 198, in _run_module_as_main
File "", line 88, in run_code
File "C:\ProgramData\miniconda3\envs\asap\Scripts\asap.exe_main.py", line 7, in
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\click\core.py", line 1161, in call
return self.main(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\click\core.py", line 1082, in main
rv = self.invoke(ctx)
^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\click\core.py", line 1697, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\click\core.py", line 1697, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\click\core.py", line 1443, in invoke
return ctx.invoke(self.callback, **ctx.params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\click\core.py", line 788, in invoke
return __callback(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\click\decorators.py", line 33, in new_func
return f(get_current_context(), *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\asaplib\cli\cmd_asap.py", line 577, in pca
map_process(ctx.obj, reduce_dict, axes, map_name)
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\asaplib\cli\func_asap.py", line 137, in map_process
proj = dreducer.fit_transform(obj['design_matrix'])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\asaplib\reducedim\dim_reducer.py", line 123, in fit_transform
X = self.engines[element].fit_transform(X)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\asaplib\reducedim\ml_pca.py", line 132, in fit_transform
self.fit(desc)
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\asaplib\reducedim\ml_pca.py", line 75, in fit
C_desc = self.scalecenter_matrix(desc)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\asaplib\reducedim\ml_pca.py", line 47, in scalecenter_matrix
print(self.scaler.fit(desc))
^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\sklearn\preprocessing_data.py", line 894, in fit
return self.partial_fit(X, y, sample_weight)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\sklearn\base.py", line 1389, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\sklearn\preprocessing_data.py", line 930, in partial_fit
X = validate_data(
^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\sklearn\utils\validation.py", line 2944, in validate_data
out = check_array(X, input_name="X", **check_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\asap\Lib\site-packages\sklearn\utils\validation.py", line 1093, in check_array
raise ValueError(msg)
ValueError: Expected 2D array, got 1D array instead:
array=[].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

that is, apparently, descriptors are not being read. How can this problem be fixed?