[Fix] CuBIC, SPADE with scipy 1.14.0, deprecated .A attribute in `s…

…cipy.sparse` matrices` (#636)

* fix deprecated .A attribute on coo_matrix
* make precision for cubic explicit
* ensure float64 precision for data when using scipy.stats.kstat

.github/workflows/CI.yml

@@ -131,22 +131,22 @@ jobs:
       fail-fast: false
       matrix:
         # OS [ubuntu-latest, macos-latest, windows-latest]
-        os: [macos-11,macos-12]
+        os: [macos-12,macos-13]
         python-version: [3.11]
     steps:
       - name: Get current year-month
         id: date
         run: echo "date=$(date +'%Y-%m')" >> $GITHUB_OUTPUT
 
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4.1.6
 
       - name: Cache conda
         uses: actions/cache@v3
         with:
           path: ~/conda_pkgs_dir
           key: ${{ runner.os }}-conda-${{hashFiles('requirements/environment.yml') }}-${{ hashFiles('**/CI.yml') }}-${{ steps.date.outputs.date }}
 
-      - uses: conda-incubator/setup-miniconda@030178870c779d9e5e1b4e563269f3aa69b04081 # corresponds to v3.0.3
+      - uses: conda-incubator/setup-miniconda@a4260408e20b96e80095f42ff7f1a15b27dd94ca # corresponds to v3.0.4
         with:
           auto-update-conda: true
           python-version: ${{ matrix.python-version }}

elephant/cubic.py

@@ -237,5 +237,6 @@ def _kstat(data):
     """
     if len(data) == 0:
         raise ValueError('The input data must be a non-empty array')
-    moments = [scipy.stats.kstat(data, n=n) for n in [1, 2, 3]]
+    # Due to issues with precision, ensure float64 (default) is the precision of the data array. (scipy == 1.14.0)
+    moments = [scipy.stats.kstat(data.astype(np.float64), n=n) for n in [1, 2, 3]]
     return moments

elephant/spade.py

@@ -768,7 +768,7 @@ def _build_context(binary_matrix, winlen):
         (np.ones((len(windows_col)), dtype=bool),
          (windows_row, windows_col)),
         shape=(num_bins, winlen * num_neurons),
-        dtype=bool).A
+        dtype=bool).toarray()
     # Array containing all the possible attributes (each spike is indexed by
     # a number equal to neu idx*winlen + bin_idx)
     attributes = np.array(