def __load_data(self, file_path): """Loads a reflectivity dataset from a given file path and applies scaling. Args: file_path (string): a path to the file with the data to construct the model for. """ data = ReflectDataset(file_path) #Load the data for which the model is designed for. self.filename = os.path.basename(data.filename) data.scale(np.max(data.data[1])) #Normalise Y and Error by dividing by max R point. x, y, y_err = data.x.tolist(), data.y.tolist(), data.y_err.tolist() removed = [] #Remove any points containing 0 values as these cause NaNs when fitting. for i in range(len(x)): if x[i] == 0 or y[i] == 0 or y_err[i] == 0: removed.append(i) #Remove the identified points and return the processed dataset. x = np.delete(np.array(x), removed) y = np.delete(np.array(y), removed) y_err = np.delete(np.array(y_err), removed) data_new = np.array([x, y, y_err]) return ReflectDataset(data_new)