human
Solve the following python programming problem given the following code:
# Need to make the model into a Pints one
class PintsOneComp(pints.ForwardModel):
    def n_parameters(self):
        return 1
    def simulate(self, parameter, times):
        return simulate(onecomp, parameter, 1, times)
    
problem = pints.SingleOutputProblem(PintsOneComp(), times, noisy_data) # Create a model instance with measured data
error_measure = pints.SumOfSquaresError(problem) # Define the error measure to be used (as sumofsquares function)
optimisation = pints.OptimisationController(error_measure, [1], method=pints.XNES) # Define a statistical problem
optimisation.set_log_to_screen(False) # Suppress log output
parameters, error = optimisation.run() # Run the statistical model

# How does it look?
print('Custom calculation result: \t'+str(result.x))
print('Pints calculation result: \t'+str(parameters[0]))
plt.figure()
plt.plot(times, noisy_data, '.', label='Measured values')
plt.plot(times, recon_model, '--', label='Custom inferred values')
plt.plot(times, PintsOneComp().simulate(parameters, times), '--', lw=2, label='Pints inferred values')
plt.legend()
plt.show()
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

Using PINTS
The work above estimates $k$ pretty well simply based on minimising the output of scalar_to_minimise. So how, and more importantly why, do we use PINTS?
PINTS formalises the process of creating an optimiser, making it more re-usable in different settings. Rather than rewriting code every time new measures are needed, PINTS allows us to change to different error measures, optimisation methods, and more, relatively simply. Not only that, but PINTS is both faster to write and faster to run than the section above!