# Training data
# use either one of the following three:
# DataTrain = ReadData(DataTrain)
# DataTrain = pd.read_csv('../result/Train_NaN_Meaned', sep = '\t')
DataTrain = pd.read_csv('../result/Train_NaN_Meaned_without_2627', sep = '\t')
# Sample data
DataSample_full = ReadData(DataSample_full)
DataSample_partial = ReadData(DataSample_partial)
# ----------
# Format Data so they are indexed by start position
# ----------
DataTrain.set_index('start', drop=False, inplace=True, verify_integrity=True)
DataSample_full.set_index('start', drop=False, inplace=True, verify_integrity=True)
DataSample_partial.set_index('start', drop=False, inplace=True, verify_integrity=True)
# ----------
# Read the Imputation result
# ----------
Resultpath = '../result/raw/'
method = 'lasso_M1'
# method = 'rr_M1'
filename = Resultpath + method + '_predictions.txt'
print(method)
Result = pd.read_csv(filename, sep='\t')
# Result = ConvertResult(Result)
Result.set_index('start', drop=False, inplace=True, verify_integrity=True)
