import pandas as pd
import numpy as np


def create_calr_example_df(n_rows, start_date):
    '''

    '''
    np.random.seed(20)
    array = np.random.rand(n_rows)
    cumulative = np.cumsum(array)
    d = {
        'feature1_subject_1': array,
        'feature1_subject_2': array,
        'feature2_subject_1': cumulative,
        'feature2_subject_2': cumulative*2
    }
    idx = pd.date_range(start_date, periods=n_rows,
                        freq="MIN", name='Date_Time_1')
    return pd.DataFrame(data=d, index=idx)

[((110, 128), 'numpy.random.seed', 'np.random.seed', (['(20)'], {}), '(20)\n', (124, 128), True, 'import numpy as np\n'), ((141, 163), 'numpy.random.rand', 'np.random.rand', (['n_rows'], {}), '(n_rows)\n', (155, 163), True, 'import numpy as np\n'), ((181, 197), 'numpy.cumsum', 'np.cumsum', (['array'], {}), '(array)\n', (190, 197), True, 'import numpy as np\n'), ((383, 456), 'pandas.date_range', 'pd.date_range', (['start_date'], {'periods': 'n_rows', 'freq': '"""MIN"""', 'name': '"""Date_Time_1"""'}), "(start_date, periods=n_rows, freq='MIN', name='Date_Time_1')\n", (396, 456), True, 'import pandas as pd\n'), ((492, 523), 'pandas.DataFrame', 'pd.DataFrame', ([], {'data': 'd', 'index': 'idx'}), '(data=d, index=idx)\n', (504, 523), True, 'import pandas as pd\n')]