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Use Newton's method to take the inverse of u = tanh x, that is for a given u, calculate tanh-u= x. Clearly this is the root of f(x) = tanh(x) u where u is a constant. Include the function f(x) to evaluate Include a function for f'(x) to evaluate The programming problem is how to pass the value of u to these functions? Python functions can be defined such that they accept a list of arguments in addition to the variable x where they are to be evaluated. For exmaple: def f(x, *args) : u = args return numpy.tanh(x)-u # -- This is the value of u we want to take the arctanh of. # -- args_to_pass wil be passed into both f and fp to help evaluate those funcitons. # -- It is a 'tuple' type container, a collection which is ordered and unchangeable, different from a list. args_to_pass=0.9,) scipy.optimize.newton(f,0.0,fprime=fp, args=args_to_pass) Verify your results with numpy.arctanh. Use Newton's method to take the inverse of u = tanh x, that is for a given u, calculate tanh-u= x. Clearly this is the root of f(x) = tanh(x) u where u is a constant. Include the function f(x) to evaluate Include a function for f'(x) to evaluate The programming problem is how to pass the value of u to these functions? Python functions can be defined such that they accept a list of arguments in addition to the variable x where they are to be evaluated. For exmaple: def f(x, *args) : u = args return numpy.tanh(x)-u # -- This is the value of u we want to take the arctanh of. # -- args_to_pass wil be passed into both f and fp to help evaluate those funcitons. # -- It is a 'tuple' type container, a collection which is ordered and unchangeable, different from a list. args_to_pass=0.9,) scipy.optimize.newton(f,0.0,fprime=fp, args=args_to_pass) Verify your results with numpy.arctanh