old_scripts/info_reduc.py
def add_error(percent_err, df):
    """
    Given a dataframe of nuclide vectors, add error to each element in each 
    nuclide vector that has a random value within the range [1-err, 1+err]

    Parameters
    ----------
    percent_err : a float indicating the maximum error that can be added to the nuclide 
                  vectors
    df : dataframe of only nuclide concentrations

    Returns
    -------
    df_err : dataframe with nuclide concentrations altered by some error

    """
    x = df.shape[0]
    y = df.shape[1]
    err = percent_err / 100.0
    low = 1 - err
    high = 1 + err
    errs = np.random.uniform(low, high, (x, y))
    df_err = df * errs
    df_err_scaled = scale(df_err)
    
    return df_err

    Given a dataframe of nuclide vectors, add error to each element in each 
    nuclide vector that has a random value within the range [1-err, 1+err]

    Parameters
    ----------
    percent_err : a float indicating the maximum error that can be added to the nuclide 
                  vectors
    df : dataframe of only nuclide concentrations

    Returns
    -------
    df_err : dataframe with nuclide concentrations altered by some error

    
Given a dataframe of nuclide vectors, add error to each element in each
nuclide vector that has a random value within the range [1-err, 1+err]

Parameters

percent_err : a float indicating the maximum error that can be added to the nuclide
vectors
df : dataframe of only nuclide concentrations

Returns

df_err : dataframe with nuclide concentrations altered by some error
Given a dataframe of nuclide vectors add error to each element in each nuclide vector that has a random value within the range [ 1 - err 1 + err ] Parameters percent_err : a float indicating the maximum error that can be added to the nuclide vectors df : dataframe of only nuclide concentrations Returns df_err : dataframe with nuclide concentrations altered by some error
def add_error(percent_err, df):
    x = df.shape[0]
    y = df.shape[1]
    err = percent_err / 100.0
    low = 1 - err
    high = 1 + err
    errs = np.random.uniform(low, high, (x, y))
    df_err = df * errs
    df_err_scaled = scale(df_err)
    return df_err
def add_error ( percent_err , df ) : x = df . shape [ 0 ] y = df . shape [ 1 ] err = percent_err / 100.0 low = 1 - err high = 1 + err errs = np . random . uniform ( low , high , ( x , y ) ) df_err = df * errs df_err_scaled = scale ( df_err ) return df_err
Given a dataframe of nuclide vectors, add error to each element in each
nuclide vector that has a random value within the range [1-err, 1+err]
Given a dataframe of nuclide vectors add error to each element in each nuclide vector that has a random value within the range [ 1 - err 1 + err ]
"""
    Given a dataframe of nuclide vectors, add error to each element in each 
    nuclide vector that has a random value within the range [1-err, 1+err]

    Parameters
    ----------
    percent_err : a float indicating the maximum error that can be added to the nuclide 
                  vectors
    df : dataframe of only nuclide concentrations

    Returns
    -------
    df_err : dataframe with nuclide concentrations altered by some error

    """
percent_err
df