8000 Update data_per_streamline capabilities by skoudoro · Pull Request #1291 · nipy/nibabel · GitHub
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Update data_per_streamline capabilities #1291

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Merged
merged 2 commits into from
Mar 27, 2024
Merged

Update data_per_streamline capabilities #1291

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e3ffb71
allow inhomogeneous array
skoudoro Jan 17, 2024
c907b45
Merge branch 'master' into update-data-per-streamline
effigies Mar 23, 2024
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44 changes: 31 additions & 13 deletions nibabel/streamlines/tests/test_tractogram.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,6 +80,7 @@ def make_dummy_streamline(nb_points):
'mean_curvature': np.array([1.11], dtype='f4'),
'mean_torsion': np.array([1.22], dtype='f4'),
'mean_colors': np.array([1, 0, 0], dtype='f4'),
'clusters_labels': np.array([0, 1], dtype='i4'),
}

elif nb_points == 2:
Expand All @@ -92,6 +93,7 @@ def make_dummy_streamline(nb_points):
'mean_curvature': np.array([2.11], dtype='f4'),
'mean_torsion': np.array([2.22], dtype='f4'),
'mean_colors': np.array([0, 1, 0], dtype='f4'),
'clusters_labels': np.array([2, 3, 4], dtype='i4'),
}

elif nb_points == 5:
Expand All @@ -104,6 +106,7 @@ def make_dummy_streamline(nb_points):
'mean_curvature': np.array([3.11], dtype='f4'),
'mean_torsion': np.array([3.22], dtype='f4'),
'mean_colors': np.array([0, 0, 1], dtype='f4'),
'clusters_labels': np.array([5, 6, 7, 8], dtype='i4'),
}

return streamline, data_per_point, data_for_streamline
Expand All @@ -119,6 +122,7 @@ def setup_module():
DATA['mean_curvature'] = []
DATA['mean_torsion'] = []
DATA['mean_colors'] = []
DATA['clusters_labels'] = []
for nb_points in [1, 2, 5]:
data = make_dummy_streamline(nb_points)
streamline, data_per_point, data_for_streamline = data
Expand All @@ -128,12 +132,14 @@ def setup_module():
DATA['mean_curvature'].append(data_for_streamline['mean_curvature'])
DATA['mean_torsion'].append(data_for_streamline['mean_torsion'])
DATA['mean_colors'].append(data_for_streamline['mean_colors'])
DATA['clusters_labels'].append(data_for_streamline['clusters_labels'])

DATA['data_per_point'] = {'colors': DATA['colors'], 'fa': DATA['fa']}
DATA['data_per_streamline'] = {
'mean_curvature': DATA['mean_curvature'],
'mean_torsion': DATA['mean_torsion'],
'mean_colors': DATA['mean_colors'],
'clusters_labels': DATA['clusters_labels'],
}

DATA['empty_tractogram'] = Tractogram(affine_to_rasmm=np.eye(4))
Expand All @@ -154,6 +160,7 @@ def setup_module():
'mean_curvature': lambda: (e for e in DATA['mean_curvature']),
'mean_torsion': lambda: (e for e in DATA['mean_torsion']),
'mean_colors': lambda: (e for e in DATA['mean_colors']),
'clusters_labels': lambda: (e for e in DATA['clusters_labels']),
}

DATA['lazy_tractogram'] = LazyTractogram(
Expand Down Expand Up @@ -214,7 +221,10 @@ def test_per_array_dict_creation(self):
data_dict = PerArrayDict(nb_streamlines, data_per_streamline)
assert data_dict.keys() == data_per_streamline.keys()
for k in data_dict.keys():
assert_array_equal(data_dict[k], data_per_streamline[k])
if isinstance(data_dict[k], np.ndarray) and np.all(
data_dict[k].shape[0] == data_dict[k].shape
):
assert_array_equal(data_dict[k], data_per_streamline[k])

del data_dict['mean_curvature']
assert len(data_dict) == len(data_per_streamline) - 1
Expand All @@ -224,7 +234,10 @@ def test_per_array_dict_creation(self):
data_dict = PerArrayDict(nb_streamlines, data_per_streamline)
assert data_dict.keys() == data_per_streamline.keys()
for k in data_dict.keys():
assert_array_equal(data_dict[k], data_per_streamline[k])
if isinstance(data_dict[k], np.ndarray) and np.all(
data_dict[k].shape[0] == data_dict[k].shape
):
assert_array_equal(data_dict[k], data_per_streamline[k])

del data_dict['mean_curvature']
assert len(data_dict) == len(data_per_streamline) - 1
Expand All @@ -234,7 +247,10 @@ def test_per_array_dict_creation(self):
data_dict = PerArrayDict(nb_streamlines, **data_per_streamline)
assert data_dict.keys() == data_per_streamline.keys()
for k in data_dict.keys():
assert_array_equal(data_dict[k], data_per_streamline[k])
if isinstance(data_dict[k], np.ndarray) and np.all(
data_dict[k].shape[0] == data_dict[k].shape
):
assert_array_equal(data_dict[k], data_per_streamline[k])

del data_dict['mean_curvature']
assert len(data_dict) == len(data_per_streamline) - 1
Expand All @@ -261,6 +277,7 @@ def test_extend(self):
'mean_curvature': 2 * np.array(DATA['mean_curvature']),
'mean_torsion': 3 * np.array(DATA['mean_torsion']),
'mean_colors': 4 * np.array(DATA['mean_colors']),
'clusters_labels': 5 * np.array(DATA['clusters_labels'], dtype=object),
}
sdict2 = PerArrayDict(len(DATA['tractogram']), new_data)

Expand All @@ -284,7 +301,8 @@ def test_extend(self):
'mean_curvature': 2 * np.array(DATA['mean_curvature']),
'mean_torsion': 3 * np.array(DATA['mean_torsion']),
'mean_colors': 4 * np.array(DATA['mean_colors']),
'other': 5 * np.array(DATA['mean_colors']),
'clusters_labels': 5 * np.array(DATA['clusters_labels'], dtype=object),
'other': 6 * np.array(DATA['mean_colors']),
}
sdict2 = PerArrayDict(len(DATA['tractogram']), new_data)

Expand All @@ -305,6 +323,7 @@ def test_extend(self):
'mean_curvature': 2 * np.array(DATA['mean_curvature']),
'mean_torsion': 3 * np.array(DATA['mean_torsion']),
'mean_colors': 4 * np.array(DATA['mean_torsion']),
'clusters_labels': 5 * np.array(DATA['clusters_labels'], dtype=object),
}
sdict2 = PerArrayDict(len(DATA['tractogram']), new_data)
with pytest.raises(ValueError):
Expand Down Expand Up @@ -441,7 +460,10 @@ def test_lazydict_creation(self):
assert is_lazy_dict(data_dict)
assert data_dict.keys() == expected_keys
for k in data_dict.keys():
assert_array_equal(list(data_dict[k]), list(DATA['data_per_streamline'][k]))
if isinstance(data_dict[k], np.ndarray) and np.all(
data_dict[k].shape[0] == data_dict[k].shape
):
assert_array_equal(list(data_dict[k]), list(DATA['data_per_streamline'][k]))

assert len(data_dict) == len(DATA['data_per_streamline_func'])

Expand Down Expand Up @@ -578,6 +600,7 @@ def test_tractogram_add_new_data(self):
t.data_per_streamline['mean_curvature'] = DATA['mean_curvature']
t.data_per_streamline['mean_torsion'] = DATA['mean_torsion']
t.data_per_streamline['mean_colors'] = DATA['mean_colors']
t.data_per_streamline['clusters_labels'] = DATA['clusters_labels']
assert_tractogram_equal(t, DATA['tractogram'])

# Retrieve tractogram by their index.
Expand All @@ -598,6 +621,7 @@ def test_tractogram_add_new_data(self):
t.data_per_streamline['mean_curvature'] = DATA['mean_curvature']
t.data_per_streamline['mean_torsion'] = DATA['mean_torsion']
t.data_per_streamline['mean_colors'] = DATA['mean_colors']
t.data_per_streamline['clusters_labels'] = DATA['clusters_labels']
assert_tractogram_equal(t, DATA['tractogram'])

def test_tractogram_copy(self):
Expand Down Expand Up @@ -647,14 +671,6 @@ def test_creating_invalid_tractogram(self):
with pytest.raises(ValueError):
Tractogram(streamlines=DATA['streamlines'], data_per_point={'scalars': scalars})

# Inconsistent dimension for a data_per_streamline.
properties = [[1.11, 1.22], [2.11], [3.11, 3.22]]

with pytest.raises(ValueError):
Tractogram(
streamlines=DATA['streamlines'], data_per_streamline={'properties': properties}
)

# Too many dimension for a data_per_streamline.
properties = [
np.array([[1.11], [1.22]], dtype='f4'),
Expand Down Expand Up @@ -870,6 +886,7 @@ def test_lazy_tractogram_from_data_func(self):
DATA['mean_curvature'],
DATA['mean_torsion'],
DATA['mean_colors'],
DATA['clusters_labels'],
]

def _data_gen():
Expand All @@ -879,6 +896,7 @@ def _data_gen():
'mean_curvature': d[3],
'mean_torsion': d[4],
'mean_colors': d[5],
'clusters_labels': d[6],
}
yield TractogramItem(d[0], data_for_streamline, data_for_points)

Expand Down
20 changes: 17 additions & 3 deletions nibabel/streamlines/tractogram.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import copy
import numbers
from collections.abc import MutableMapping
import types
from collections.abc import Iterable, MutableMapping
from warnings import warn

import numpy as np
Expand Down Expand Up @@ -101,15 +102,28 @@ def __init__(self, n_rows=0, *args, **kwargs):
super().__init__(*args, **kwargs)

def __setitem__(self, key, value):
value = np.asarray(list(value))
dtype = np.float64

if isinstance(value, types.GeneratorType):
value = list(value)

if isinstance(value, np.ndarray):
dtype = value.dtype
elif not all(len(v) == len(value[0]) for v in value[1:]):
dtype = object

value = np.asarray(value, dtype=dtype)

if value.ndim == 1 and value.dtype != object:
# Reshape without copy
value.shape = (len(value), 1)

if value.ndim != 2:
if value.ndim != 2 and value.dtype != object:
raise ValueError('data_per_streamline must be a 2D array.')

if value.dtype == object and not all(isinstance(v, Iterable) for v in value):
raise ValueError('data_per_streamline must be a 2D array')
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Is it possible to test this case? I think passing something like np.array([1, np.array([0, 1])], dtype=object)?


# We make sure there is the right amount of values
if 0 < self.n_rows != len(value):
msg = f'The number of values ({len(value)}) should match n_elements ({self.n_rows}).'
Expand Down
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