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)
