def test_set_varm(adata):
    init_hash = joblib.hash(adata)

    dim0_size = np.random.randint(2, adata.shape[1] - 1)
    dim1_size = np.random.randint(1, 99)
    orig_varm_val = adata.varm["o"].copy()
    subset_idx = np.random.choice(adata.var_names, dim0_size, replace=False)

    subset = adata[:, subset_idx]
    assert subset.is_view
    subset.varm = dict(o=np.ones((dim0_size, dim1_size)))
    assert not subset.is_view
    assert np.all(orig_varm_val == adata.varm["o"])  # Checking for mutation
    assert np.all(subset.varm["o"] == np.ones((dim0_size, dim1_size)))

    subset = adata[:, subset_idx]
    subset_hash = joblib.hash(subset)
    with pytest.raises(ValueError):
        subset.varm = dict(o=np.ones((dim0_size + 1, dim1_size)))
    with pytest.raises(ValueError):
        subset.varm = dict(o=np.ones((dim0_size - 1, dim1_size)))
    # subset should not be changed by failed setting
    assert subset_hash == joblib.hash(subset)
    assert init_hash == joblib.hash(adata)
def gerrity_score(self):
        """
        Gerrity Score, which weights each cell in the contingency table by its observed relative frequency.
        :return:
        """
        k = self.table.shape[0]
        n = float(self.table.sum())
        p_o = self.table.sum(axis=0) / n
        p_sum = np.cumsum(p_o)[:-1]
        a = (1.0 - p_sum) / p_sum
        s = np.zeros(self.table.shape, dtype=float)
        for (i, j) in np.ndindex(*s.shape):
            if i == j:
                s[i, j] = 1.0 / (k - 1.0) * (np.sum(1.0 / a[0:j]) + np.sum(a[j:k - 1]))
            elif i < j:
                s[i, j] = 1.0 / (k - 1.0) * (np.sum(1.0 / a[0:i]) - (j - i) + np.sum(a[j:k - 1]))
            else:
                s[i, j] = s[j, i]
        return np.sum(self.table / float(self.table.sum()) * s)