[Fix] CuBIC, SPADE with scipy 1.14.0, deprecated .A attribute in scipy.sparse matrices`

[Moritz-Alexander-Kern]

Description

This PR addresses the issue arising from the deprecation of the .A attribute in scipy.sparse matrices starting with scipy==1.14.0 .

The .A attribute was used to convert sparse matrices to dense ndarray format.

However, this attribute was deprecated and has been removed in favor of the more explicit .toarray() method.

This deprecation was introduced with scipy==1.11.0. The PR removing it is #20499, see: scipy/scipy#20499

See also release notes: https://docs.scipy.org/doc/scipy/release/1.14.0-notes.html#expired-deprecations

Changes

SPADE

Replaced the use of .A with .toarray() in the _build_context function in the spade module.
This change preserves the functionality of converting a coo_matrix to a dense format without breaking the code due to the deprecated attribute.

This was done following the official recommendation, see property docstring:

    @property
    def A(self) -> np.ndarray:
        """DEPRECATED: Return a dense array.
        .. deprecated:: 1.11.0
            `.A` is deprecated and will be removed in v1.14.0.
            Use `.toarray()` instead.
        """
        if isinstance(self, sparray):
            message = ("`.A` is deprecated and will be removed in v1.14.0. "
                       "Use `.toarray()` instead.")
            warn(VisibleDeprecationWarning(message), stacklevel=2)
        return self.toarray()

Cubic

Ensure numeric precision of data ndarray is float64.

On Windows test fail with:

__________________________ CubicTestCase.test_cubic ___________________________

self = <elephant.test.test_cubic.CubicTestCase testMethod=test_cubic>

    def test_cubic(self):
    
        # Computing the output of CuBIC for the test data AnalogSignal
        xi, p_vals, k, test_aborted = cubic.cubic(
            self.data_signal, alpha=self.alpha)
    
        # Check the types of the outputs
        self.assertIsInstance(xi, int)
        self.assertIsInstance(p_vals, list)
        self.assertIsInstance(k, list)
    
        # Check that the number of tests is the output order of correlation
        self.assertEqual(xi, len(p_vals))
    
        # Check that all the first  xi-1 tests have not passed the
        # significance level alpha
        for p in p_vals[:-1]:
            self.assertGreater(self.alpha, p)
    
        # Check that the last p-value has passed the significance level
        self.assertGreater(p_vals[-1], self.alpha)
    
        # Check that the number of cumulant of the output is 3
        self.assertEqual(3, len(k))
    
        # Check the analytical constrain of the cumulants for which K_1<K_2
        self.assertGreater(k[1], k[0])
    
        # Check the computed order of correlation is the expected
        # from the test data
        self.assertEqual(xi, self.xi)
    
        # Computing the output of CuBIC for the test data Array
>       xi, p_vals, k, test_aborted = cubic.cubic(
            self.data_array, alpha=self.alpha)

elephant\test\test_cubic.py:74: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
elephant\cubic.py:142: in cubic
    pval = _H03xi(kappa, xi, L)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

kappa = [0.03, -0.1303980335803358, 3.000090626933894], xi = 1, L = 100000

    def _H03xi(kappa, xi, L):
        """
        Computes the p_value for testing  the :math:`H_0: k_3(data)<=k^*_{3,\\xi}`
        hypothesis of CuBIC in the stationary rate version
    
        Parameters
        -----
        kappa : list
            The first three cumulants of the populaton of spike trains
        xi : int
            The the maximum order of correlation :math:`\\xi` supposed in the
            hypothesis for which is computed the p value of :math:`H_0`
        L : float
            The length of the orginal population histogram on which is performed
            the CuBIC analysis
    
        Returns
        -----
        p : float
            The p-value of the hypothesis tests
        """
    
        # Check the order condition of the cumulants necessary to perform CuBIC
        if kappa[1] < kappa[0]:
>           raise ValueError(f"The null hypothesis H_0 cannot be tested: the "
                             f"population count histogram variance ({kappa[1]}) "
                             f"is less than the mean ({kappa[0]}). This can "
                             f"happen when the spike train population is not "
                             f"large enough or the bin size is small.")
E           ValueError: The null hypothesis H_0 cannot be tested: the population count histogram variance (-0.1303980335803358) is less than the mean (0.03). This can happen when the spike train population is not large enough or the bin size is small.

This seems similar to:
#227
#229

[coveralls]

coverage: 88.222%. remained the same
when pulling e7cf9e0 on INM-6:fix/spade_with_scipy_1_14_0
into bdd98ee on NeuralEnsemble:master.

[coveralls]

coverage: 87.892% (-0.3%) from 88.222%
when pulling c3a1aa6 on INM-6:fix/spade_with_scipy_1_14_0
into bdd98ee on NeuralEnsemble:master.

[coveralls]

coverage: 88.222%. remained the same
when pulling c3a1aa6 on INM-6:fix/spade_with_scipy_1_14_0
into bdd98ee on NeuralEnsemble:master.

[coveralls]

coverage: 87.892% (-0.3%) from 88.222%
when pulling 17d4609 on INM-6:fix/spade_with_scipy_1_14_0
into bdd98ee on NeuralEnsemble:master.

[coveralls]

coverage: 88.222%. remained the same
when pulling 17d4609 on INM-6:fix/spade_with_scipy_1_14_0
into bdd98ee on NeuralEnsemble:master.

[coveralls]

coverage: 88.222%. remained the same
when pulling c8fcf8f on INM-6:fix/spade_with_scipy_1_14_0
into bdd98ee on NeuralEnsemble:master.

[Moritz-Alexander-Kern]

build wheels