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Correct codacy warnings

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This commit is contained in:
FilippoOlivo
2024-10-22 14:26:39 +02:00
committed by Nicola Demo
parent c9304fb9bb
commit 1bc1b3a580
15 changed files with 252 additions and 210 deletions
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134
tests/test_label_tensor/test_label_tensor.py
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@@ -4,29 +4,23 @@ import pytest
from pina.label_tensor import LabelTensor
data = torch.rand((20, 3))
labels_column = {
1: {
"name": "space",
"dof": ['x', 'y', 'z']
}
}
labels_row = {
0: {
"name": "samples",
"dof": range(20)
}
}
labels_column = {1: {"name": "space", "dof": ['x', 'y', 'z']}}
labels_row = {0: {"name": "samples", "dof": range(20)}}
labels_list = ['x', 'y', 'z']
labels_all = labels_column | labels_row
@pytest.mark.parametrize("labels", [labels_column, labels_row, labels_all, labels_list])
@pytest.mark.parametrize("labels",
[labels_column, labels_row, labels_all, labels_list])
def test_constructor(labels):
print(LabelTensor(data, labels))
def test_wrong_constructor():
with pytest.raises(ValueError):
LabelTensor(data, ['a', 'b'])
@pytest.mark.parametrize("labels", [labels_column, labels_all])
@pytest.mark.parametrize("labels_te", ['z', ['z'], {'space': ['z']}])
def test_extract_column(labels, labels_te):
@@ -37,6 +31,7 @@ def test_extract_column(labels, labels_te):
assert new.shape[0] == 20
assert torch.all(torch.isclose(data[:, 2].reshape(-1, 1), new))
@pytest.mark.parametrize("labels", [labels_row, labels_all])
@pytest.mark.parametrize("labels_te", [{'samples': [2]}])
def test_extract_row(labels, labels_te):
@@ -47,10 +42,14 @@ def test_extract_row(labels, labels_te):
assert new.shape[0] == 1
assert torch.all(torch.isclose(data[2].reshape(1, -1), new))
@pytest.mark.parametrize("labels_te", [
{'samples': [2], 'space': ['z']},
{'space': 'z', 'samples': 2}
])
@pytest.mark.parametrize("labels_te", [{
'samples': [2],
'space': ['z']
}, {
'space': 'z',
'samples': 2
}])
def test_extract_2D(labels_te):
labels = labels_all
tensor = LabelTensor(data, labels)
@@ -58,7 +57,8 @@ def test_extract_2D(labels_te):
assert new.ndim == tensor.ndim
assert new.shape[1] == 1
assert new.shape[0] == 1
assert torch.all(torch.isclose(data[2,2].reshape(1, 1), new))
assert torch.all(torch.isclose(data[2, 2].reshape(1, 1), new))
def test_extract_3D():
data = torch.rand(20, 3, 4)
@@ -72,10 +72,7 @@ def test_extract_3D():
"dof": range(4)
},
}
labels_te = {
'space': ['x', 'z'],
'time': range(1, 4)
}
labels_te = {'space': ['x', 'z'], 'time': range(1, 4)}
tensor = LabelTensor(data, labels)
new = tensor.extract(labels_te)
@@ -84,15 +81,13 @@ def test_extract_3D():
assert new.shape[0] == 20
assert new.shape[1] == 2
assert new.shape[2] == 3
assert torch.all(torch.isclose(
data[:, 0::2, 1:4].reshape(20, 2, 3),
new
))
assert torch.all(torch.isclose(data[:, 0::2, 1:4].reshape(20, 2, 3), new))
assert tensor2.ndim == tensor.ndim
assert tensor2.shape == tensor.shape
assert tensor.full_labels == tensor2.full_labels
assert new.shape != tensor.shape
def test_concatenation_3D():
data_1 = torch.rand(20, 3, 4)
labels_1 = ['x', 'y', 'z', 'w']
@@ -152,27 +147,28 @@ def test_concatenation_3D():
def test_summation():
lt1 = LabelTensor(torch.ones(20,3), labels_all)
lt2 = LabelTensor(torch.ones(30,3), ['x', 'y', 'z'])
lt1 = LabelTensor(torch.ones(20, 3), labels_all)
lt2 = LabelTensor(torch.ones(30, 3), ['x', 'y', 'z'])
with pytest.raises(RuntimeError):
LabelTensor.summation([lt1, lt2])
lt1 = LabelTensor(torch.ones(20,3), labels_all)
lt2 = LabelTensor(torch.ones(20,3), labels_all)
lt1 = LabelTensor(torch.ones(20, 3), labels_all)
lt2 = LabelTensor(torch.ones(20, 3), labels_all)
lt_sum = LabelTensor.summation([lt1, lt2])
assert lt_sum.ndim == lt_sum.ndim
assert lt_sum.shape[0] == 20
assert lt_sum.shape[1] == 3
assert lt_sum.full_labels == labels_all
assert torch.eq(lt_sum.tensor, torch.ones(20,3)*2).all()
lt1 = LabelTensor(torch.ones(20,3), labels_all)
lt2 = LabelTensor(torch.ones(20,3), labels_all)
assert torch.eq(lt_sum.tensor, torch.ones(20, 3) * 2).all()
lt1 = LabelTensor(torch.ones(20, 3), labels_all)
lt2 = LabelTensor(torch.ones(20, 3), labels_all)
lt3 = LabelTensor(torch.zeros(20, 3), labels_all)
lt_sum = LabelTensor.summation([lt1, lt2, lt3])
assert lt_sum.ndim == lt_sum.ndim
assert lt_sum.shape[0] == 20
assert lt_sum.shape[1] == 3
assert lt_sum.full_labels == labels_all
assert torch.eq(lt_sum.tensor, torch.ones(20,3)*2).all()
assert torch.eq(lt_sum.tensor, torch.ones(20, 3) * 2).all()
def test_append_3D():
data_1 = torch.rand(20, 3, 2)
@@ -187,6 +183,7 @@ def test_append_3D():
assert lt1.full_labels[1]['dof'] == range(3)
assert lt1.full_labels[2]['dof'] == ['x', 'y', 'z', 'w']
def test_append_2D():
data_1 = torch.rand(20, 2)
labels_1 = ['x', 'y']
@@ -199,12 +196,31 @@ def test_append_2D():
assert lt1.full_labels[0]['dof'] == range(400)
assert lt1.full_labels[1]['dof'] == ['x', 'y', 'z', 'w']
def test_vstack_3D():
data_1 = torch.rand(20, 3, 2)
labels_1 = {1:{'dof': ['a', 'b', 'c'], 'name': 'first'}, 2: {'dof': ['x', 'y'], 'name': 'second'}}
labels_1 = {
1: {
'dof': ['a', 'b', 'c'],
'name': 'first'
},
2: {
'dof': ['x', 'y'],
'name': 'second'
}
}
lt1 = LabelTensor(data_1, labels_1)
data_2 = torch.rand(20, 3, 2)
labels_1 = {1:{'dof': ['a', 'b', 'c'], 'name': 'first'}, 2: {'dof': ['x', 'y'], 'name': 'second'}}
labels_1 = {
1: {
'dof': ['a', 'b', 'c'],
'name': 'first'
},
2: {
'dof': ['x', 'y'],
'name': 'second'
}
}
lt2 = LabelTensor(data_2, labels_1)
lt_stacked = LabelTensor.vstack([lt1, lt2])
assert lt_stacked.shape == (40, 3, 2)
@@ -214,12 +230,13 @@ def test_vstack_3D():
assert lt_stacked.full_labels[1]['name'] == 'first'
assert lt_stacked.full_labels[2]['name'] == 'second'
def test_vstack_2D():
data_1 = torch.rand(20, 2)
labels_1 = { 1: {'dof': ['x', 'y'], 'name': 'second'}}
labels_1 = {1: {'dof': ['x', 'y'], 'name': 'second'}}
lt1 = LabelTensor(data_1, labels_1)
data_2 = torch.rand(20, 2)
labels_1 = { 1: {'dof': ['x', 'y'], 'name': 'second'}}
labels_1 = {1: {'dof': ['x', 'y'], 'name': 'second'}}
lt2 = LabelTensor(data_2, labels_1)
lt_stacked = LabelTensor.vstack([lt1, lt2])
assert lt_stacked.shape == (40, 2)
@@ -228,35 +245,36 @@ def test_vstack_2D():
assert lt_stacked.full_labels[0]['name'] == 0
assert lt_stacked.full_labels[1]['name'] == 'second'
def test_sorting():
data = torch.ones(20, 5)
data[:,0] = data[:,0]*4
data[:,1] = data[:,1]*2
data[:,2] = data[:,2]
data[:,3] = data[:,3]*5
data[:,4] = data[:,4]*3
data[:, 0] = data[:, 0] * 4
data[:, 1] = data[:, 1] * 2
data[:, 2] = data[:, 2]
data[:, 3] = data[:, 3] * 5
data[:, 4] = data[:, 4] * 3
labels = ['d', 'b', 'a', 'e', 'c']
lt_data = LabelTensor(data, labels)
lt_sorted = LabelTensor.sort_labels(lt_data)
assert lt_sorted.shape == (20,5)
assert lt_sorted.shape == (20, 5)
assert lt_sorted.labels == ['a', 'b', 'c', 'd', 'e']
assert torch.eq(lt_sorted.tensor[:,0], torch.ones(20) * 1).all()
assert torch.eq(lt_sorted.tensor[:,1], torch.ones(20) * 2).all()
assert torch.eq(lt_sorted.tensor[:,2], torch.ones(20) * 3).all()
assert torch.eq(lt_sorted.tensor[:,3], torch.ones(20) * 4).all()
assert torch.eq(lt_sorted.tensor[:,4], torch.ones(20) * 5).all()
assert torch.eq(lt_sorted.tensor[:, 0], torch.ones(20) * 1).all()
assert torch.eq(lt_sorted.tensor[:, 1], torch.ones(20) * 2).all()
assert torch.eq(lt_sorted.tensor[:, 2], torch.ones(20) * 3).all()
assert torch.eq(lt_sorted.tensor[:, 3], torch.ones(20) * 4).all()
assert torch.eq(lt_sorted.tensor[:, 4], torch.ones(20) * 5).all()
data = torch.ones(20, 4, 5)
data[:,0,:] = data[:,0]*4
data[:,1,:] = data[:,1]*2
data[:,2,:] = data[:,2]
data[:,3,:] = data[:,3]*3
data[:, 0, :] = data[:, 0] * 4
data[:, 1, :] = data[:, 1] * 2
data[:, 2, :] = data[:, 2]
data[:, 3, :] = data[:, 3] * 3
labels = {1: {'dof': ['d', 'b', 'a', 'c'], 'name': 1}}
lt_data = LabelTensor(data, labels)
lt_sorted = LabelTensor.sort_labels(lt_data, dim=1)
assert lt_sorted.shape == (20,4, 5)
assert lt_sorted.shape == (20, 4, 5)
assert lt_sorted.full_labels[1]['dof'] == ['a', 'b', 'c', 'd']
assert torch.eq(lt_sorted.tensor[:,0,:], torch.ones(20,5) * 1).all()
assert torch.eq(lt_sorted.tensor[:,1,:], torch.ones(20,5) * 2).all()
assert torch.eq(lt_sorted.tensor[:,2,:], torch.ones(20,5) * 3).all()
assert torch.eq(lt_sorted.tensor[:,3,:], torch.ones(20,5) * 4).all()
assert torch.eq(lt_sorted.tensor[:, 0, :], torch.ones(20, 5) * 1).all()
assert torch.eq(lt_sorted.tensor[:, 1, :], torch.ones(20, 5) * 2).all()
assert torch.eq(lt_sorted.tensor[:, 2, :], torch.ones(20, 5) * 3).all()
assert torch.eq(lt_sorted.tensor[:, 3, :], torch.ones(20, 5) * 4).all()

9
tests/test_label_tensor/test_label_tensor_01.py
View File

@@ -54,9 +54,8 @@ def test_extract_order():
label_to_extract = ['c', 'a']
tensor = LabelTensor(data, labels)
new = tensor.extract(label_to_extract)
expected = torch.cat(
(data[:, 2].reshape(-1, 1), data[:, 0].reshape(-1, 1)),
dim=1)
expected = torch.cat((data[:, 2].reshape(-1, 1), data[:, 0].reshape(-1, 1)),
dim=1)
assert new.labels == label_to_extract
assert new.shape[1] == len(label_to_extract)
assert torch.all(torch.isclose(expected, new))
@@ -91,6 +90,7 @@ def test_getitem():
assert tensor_view.labels == ['a', 'c']
assert torch.allclose(tensor_view, data[:, 0::2])
def test_getitem2():
tensor = LabelTensor(data, labels)
tensor_view = tensor[:5]
@@ -101,6 +101,7 @@ def test_getitem2():
tensor_view = tensor[idx]
assert tensor_view.labels == labels
def test_slice():
tensor = LabelTensor(data, labels)
tensor_view = tensor[:5, :2]
@@ -114,4 +115,4 @@ def test_slice():
tensor_view3 = tensor[:, 2]
assert tensor_view3.labels == labels[2]
assert torch.allclose(tensor_view3, data[:, 2].reshape(-1, 1))
assert torch.allclose(tensor_view3, data[:, 2].reshape(-1, 1))