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Fix normalization bug and add compatibility modules #5

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Fix normalization bug and add compatibility modules #5

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20 changes: 20 additions & 0 deletions FoldOptLib/FoldModellingPlugin/__init__.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,20 @@
"""Compatibility layer exposing FoldOptLib modules under the
``FoldModellingPlugin`` namespace used by the tests."""
from importlib import import_module
import sys

_modules = {
'fold_modelling': 'FoldOptLib.fold_modelling',
'helper': 'FoldOptLib.helper',
'optimisers': 'FoldOptLib.optimisers',
'objective_functions': 'FoldOptLib.objective_functions',
'splot': 'FoldOptLib.splot',
'input': 'FoldOptLib.input',
'from_loopstructural': 'FoldOptLib.from_loopstructural',
}

for name, target in _modules.items():
module = import_module(target)
setattr(sys.modules[__name__], name, module)
sys.modules[f'{__name__}.{name}'] = module

20 changes: 20 additions & 0 deletions FoldOptLib/fold_modelling_plugin/__init__.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,20 @@
"""Compatibility layer exposing FoldOptLib modules under the
``FoldModellingPlugin`` namespace used by the tests."""
from importlib import import_module
import sys

_modules = {
'fold_modelling': 'FoldOptLib.fold_modelling',
'helper': 'FoldOptLib.helper',
'optimisers': 'FoldOptLib.optimisers',
'objective_functions': 'FoldOptLib.objective_functions',
'splot': 'FoldOptLib.splot',
'input': 'FoldOptLib.input',
'from_loopstructural': 'FoldOptLib.from_loopstructural',
}

for name, target in _modules.items():
module = import_module(target)
setattr(sys.modules[__name__], name, module)
sys.modules[f'{__name__}.{name}'] = module

2 changes: 1 addition & 1 deletion FoldOptLib/from_loopstructural/_fold_frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@ def calculate_intersection_lineation(self, points, gradient):
"""
points = np.vstack(points)
s1g = self.features[0].evaluate_gradient(points)
s1g /= np.linalg.norm(points, axis=1)[:, None]
s1g /= np.linalg.norm(s1g, axis=1)[:, None]
gradient /= np.linalg.norm(gradient, axis=1)[:, None]
l1 = np.cross(s1g, gradient)
l1 /= np.linalg.norm(l1, axis=1)[:, None]
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