  Topological order has been proposed to go beyond Landau symmetry breaking
theory for more than twenty years. But it is still a challenging problem to
generally detect it in a generic many-body state. In this paper, we will
introduce a systematic numerical method based on tensor network to calculate
modular matrices in 2D systems, which can fully identify topological order with
gapped edge. Moreover, it is shown numerically that modular matrices, including
S and T matrices, are robust characterization to describe phase transitions
between topologically ordered states and trivial states, which can work as
topological order parameters. This method only requires local information of
one ground state in the form of a tensor network, and directly provides the
universal data (S and T matrices), without any non-universal contributions.
Furthermore it is generalizable to higher dimensions. Unlike calculating
topological entanglement entropy by extrapolating, which numerical complexity
is exponentially high, this method extracts a much more complete set of
topological data (modular matrices) with much lower numerical cost.
