Merge pull request #125 from coganlab/mri_explicit

Mri explicit

.github/workflows/Package.yml

@@ -16,18 +16,26 @@ jobs:
         with:
           python-version: '3.10'
           cache: 'pip'
-      # Publish (moved to end of document later)
-      - name: Build source distribution
+      - name: Setup pip
         run: |
           python -m pip install --upgrade pip
           python -m pip install build --user
+          python -m pip install cibuildwheel
+      - name: Build source distribution
+        run: |
           python -m build --sdist
-      - name: Store the distribution packages
+      - name: Build wheel
+        run: |
+          python -m cibuildwheel --output-dir dist/
+        env:
+          CIBW_BUILD: cp310-*
+      - name: Store dist & wheel
         uses: actions/upload-artifact@v3
         with:
-          name: python-package-distributions
+          name: python-package-dist-wheel
           path: dist/
 
+
   publish-to-testpypi:
     name: Publish Python 🐍 distribution 📦 to TestPyPI
     needs:
@@ -42,10 +50,10 @@ jobs:
       id-token: write  # IMPORTANT: mandatory for trusted publishing
 
     steps:
-    - name: Download all the dists
+    - name: Download all the dists & wheels
       uses: actions/download-artifact@v3
       with:
-        name: python-package-distributions
+        name: python-package-dist-wheel
         path: dist/
     - name: Publish distribution 📦 to TestPyPI
       uses: pypa/gh-action-pypi-publish@release/v1
@@ -65,7 +73,7 @@ jobs:
       - name: Download all the dists
         uses: actions/download-artifact@v3
         with:
-          name: python-package-distributions
+          name: python-package-dist-wheel
           path: dist/
       - name: Publish package distributions to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1

.github/workflows/Python-CI.yml

@@ -33,6 +33,9 @@ jobs:
         - os: ubuntu-latest
           python: '3.10'
           kind: pip
+        - os: ubuntu-latest
+          python: '3.13'
+          kind: pip
     environment: release
     steps:
       - uses: actions/checkout@v4
@@ -51,6 +54,7 @@ jobs:
             **/requirements.txt
             **/pyproject.toml
       - if: matrix.os != 'windows-latest'
+        shell: bash {0}
         run: |
           python -m pip install --upgrade pip
           pip install -e . -v
@@ -62,16 +66,17 @@ jobs:
       - run: ./.github/check_qt_import.sh $MNE_QT_BACKEND
         if: ${{ env.MNE_QT_BACKEND != '' }}
       - name: List packages
+        shell: bash {0}
         run: |
           python -c "import mne; mne.sys_info()"
       - name: fetch mne datasets
         run: python -c "import mne; mne.datasets.misc.data_path(); mne.datasets.epilepsy_ecog.data_path(); mne.datasets.sample.data_path()"
-        if: matrix.os == 'ubuntu-latest'
+        if: matrix.os == 'ubuntu-latest' && matrix.python == '3.10'
       - name: Test with pytest
         run: |
           pip install -r envs/dev/testing-reqs.txt
           pytest tests ieeg -v --cov=. --doctest-modules --pycodestyle --cov-report xml
-        if: matrix.os == 'ubuntu-latest'
+        if: matrix.os == 'ubuntu-latest' && matrix.python == '3.10'
       - name: Upload coverage reports to Codecov
         uses: codecov/codecov-action@v3
-        if: matrix.os == 'ubuntu-latest'
+        if: matrix.os == 'ubuntu-latest' && matrix.python == '3.10'

.gitignore

@@ -10,3 +10,9 @@ docs/auto_examples/*
 docs/ieeg*
 docs/modules.rst
 docs/sg_execution_times.rst
+
+# Files compiled and built upon installing ieeg
+ieeg.egg-info/*
+ieeg/**/*.pyd
+ieeg/**/*.c
+!ieeg/calc/_fast/ufuncs.c

.readthedocs.yaml

@@ -22,7 +22,7 @@ build:
     # golang: "1.19"
   jobs:
     pre_build:
-      - pip install --upgrade pip myst_parser sphinx_gallery SQLAlchemy sphinxcontrib-matlabdomain sphinx_rtd_theme sphinx_copybutton memory_profiler cython
+      - pip install --upgrade pip myst_parser sphinx_gallery traitlets SQLAlchemy sphinxcontrib-matlabdomain sphinx_rtd_theme sphinx_copybutton memory_profiler cython
       - sphinx-apidoc -f -o docs/references -e -T -t docs/_templates ieeg
       - python -c "import mne; mne.sys_info()"
 

README.md

@@ -12,7 +12,7 @@ A repo of current preprocessing pipelines for the [Cogan Lab](https://www.coganl
 
 ## Pipeline Functionality
 
-[![Python (3.10) on Windows/Linux](https://github.com/coganlab/IEEG_Pipelines/actions/workflows/Python-CI.yml/badge.svg)](https://github.com/coganlab/IEEG_Pipelines/actions/workflows/Python-CI.yml)
+[![Python (3.10 - 3.13) on Windows/Linux/Mac](https://github.com/coganlab/IEEG_Pipelines/actions/workflows/Python-CI.yml/badge.svg)](https://github.com/coganlab/IEEG_Pipelines/actions/workflows/Python-CI.yml)
 
 [![MATLAB latest](https://github.com/coganlab/IEEG_Pipelines/actions/workflows/MATLAB-CI.yml/badge.svg)](https://github.com/coganlab/IEEG_Pipelines/actions/workflows/MATLAB-CI.yml)
 
@@ -33,15 +33,15 @@ A repo of current preprocessing pipelines for the [Cogan Lab](https://www.coganl
 
 ### Python
 
-Version 3.10 supported
+Version 3.10 - 3.13 supported
 
 #### Conda
 
 1. Install Anaconda
 2. Create an anaconda environment with python and pip packages installed
     
      ```bash
-     conda create -n <YOUR_NAME> python<3.13 pip
+     conda create -n <YOUR_NAME> python<3.14 pip
      ```
 3. Activate the environment
 

envs/requirements.txt

@@ -1,6 +1,6 @@
 bids>=0.0
 mne
-mne-bids<=0.14
+mne-bids>=0.16
 numpy
 matplotlib
 scipy

ieeg/calc/fast.py

@@ -117,10 +117,18 @@ def mixup(arr: np.ndarray, obs_axis: int, alpha: float = 1.,
            [[16.        , 17.        , 18.        , 19.        ],
             [20.        , 21.        , 22.        , 23.        ]]])
     """
-    if seed is None:
-        seed = np.random.randint(0, 2**16 - 1)
 
-    cmixup(arr, obs_axis, alpha, seed)
+    if arr.ndim > 3:
+        for i in range(arr.shape[0]):
+            mixup(arr[i], obs_axis - 1, alpha, seed)
+    elif arr.ndim == 1:
+        raise ValueError("Array must have at least 2 dimensions")
+    else:
+        if seed is None:
+            seed = np.random.randint(0, 2 ** 16 - 1)
+        if obs_axis == 0:
+            arr = arr.swapaxes(1, obs_axis)
+        cmixup(arr, 1, alpha, seed)
 
 
 def norm(arr: np.ndarray, obs_axis: int = -1) -> None:
@@ -178,7 +186,12 @@ def mean_diff(group1: np.ndarray, group2: np.ndarray,
     >>> mean_diff(group1, group2, axis=1)
     array([ 0., 30.,  0.,  5.,  0.])
     """
-    in1 = np.moveaxis(group1, axis, -1)
-    in2 = np.moveaxis(group2, axis, -1)
+    assert group1.ndim == group2.ndim, ("Arrays must have the same number of"
+                                        "dimensions")
+    if group1.ndim > 1 and axis not in (group1.ndim - 1, -1):
+        in1 = np.moveaxis(group1, axis, -1)
+        in2 = np.moveaxis(group2, axis, -1)
+    else:
+        in1, in2 = group1, group2
 
     return _md(in1, in2)

ieeg/calc/mat.py

@@ -5,9 +5,7 @@
 from ieeg.calc.fast import concatenate_arrays
 
 import numpy as np
-from numpy.matlib import repmat
 from numpy.typing import ArrayLike
-from numpy.core.numeric import normalize_axis_tuple
 
 import ieeg
 
@@ -45,6 +43,56 @@ def iter_nest_dict(d: dict, _lvl: int = 0, _coords=()):
             yield _coords + (k,), v
 
 
+def lcs(*strings: str) -> str:
+    """Find the longest common substring in a list of strings.
+
+    Parameters
+    ----------
+    *strings : str
+        The strings to find the longest common substring of.
+
+    Returns
+    -------
+    str
+        The longest common substring in the list of strings.
+
+    Examples
+    --------
+    >>> lcs('ABAB')
+    'ABAB'
+    >>> lcs('ABAB', 'BABA')
+    'ABA'
+    >>> lcs('ABAB', 'BABA', 'ABBA')
+    'AB'
+    """
+    if not strings:
+        return ""
+
+    def _lcs_two_strings(s1, s2):
+        n, m = len(s1), len(s2)
+        dp = [[0] * (m + 1) for _ in range(n + 1)]
+        max_len = 0
+        end_pos = 0
+
+        for i in range(1, n + 1):
+            for j in range(1, m + 1):
+                if s1[i - 1] == s2[j - 1]:
+                    dp[i][j] = dp[i - 1][j - 1] + 1
+                    if dp[i][j] > max_len:
+                        max_len = dp[i][j]
+                        end_pos = i
+
+        return s1[end_pos - max_len:end_pos]
+
+    common_substr = strings[0]
+    for string in strings[1:]:
+        common_substr = _lcs_two_strings(common_substr, string)
+        if not common_substr:
+            break
+
+    return common_substr
+
+
 class LabeledArray(np.ndarray):
     """ A numpy array with labeled dimensions, acting like a dictionary.
 
@@ -73,6 +121,7 @@ class LabeledArray(np.ndarray):
     Examples
     --------
     >>> import numpy as np
+    >>> np.set_printoptions(legacy='1.21')
     >>> from ieeg.calc.mat import LabeledArray
     >>> arr = np.ones((2, 3, 4), dtype=int)
     >>> labels = (('a', 'b'), ('c', 'd', 'e'), ('f', 'g', 'h', 'i'))
@@ -240,7 +289,7 @@ def swapaxes(self, axis1, axis2):
         return LabeledArray(arr, new)
 
     def transpose(self, axes):
-        axes = normalize_axis_tuple(axes, self.ndim)
+        axes = np._core.numeric.normalize_axis_tuple(axes, self.ndim)
         new_labels = [self.labels[i] for i in axes]
         arr_t = super(LabeledArray, self).transpose(axes)
         return LabeledArray(arr_t, new_labels)
@@ -909,7 +958,8 @@ def __matmul__(self, other):
         return result
 
     def __add__(self, other):
-        result = super().__add__(self.delimiter).__add__(other)
+        result = self.view(np.char.chararray).__add__(
+            self.delimiter).__add__(other.view(np.char.chararray))
         return Labels(result)
 
     def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
@@ -945,8 +995,8 @@ def decompose(self) -> list['Labels', ...]:
         for i, dim in enumerate(self.shape):
             for j in range(dim):
                 row = np.take(self, j, axis=i).flatten().astype(str)
-                common = _longest_common_substring(tuple(map(
-                    lambda x: tuple(x.split(self.delimiter, )), row)))
+                splitted = row.split(self.delimiter)
+                common = functools.reduce(np.intersect1d, splitted)
                 if len(common) == 0:
                     common = np.unique(row).tolist()
                 new_labels[i][j] = self.delimiter.join(common)
@@ -1039,25 +1089,29 @@ def _make_array_unique(arr: np.ndarray, delimiter: str) -> np.ndarray:
     return out
 
 
-def _longest_common_substring(strings: tuple[tuple[str]]) -> tuple[str]:
-    matrix = [[] for _ in range(len(strings))]
-    for i in range(len(strings) - 1):
-        matrix[i] = _lcs(strings[i], strings[i + 1])
-    else:
-        matrix[-1] = [True for _ in range(len(strings[-1]))]
-    return np.array(strings[0])[np.all(matrix, axis=0)].tolist()
-
+def is_broadcastable(shp1: tuple[int, ...], shp2: tuple[int, ...]):
+    """Check if two shapes are broadcastable.
 
-@functools.lru_cache(None)
-def _lcs(s1: tuple, s2: tuple) -> list[bool]:
-    matrix = [False for _ in range(len(s1))]
-    for i in range(len(s1)):
-        if s1[i] == s2[i]:
-            matrix[i] = True
-    return matrix
+    Parameters
+    ----------
+    shp1 : tuple[int, ...]
+        The first shape.
+    shp2 : tuple[int, ...]
+        The second shape.
 
+    Returns
+    -------
+    bool
 
-def is_broadcastable(shp1: tuple[int, ...], shp2: tuple[int, ...]):
+    Examples
+    --------
+    >>> is_broadcastable((2, 3), (2, 3))
+    True
+    >>> is_broadcastable((2, 3), (3, 2))
+    False
+    >>> is_broadcastable((2, 3), (2, 1))
+    True
+    """
 
     ndim1 = len(shp1)
     ndim2 = len(shp2)
@@ -1348,13 +1402,12 @@ def get_elbow(data: np.ndarray) -> int:
     """
     nPoints = len(data)
     allCoord = np.vstack((range(nPoints), data)).T
-    np.array([range(nPoints), data])
     firstPoint = allCoord[0]
     lineVec = allCoord[-1] - allCoord[0]
     lineVecNorm = lineVec / np.sqrt(np.sum(lineVec ** 2))
     vecFromFirst = allCoord - firstPoint
-    scalarProduct = np.sum(vecFromFirst * repmat(
-        lineVecNorm, nPoints, 1), axis=1)
+    scalarProduct = np.sum(vecFromFirst * np.tile(lineVecNorm,
+                                                  (nPoints, 1)), axis=1)
     vecFromFirstParallel = np.outer(scalarProduct, lineVecNorm)
     vecToLine = vecFromFirst - vecFromFirstParallel
     distToLine = np.sqrt(np.sum(vecToLine ** 2, axis=1))
@@ -1410,6 +1463,7 @@ def _cat_test():
     Examples
     --------
     >>> import numpy as np
+    >>> np.set_printoptions(legacy='1.21')
     >>> a = np.array([[1, 2, 3]])
     >>> b = np.array([[4, 5]])
     >>> c = np.array([[6, 7, 8, 9]])