def __read_condition(self):
        # float32 -- SpikeTrain length in ms
        self.__t_stop = np.fromfile(self._fsrc, dtype=np.float32, count=1)[0]

        # float32 -- number of stimulus parameters
        numelements = int(np.fromfile(self._fsrc, dtype=np.float32,
                                      count=1)[0])

        # [float32] * numelements -- stimulus parameter values
        paramvals = np.fromfile(self._fsrc, dtype=np.float32,
                                count=numelements).tolist()

        # organize the parameers into a dictionary with arbitrary names
        paramnames = ['Param%s' % i for i in range(len(paramvals))]
        self.__params = dict(zip(paramnames, paramvals))
Make a line plot from for Y on X (Y = f(X)) on the canvas.
dataset/ETHPy150Open NeuralEnsemble/python-neo/neo/io/brainwaref32io.py/BrainwareF32IO.__read_condition--crossover with--dataset/ETHPy150Open SheffieldML/GPy/GPy/plotting/abstract_plotting_library.py/AbstractPlottingLibrary.plot