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src/s/y/sympy-0.7.5/sympy/matrices/dense.py   sympy(Download)
        for i in range(self.cols):
            a.extend(self._mat[i::self.cols])
        return self._new(self.cols, self.rows, a)
 
    def _eval_conjugate(self):
        D: Dirac conjugation
        """
        out = self._new(self.rows, self.cols,
                lambda i, j: self[i, j].conjugate())
        return out
            # if a new method is added.
            raise ValueError("Inversion method unrecognized")
        return self._new(rv)
 
    def equals(self, other, failing_expression=False):
            L[i, i] = sqrt(self[i, i] -
                    sum(L[i, k]**2 for k in range(i)))
        return self._new(L)
 
    def _LDLdecomposition(self):
            D[i, i] = self[i, i] - sum(L[i, k]**2*D[k, k]
                for k in range(i))
        return self._new(L), self._new(D)
 
    def _lower_triangular_solve(self, rhs):

src/s/y/sympy-HEAD/sympy/matrices/dense.py   sympy(Download)
        for i in range(self.cols):
            a.extend(self._mat[i::self.cols])
        return self._new(self.cols, self.rows, a)
 
    def _eval_conjugate(self):
        D: Dirac conjugation
        """
        out = self._new(self.rows, self.cols,
                lambda i, j: self[i, j].conjugate())
        return out
            # if a new method is added.
            raise ValueError("Inversion method unrecognized")
        return self._new(rv)
 
    def equals(self, other, failing_expression=False):
            L[i, i] = sqrt(self[i, i] -
                    sum(L[i, k]**2 for k in range(i)))
        return self._new(L)
 
    def _LDLdecomposition(self):
            D[i, i] = self[i, i] - sum(L[i, k]**2*D[k, k]
                for k in range(i))
        return self._new(L), self._new(D)
 
    def _lower_triangular_solve(self, rhs):