def deploy_tokens_and_fund_accounts(
    token_amount: int,
    number_of_tokens: int,
    deploy_service: BlockChainService,
    participants: typing.List[typing.Address],
    contract_manager: ContractManager,
) -> typing.List[typing.TokenAddress]:
    """ Deploy `number_of_tokens` ERC20 token instances with `token_amount` minted and
    distributed among `blockchain_services`. Optionally the instances will be registered with
    the raiden registry.

    Args:
        token_amount (int): number of units that will be created per token
        number_of_tokens (int): number of token instances that will be created
        deploy_service (BlockChainService): the blockchain connection that will deploy
        participants (list(address)): participant addresses that will receive tokens
    """
    result = list()
    for _ in range(number_of_tokens):
        token_address = deploy_contract_web3(
            CONTRACT_HUMAN_STANDARD_TOKEN,
            deploy_service.client,
            contract_manager=contract_manager,
            constructor_arguments=(token_amount, 2, "raiden", "Rd"),
        )

        result.append(token_address)

        # only the creator of the token starts with a balance (deploy_service),
        # transfer from the creator to the other nodes
        for transfer_to in participants:
            deploy_service.token(token_address).transfer(
                to_address=transfer_to, amount=token_amount // len(participants)
            )

    return result
def felosses(losses, coeffs, title='', log=True, ax=0):
    """plot iron losses with steinmetz or jordan approximation
    Args:
      losses: dict with f, B, pfe values
      coeffs: list with steinmetz (cw, alpha, beta) or
              jordan (cw, alpha, ch, beta, gamma) coeffs
      title: title string
      log: log scale for x and y axes if True

    """
    import femagtools.losscoeffs as lc
    if ax == 0:
        ax = plt.gca()

    fo = losses['fo']
    Bo = losses['Bo']
    B = plt.np.linspace(0.9*np.min(losses['B']),
                        1.1*0.9*np.max(losses['B']))

    for i, f in enumerate(losses['f']):
        pfe = [p for p in np.array(losses['pfe'])[i] if p]
        if f > 0:
            if len(coeffs) == 5:
                ax.plot(B, lc.pfe_jordan(f, B, *coeffs, fo=fo, Bo=Bo))
            elif len(coeffs) == 3:
                ax.plot(B, lc.pfe_steinmetz(f, B, *coeffs, fo=fo, Bo=Bo))
        plt.plot(losses['B'][:len(pfe)], pfe,
                 marker='o', label="{} Hz".format(f))

    ax.set_title("Fe Losses/(W/kg) " + title)
    if log:
        ax.set_yscale('log')
        ax.set_xscale('log')
    ax.set_xlabel("Flux Density [T]")
    # plt.ylabel("Pfe [W/kg]")
    ax.legend()
    ax.grid(True)