Define "KPI" function Call "KPI" for "global_dataframe" as "gKPI". Define the dataframe "RdataFrame" with columns: - "absGap" - "dG1", "dG2", "dG3",etc. With "NVEI" columns. - "Id1","Id2","Id3", etc. With "NNewInd" columns. For the first 2 indexes of "RdataFrame", in the columns "dG1", "dG2", etc. Put the information of the columns: "time" and "Volt" of "gKPI" in Start a loop of 1000 "Sample" in "Samples", per each sample: - Get sub dataframe "SDFrame" componed by 10% of the indexes of "global_dataframe" choosen randomly. Store the list "Indexes" of indexes choosen transposed. - Call "KPI" for "SDFrame" as "sKPI". - Calculate absolute error, "absGap", and detailed error per "Volt" value, "detGap", between "sKPI" and "gKPI". - Store "absGap" and "detGap" in the dataframe "RdataFrame" in columns "aGap", "dG1", "dG2", "dG3",etc. - Store "Indexes" in the dataframe "RdataFrame" in the columns "Id1","Id2","Id3",etc.

def KPI(df):
  df_kpi = df[['time', 'Volt']]
  df_kpi['time'] = df_kpi['time'].abs()
  df_kpi = df_kpi.sort_values(by=['time'])
  df_kpi['time'] = df_kpi['time'] - df_kpi['time'].min()
  df_kpi = df_kpi.sort_values(by=['Volt'])
  df_kpi = df_kpi.reset_index(drop=True)
  df_kpi['diff'] = df_kpi['time'].diff()
  df_kpi['mean'] = df_kpi['diff'].mean()
  df_kpi['std'] = df_kpi['diff'].std()
  df_kpi['cov'] = df_kpi['diff'].std()/df_kpi['diff'].mean()
  df_kpi['Volt'] = df_kpi['Volt'] - df