Niko Savola
Niko Savola
@wforbom, @Dzupdu, and I were doing a school project on NNQS and found the physics for the _J1-J2 ground state example_ a bit misleading with the literature using antiferromagnetic structure...
On latest pandas versions, calling `epra.analyze_all_variations` results in the following warnings: ```python c:\pyepr\pyEPR\core_quantum_analysis.py:712: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future...
This PR adds dtypes to all the empty Pandas Series (there might be some more but these were all the ones used in `do_EPR_analysis`) Closes #107
This resolves the following warnings emerging during EPR analysis ``` pyepr\pyEPR\core_distributed_analysis.py:1307: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead....
Calling `eprh.hfss_report_full_convergence` sometimes results in _ValueError: Data has no positive values, and therefore can not be log-scaled._ In this case, the simulations did not converge into tight criterion, but behaved...
Running `eprh.do_EPR_analysis()` will result in the following deprecation warning on newest Pandas versions: ``` pyepr\pyEPR\core_distributed_analysis.py:1307: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future...
`calc_energy_electric` (and `calc_energy_magnetic`) are called in many places in DistributedAnalysis and currently you are supposed to transfer the result of these as a parameter like `U_E` and `U_H` to for...
Currently, I get ``` FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. Ljs...
Similarly to #103, I think junctions should be possible to give in `ProjectInfo` initialisation as arguments. Doing ```python pinfo.junctions['jAlice'] = {'Lj_variable':'Lj_alice', 'rect':'rect_alice', 'line': 'line_alice', 'Cj_variable':'Cj_alice'} pinfo.junctions['jBob'] = {'Lj_variable':'Lj_bob', 'rect':'rect_bob', 'line':...
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