class AliFlowAnalysisWithQCumulants

 0.) base:
 1.) common:
 2a.) particle weights:
 2b.) event weights:
 3.) integrated flow:
 4.) differential flow:
 5.) other differential correlators:
 6.) distributions:
 7.) various:
 8.) debugging and cross-checking:
 9.) mixed harmonics:
 10.) Control histograms:
 11.) Bootstrap:
 constructor

 destructor

 a) Cross check if the settings make sense before starting the QC adventure;
 b) Access all common constants;
 c) Book all objects;
 d) Store flags for integrated and differential flow;
 e) Store flags for distributions of corelations;
 f) Store harmonic which will be estimated;
 g) Store flags for mixed harmonics;
 h) Store flags for control histograms;
 i) Store bootstrap flags.

 Running over data only in this method.

 Calculate the final results.

 Evaluate all correlators for reference flow with nested loops.

 Evalauted all correlators for differential flow with nested loops.

 Calculate correction terms for non-uniform acceptance of the detector for reference flow (cos terms).

 calculate corrections for non-uniform acceptance of the detector for no-name integrated flow (sin terms)

 a) Get pointers for common control and common result histograms;
 b) Get pointers for histograms holding particle weights;
 c) Get pointers for reference flow histograms;
 d) Get pointers for differential flow histograms;
 e) Get pointers for 2D differential flow histograms;
 f) Get pointers for other differential correlators;
 g) Get pointers for mixed harmonics histograms;
 h) Get pointers for nested loops' histograms;
 i) Get pointers for control histograms;
 j) Get pointers for bootstrap.

 project 2D profile onto pt axis to get 1D profile

 project 2D profile onto eta axis to get 1D profile

 Printing on the screen the final results for integrated flow (RF, POI and RP).

store the final results in output .root file

store the final results in output .root file

 Book common control histograms and common histograms for final results.
  a) Book common control histograms;
  b) Book common result histograms.

 Book and fill histograms which hold phi, pt and eta weights.

 Book all objects for integrated flow:
  a) Book profile to hold all flags for integrated flow;
  b) Book event-by-event quantities;
  c) Book profiles; // to be improved (comment)
  d) Book histograms holding the final results.

 Book all objects for control histograms.

 Book all objects needed for bootstrap.

 Book all objects for mixed harmonics.

 Initialize arrays of all objects relevant for calculations with nested loops.

 Initialize arrays of all objects relevant for mixed harmonics.

 Initialize arrays of all objects relevant for control histograms.

 Initialize arrays of all objects relevant for control histograms.

 Book all objects relevant for calculations with nested loops.

 Calculate in this method all multiparticle azimuthal correlations.

 Remark 1: All multiparticle correlations are stored in TProfile fIntFlowCorrelationsAllPro;
 Remark 2: There is a special TProfile fIntFlowCorrelationsPro holding results
           only for same harmonic's correlations <<2>>, <<4>>, <<6>> and <<8>>;
 Remark 3: Binning of fIntFlowCorrelationsAllPro is organized as follows:

  1st bin: <2>_{1n|1n} = two1n1n = cos(1n(phi1-phi2))>
  2nd bin: <2>_{2n|2n} = two2n2n = cos(2n(phi1-phi2))>
  3rd bin: <2>_{3n|3n} = two3n3n = cos(3n(phi1-phi2))>
  4th bin: <2>_{4n|4n} = two4n4n = cos(4n(phi1-phi2))>
  5th bin:           ----  EMPTY ----
  6th bin: <3>_{2n|1n,1n} = three2n1n1n = <cos(n(2*phi1-phi2-phi3))>
  7th bin: <3>_{3n|2n,1n} = three3n2n1n = <cos(n(3*phi1-2*phi2-phi3))>
  8th bin: <3>_{4n|2n,2n} = three4n2n2n = <cos(n(4*phi1-2*phi2-2*phi3))>
  9th bin: <3>_{4n|3n,1n} = three4n3n1n = <cos(n(4*phi1-3*phi2-phi3))>
 10th bin:           ----  EMPTY ----
 11th bin: <4>_{1n,1n|1n,1n} = four1n1n1n1n = <cos(n(phi1+phi2-phi3-phi4))>
 12th bin: <4>_{2n,1n|2n,1n} = four2n1n2n1n = <cos(n(2*phi1+phi2-2*phi3-phi4))>
 13th bin: <4>_{2n,2n|2n,2n} = four2n2n2n2n = <cos(2n(phi1+phi2-phi3-phi4))>
 14th bin: <4>_{3n|1n,1n,1n} = four3n1n1n1n = <cos(n(3*phi1-phi2-phi3-phi4))>
 15th bin: <4>_{3n,1n|3n,1n} = four3n1n3n1n = <cos(n(3*phi1+phi2-3*phi3-phi4))>
 16th bin: <4>_{3n,1n|2n,2n} = four3n1n2n2n = <cos(n(3*phi1+phi2-2*phi3-2*phi4))>
 17th bin: <4>_{4n|2n,1n,1n} = four4n2n1n1n = <cos(n(4*phi1-2*phi2-phi3-phi4))>
 18th bin:           ----  EMPTY ----
 19th bin: <5>_{2n,1n|1n,1n,1n} = five2n1n1n1n1n = <cos(n(2*phi1+phi2-phi3-phi4-phi5))>
 20th bin: <5>_{2n,2n|2n,1n,1n} = five2n2n2n1n1n = <cos(n(2*phi1+2*phi2-2*phi3-phi4-phi5))>
 21st bin: <5>_{3n,1n|2n,1n,1n} = five3n1n2n1n1n = <cos(n(3*phi1+phi2-2*phi3-phi4-phi5))>
 22nd bin: <5>_{4n|1n,1n,1n,1n} = five4n1n1n1n1n = <cos(n(4*phi1-phi2-phi3-phi4-phi5))>
 23rd bin:           ----  EMPTY ----
 24th bin: <6>_{1n,1n,1n|1n,1n,1n} = six1n1n1n1n1n1n = <cos(n(phi1+phi2+phi3-phi4-phi5-phi6))>
 25th bin: <6>_{2n,1n,1n|2n,1n,1n} = six2n1n1n2n1n1n = <cos(n(2*phi1+phi2+phi3-2*phi4-phi5-phi6))>
 26th bin: <6>_{2n,2n|1n,1n,1n,1n} = six2n2n1n1n1n1n = <cos(n(2*phi1+2*phi2-phi3-phi4-phi5-phi6))>
 27th bin: <6>_{3n,1n|1n,1n,1n,1n} = six3n1n1n1n1n1n = <cos(n(3*phi1+phi2-phi3-phi4-phi5-phi6))>
 28th bin:           ----  EMPTY ----
 29th bin: <7>_{2n,1n,1n|1n,1n,1n,1n} = seven2n1n1n1n1n1n1n =  <cos(n(2*phi1+phi2+phi3-phi4-phi5-phi6-phi7))>
 30th bin:           ----  EMPTY ----
 31st bin: <8>_{1n,1n,1n,1n|1n,1n,1n,1n} = eight1n1n1n1n1n1n1n1n = <cos(n(phi1+phi2+phi3+phi4-phi5-phi6-phi7-phi8))>
 32nd bin:           ----  EMPTY ----
  Extra correlations for v3{5} study:
 33rd bin: <4>_{4n,2n|3n,3n} = four4n2n3n3n = <cos(n(4*phi1+2*phi2-3*phi3-3*phi4))>
 34th bin: <5>_{3n,3n|2n,2n,2n} = five3n3n2n2n2n = <cos(n(3*phi1+3*phi2-2*phi3-2*phi4-2*phi5))>
  Extra correlations for Teaney-Yan study:
 35th bin: <2>_{5n|5n} = two5n5n = <cos(5n(phi1-phi2)>
 36th bin: <2>_{6n|6n} = two6n6n = <cos(6n(phi1-phi2)>
 37th bin: <3>_{5n|3n,2n} = three5n3n2n = <cos(n(5*phi1-3*phi2-2*phi3)>
 38th bin: <3>_{5n|4n,1n} = three5n4n1n = <cos(n(5*phi1-4*phi2-1*phi3)>
 39th bin: <3>_{6n|3n,3n} = three6n3n3n = <cos(n(6*phi1-3*phi2-3*phi3)>
 40th bin: <3>_{6n|4n,2n} = three6n4n2n = <cos(n(6*phi1-4*phi2-2*phi3)>
 41st bin: <3>_{6n|5n,1n} = three6n5n1n = <cos(n(6*phi1-5*phi2-1*phi3)>
 42nd bin: <4>_{6n|3n,2n,1n} = four6n3n2n1n = <cos(n(6*phi1-3*phi2-2*phi3-1*phi4)>
 43rd bin: <4>_{3n,2n|3n,2n} = four3n2n3n2n = <cos(n(3*phi1+2*phi2-3*phi3-2*phi4)>
 44th bin: <4>_{4n,1n|3n,2n} = four4n1n3n2n = <cos(n(4*phi1+1*phi2-3*phi3-2*phi4)>
 45th bin: <4>_{3n,3n|3n,3n} = four3n3n3n3n = <cos(3n*(phi1+phi2-phi3-phi4))>
 46th bin: <4>_{4n,2n|3n,3n} = four4n2n3n3n = <cos(n(4*phi1+2*phi2-3*phi3-3*phi4)>
 47th bin: <4>_{5n,1n|3n,3n} = four5n1n3n3n = <cos(n(5*phi1+1*phi2-3*phi3-3*phi4)>
 48th bin: <4>_{4n,2n|4n,2n} = four4n2n4n2n = <cos(n(4*phi1+2*phi2-4*phi3-2*phi4)>
 49th bin: <4>_{5n,1n|4n,2n} = four5n1n4n2n = <cos(n(5*phi1+1*phi2-4*phi3-2*phi4)>
 50th bin: <4>_{5n|3n,1n,1n} = four5n3n1n1n = <cos(n(5*phi1-3*phi2-1*phi3-1*phi4)>
 51st bin: <4>_{5n|2n,2n,1n} = four5n2n2n1n = <cos(n(5*phi1-2*phi2-2*phi3-1*phi4)>
 52nd bin: <4>_{5n,1n|5n,1n} = four5n1n5n1n = <cos(n(5*phi1+1*phi2-5*phi3-1*phi4)>
 53rd bin: <5>_{3n,3n|3n,2n,1n} = five3n3n3n2n1n = <cos(n(3*phi1+3*phi2-3*phi3-2*phi4-1*phi5)>
 54th bin: <5>_{4n,2n|3n,2n,1n} = five4n2n3n2n1n = <cos(n(4*phi1+2*phi2-3*phi3-2*phi4-1*phi5)>
 55th bin: <5>_{3n,2n|3n,1n,1n} = five3n2n3n1n1n = <cos(n(3*phi1+2*phi2-3*phi3-1*phi4-1*phi5)>
 56th bin: <5>_{3n,2n|2n,2n,1n} = five3n2n2n2n1n = <cos(n(3*phi1+2*phi2-2*phi3-2*phi4-1*phi5)>
 57th bin: <5>_{5n,1n|3n,2n,1n} = five5n1n3n2n1n = <cos(n(5*phi1+1*phi2-3*phi3-2*phi4-1*phi5)>
 58th bin: <6>_{3n,2n,1n|3n,2n,1n} = six3n2n1n3n2n1n = <cos(n(3*phi1+2*phi2+1*phi3-3*phi4-2*phi5-1*phi6)>
  Extra correlations for Teaney-Yan study (B):
 59th bin: <4>_{6n|4n,1n,1n} = four6n4n1n1n = <cos(n(6*phi1-4*phi2-1*phi3-1*phi4)>
 60th bin: <4>_{6n|2n,2n,2n} = four6n2n2n2n = <cos(n(6*phi1-2*phi2-2*phi3-2*phi4)>
 61st bin: <5>_{6n|2n,2n,1n,1n} = five6n2n2n1n1n = <cos(n(6*phi1-2*phi2-2*phi3-1*phi4-1*phi5)>
 62nd bin: <5>_{4n,1n,1n|3n,3n} = five4n1n1n3n3n = <cos(n(4*phi1+1*phi2+1*phi3-3*phi4-3*phi5)>
 63rd bin: <6>_{3n,3n|2n,2n,1n,1n} = six3n3n2n2n1n1n = <cos(n(3*phi1+3*phi2-2*phi3-2*phi4-1*phi5-1*phi6)>

 Calculate in this method all multi-particle azimuthal correlations in mixed harmonics.
 (Remark: For completeness sake, we also calculate here again correlations in the same harmonic.)

 Calculate in this method all multi-particle cumulants for azimuthal correlations in mixed harmonics.
 (Remark: For completeness sake, we also calculate here again cumulants in the same harmonic.)

 Store phi distribution for one event to illustrate flow.

 Calculate averages of products of correlations for integrated flow.

 Calculate averages of products of correction terms for NUA.

 a) Calculate unbiased estimators Cov(<2>,<4>), Cov(<2>,<6>), Cov(<2>,<8>), Cov(<4>,<6>), Cov(<4>,<8>) and Cov(<6>,<8>)
    for covariances V_(<2>,<4>), V_(<2>,<6>), V_(<2>,<8>), V_(<4>,<6>), V_(<4>,<8>) and V_(<6>,<8>).
 b) Store in histogram fIntFlowCovariances for instance the following:

             Cov(<2>,<4>) * (sum_{i=1}^{N} w_{<2>}_i w_{<4>}_i )/[(sum_{i=1}^{N} w_{<2>}_i) * (sum_{j=1}^{N} w_{<4>}_j)]

    where N is the number of events, w_{<2>} is event weight for <2> and w_{<4>} is event weight for <4>.
 c) Binning of fIntFlowCovariances is organized as follows:

     1st bin: Cov(<2>,<4>) * (sum_{i=1}^{N} w_{<2>}_i w_{<4>}_i )/[(sum_{i=1}^{N} w_{<2>}_i) * (sum_{j=1}^{N} w_{<4>}_j)]
     2nd bin: Cov(<2>,<6>) * (sum_{i=1}^{N} w_{<2>}_i w_{<6>}_i )/[(sum_{i=1}^{N} w_{<2>}_i) * (sum_{j=1}^{N} w_{<6>}_j)]
     3rd bin: Cov(<2>,<8>) * (sum_{i=1}^{N} w_{<2>}_i w_{<8>}_i )/[(sum_{i=1}^{N} w_{<2>}_i) * (sum_{j=1}^{N} w_{<8>}_j)]
     4th bin: Cov(<4>,<6>) * (sum_{i=1}^{N} w_{<4>}_i w_{<6>}_i )/[(sum_{i=1}^{N} w_{<4>}_i) * (sum_{j=1}^{N} w_{<6>}_j)]
     5th bin: Cov(<4>,<8>) * (sum_{i=1}^{N} w_{<4>}_i w_{<8>}_i )/[(sum_{i=1}^{N} w_{<4>}_i) * (sum_{j=1}^{N} w_{<8>}_j)]
     6th bin: Cov(<6>,<8>) * (sum_{i=1}^{N} w_{<6>}_i w_{<8>}_i )/[(sum_{i=1}^{N} w_{<6>}_i) * (sum_{j=1}^{N} w_{<8>}_j)]

 a) Calculate unbiased estimators Cov(*,*) for true covariances V_(*,*) for NUA terms.
 b) Store in histogram fIntFlowCovariancesNUA for instance the following:

             Cov(<2>,<cos(phi)>) * (sum_{i=1}^{N} w_{<2>}_i w_{<cos(phi)>}_i )/[(sum_{i=1}^{N} w_{<2>}_i) * (sum_{j=1}^{N} w_{<cos(phi)>}_j)]

    where N is the number of events, w_{<2>} is event weight for <2> and w_{<cos(phi)>} is event weight for <cos(phi)>.
 c) Binning of fIntFlowCovariancesNUA is organized as follows:

     1st bin: Cov(<2>,<cos(phi)>) * (sum_{i=1}^{N} w_{<2>}_i w_{<cos(phi)>}_i )/[(sum_{i=1}^{N} w_{<2>}_i) * (sum_{j=1}^{N} w_{<cos(phi)>}_j)]
     2nd bin: Cov(<2>,<sin(phi)>) * (sum_{i=1}^{N} w_{<2>}_i w_{<sin(phi)>}_i )/[(sum_{i=1}^{N} w_{<2>}_i) * (sum_{j=1}^{N} w_{<sin(phi)>}_j)]
     3rd bin: Cov(<cos(phi)>,<sin(phi)>) * (sum_{i=1}^{N} w_{<cos(phi)>}_i w_{<sin(phi)>}_i )/[(sum_{i=1}^{N} w_{<cos(phi)>}_i) * (sum_{j=1}^{N} w_{<sin(phi)>}_j)]

 From profile fIntFlowCorrelationsPro access measured correlations and spread,
 correctly calculate the statistical errors and store the final results and
 statistical errors for correlations in histogram fIntFlowCorrelationsHist.

 Remark: Statistical error of correlation is calculated as:

          statistical error = termA * spread * termB:
          termA = sqrt{sum_{i=1}^{N} w^2}/(sum_{i=1}^{N} w)
          termB = 1/sqrt(1-termA^2)

 Fill profile fAverageMultiplicity to hold average multiplicities and
 number of events for events with nRP>=0, nRP>=1, ... , and nRP>=8

 a) Calculate Q-cumulants from the measured multiparticle correlations;
 b) Propagate the statistical errors from measured multiparticle correlations to statistical errors of Q-cumulants;
 c) Remark: Q-cumulants calculated in this method are biased by non-uniform acceptance of detector !!!!
            Method CalculateQcumulantsCorrectedForNUAIntFlow() is called afterwards to correct for this bias;
 d) Store the results and statistical error of Q-cumulants in histogram fIntFlowQcumulants.
    Binning of fIntFlowQcumulants is organized as follows:

            1st bin: QC{2}
            2nd bin: QC{4}
            3rd bin: QC{6}
            4th bin: QC{8}

 Calculate cumulants for bootstrap.

 a) Calculate the final results for reference flow estimates from Q-cumulants;
 b) Propagate the statistical errors to reference flow estimates from statistical error of Q-cumulants;
 c) Store the results and statistical errors of reference flow estimates in histogram fIntFlow.
    Binning of fIntFlow is organized as follows:

            1st bin: v{2,QC}
            2nd bin: v{4,QC}
            3rd bin: v{6,QC}
            4th bin: v{8,QC}

 Fill in AliFlowCommonHistResults histograms relevant for reference flow.

 Calculate all correlations needed for integrated flow using particle weights.

 Initialize all arrays used to calculate integrated flow.

 Initialize all arrays needed to calculate differential flow.
  a) Initialize lists holding profiles;
  b) Initialize lists holding histograms;
  c) Initialize event-by-event quantities;
  d) Initialize profiles;
  e) Initialize histograms holding final results.

 Calculate differential flow cumulants from measured multiparticle correlations.

 Calculate 2D differential cumulants.

 Calculate final results for integrated flow of RPs and POIs.

 Initialize all arrays used for distributions.

 Initialize all arrays used for various unclassified objects.

 a) Book profile to hold all flags for distributions of correlations;
 b) Book all histograms to hold distributions of correlations.

 Book all objects for various unclassified quantities.

 Store all flags for distributiuons of correlations in profile fDistributionsFlags.

 Store distributions of correlations.

 Book and nest all lists nested in the base list fHistList.
  a) Book and nest lists for integrated flow;
  b) Book and nest lists for differential flow;
  c) Book and nest list for particle weights;
  d) Book and nest list for distributions;
  e) Book and nest list for various unclassified objects;
  f) Book and nest list for other differential correlators;
  g) Book and nest list for nested loops;
  h) Book and nest lists for mixed harmonics;
  i) Book and nest lists for control histograms;
  j) Book and nest lists for bootstrap.

 Book and nest lists for differential flow.

 Fill common result histograms for differential flow.

 Access and store common constants.

 a) Cross-check if the choice for multiplicity weights make sense;
 b) Cross-check if the choice for multiplicity itself make sense.

 Calculate sum of linear and quadratic event weights for correlations.

 Calculate sum of linear and quadratic event weights for NUA terms.

 Calculate sum of product of event weights for correlations.

 Calculate sum of product of event weights for NUA terms.

 Calculate reduced correlations for RPs or POIs for all pt and eta bins.

 Calculate other differential correlators for RPs or POIs for all pt and eta bins.

 Calculate all reduced correlations needed for 2D differential flow for each (pt,eta) bin.

 Calculate sums of various event weights for reduced correlations.
 (These quantitites are needed in expressions for unbiased estimators relevant for the statistical errors.)

 Calculate sum of products of various event weights for both types of correlations (the ones for int. and diff. flow).
 (These quantitites are needed in expressions for unbiased estimators relevant for the statistical errors.)

 Important: To fill fDiffFlowSumOfProductOfEventWeights[][][][] use bellow table (i,j) with following constraints:
 1.) i<j
 2.) do not store terms which DO NOT include reduced correlations;
 Table:
 [0=<2>,1=<2'>,2=<4>,3=<4'>,4=<6>,5=<6'>,6=<8>,7=<8'>] x [0=<2>,1=<2'>,2=<4>,3=<4'>,4=<6>,5=<6'>,6=<8>,7=<8'>]

 Transfer profiles into histograms and calculate statistical errors correctly.

 store products: <2><2'>, <2><4'>, <2><6'>, <2><8'>, <2'><4>,
                 <2'><4'>, <2'><6>, <2'><6'>, <2'><8>, <2'><8'>,
                 <4><4'>, <4><6'>, <4><8'>, <4'><6>, <4'><6'>,
                 <4'><8>, <4'><8'>, <6><6'>, <6><8'>, <6'><8>,
                 <6'><8'>, <8><8'>.

 a) Calculate unbiased estimators Cov(<2>,<2'>), Cov(<2>,<4'>), Cov(<4>,<2'>), Cov(<4>,<4'>) and Cov(<2'>,<4'>)
    for covariances V(<2>,<2'>), V(<2>,<4'>), V(<4>,<2'>), V(<4>,<4'>) and V(<2'>,<4'>).
 b) Store in histogram fDiffFlowCovariances[t][pe][index] for instance the following:

             Cov(<2>,<2'>) * (sum_{i=1}^{N} w_{<2>}_i w_{<2'>}_i )/[(sum_{i=1}^{N} w_{<2>}_i) * (sum_{j=1}^{N} w_{<2'>}_j)]

     where N is the number of events, w_{<2>} is event weight for <2> and w_{<2'>} is event weight for <2'>.
 c) Binning of fDiffFlowCovariances[t][pe][index] is organized as follows:

     1st bin: Cov(<2>,<2'>) * (sum_{i=1}^{N} w_{<2>}_i w_{<2'>}_i )/[(sum_{i=1}^{N} w_{<2>}_i) * (sum_{j=1}^{N} w_{<2'>}_j)]
     2nd bin: Cov(<2>,<4'>) * (sum_{i=1}^{N} w_{<2>}_i w_{<4'>}_i )/[(sum_{i=1}^{N} w_{<2>}_i) * (sum_{j=1}^{N} w_{<4'>}_j)]
     3rd bin: Cov(<4>,<2'>) * (sum_{i=1}^{N} w_{<4>}_i w_{<2'>}_i )/[(sum_{i=1}^{N} w_{<4>}_i) * (sum_{j=1}^{N} w_{<2'>}_j)]
     4th bin: Cov(<4>,<4'>) * (sum_{i=1}^{N} w_{<4>}_i w_{<4'>}_i )/[(sum_{i=1}^{N} w_{<4>}_i) * (sum_{j=1}^{N} w_{<4'>}_j)]
     5th bin: Cov(<2'>,<4'>) * (sum_{i=1}^{N} w_{<2'>}_i w_{<4'>}_i )/[(sum_{i=1}^{N} w_{<2'>}_i) * (sum_{j=1}^{N} w_{<4'>}_j)]

 Calculate final results for differential flow.

 Calculate final results for 2D diferential flow.

 a) Store all flags for integrated flow in profile fIntFlowFlags.

 Store all flags for differential flow in the profile fDiffFlowFlags.

 Store all flags for mixed harmonics in profile fMixedHarmonicsFlags.

 Store all flags for control histograms in profile fControlHistogramsFlags.

 Store all flags for bootstrap in TProfile fBootstrapFlags.

 Access all pointers to common control and common result histograms and profiles.

 Get pointers for histograms with particle weights.

 Get pointers for histograms and profiles relevant for integrated flow:
  a) Get pointer to base list for integrated flow holding profile fIntFlowFlags and lists fIntFlowProfiles and fIntFlowResults.
  b) Get pointer to profile fIntFlowFlags holding all flags for integrated flow.
  c) Get pointer to list fIntFlowProfiles and pointers to all objects that she holds.
  d) Get pointer to list fIntFlowResults and pointers to all objects that she holds.

 Get pointers for 2D differential flow histograms.
  a) Check pointers used in this method;
  b) Get pointers to 2D differential flow lists;
  c) Get pointers to 2D differential flow profiles;
  d) Get pointers to 2D differential flow histograms.

 Get pointers for other differential correlators.
  a) Get pointer to list with other differential correlators;
  b) Declare local flags;
  c) Get pointers to other differential profiles.

 Get pointer to all objects relevant for differential flow.
  a) Get pointer to base list for differential flow fDiffFlowList;
  b) Get pointer to profile fDiffFlowFlags holding all flags for differential flow. Access and set some flags;
  c) Get pointers to nested lists fDiffFlowListProfiles and fDiffFlowListResults;
  d) Define flags locally (to be improved: should I promote these flags to data members?);
  e) Get pointers to all nested lists in fDiffFlowListProfiles and to profiles which they hold;
  f) Get pointers to all nested lists in fDiffFlowListResults and to histograms which they hold.

 Book all objects needed for 2D differential flow.
  a) Define flags locally (to be improved: should I promote flags to data members?);
  b) Book e-b-e quantities;
  c) Book 2D profiles;
  d) Book 2D histograms.

 Book all histograms and profiles needed for differential flow.
  a) Book profile to hold all flags for differential flow;
  b) Define flags locally (to be improved: should I promote flags to data members?);
  c) Book e-b-e quantities;
  d) Book profiles;
  e) Book histograms holding final results.

 Calculate generalized Q-cumulants (cumulants corrected for non-unifom acceptance).

 From profile fIntFlowCorrectionTermsForNUAPro[sc] access measured correction terms for NUA
 and their spread, correctly calculate the statistical errors and store the final
 results and statistical errors for correction terms for NUA in histogram fIntFlowCorrectionTermsForNUAHist[sc].

 Remark: Statistical error of correction temrs is calculated as:

          statistical error = termA * spread * termB:
          termA = sqrt{sum_{i=1}^{N} w^2}/(sum_{i=1}^{N} w)
          termB = 1/sqrt(1-termA^2)

 Get pointers to all objects relevant for calculations with nested loops.

 Get pointers to all objects relevant for mixed harmonics.

 Get pointers to all control histograms.

 Get pointers to all bootstrap histograms.

 Store flow harmonic in common control histograms.

 Calculate all correlations needed for differential flow using particle weights.

 Fill common control histograms.

 Fill common control histograms.

 Reset all event by event quantities.

 Calculate correction terms for non-uniform acceptance for differential flow (sin terms).

 Calculate correction terms for non-uniform acceptance for differential flow (cos terms).

 Transfer profiles into histogams and correctly propagate the error.

 Calculate generalized differential flow cumulants (corrected for non-uniform acceptance).

 Calculate differential flow corrected for non-uniform acceptance.

 Evaluate with nested loops multiparticle correlations for integrated flow (without using the particle weights).

 Evaluate with nested loops multi-particle correlations for mixed harmonics.

 Cross-check results for multiparticle correlations needed for int. flow: results from Q-vectors vs results from nested loops.

 Cross-check results for corrections terms for non-uniform acceptance needed for int. flow: results from Q-vectors vs results from nested loops.

 Evaluate with nested loops multiparticle correlations for integrated flow (using the particle weights).

 Cross-check results for extra multiparticle correlations needed for int. flow
 which appear only when particle weights are used: results from Q-vectors vs results from nested loops.

 Evaluate with nested loops correction terms for non-uniform acceptance relevant for NONAME integrated flow (to be improved (name)).

 Remark: Both sin and cos correction terms are calculated in this method. Sin terms are stored in fIntFlowDirectCorrectionTermsForNUA[0],
 and cos terms in fIntFlowDirectCorrectionTermsForNUA[1]. Binning of fIntFlowDirectCorrectionTermsForNUA[sc] is organized as follows
 (sc stands for either sin or cos):

 Evaluate reduced correlations with nested loops without using the particle weights.

 Evaluate other differential correlators with nested loops without using the particle weights.

 Compare correlations needed for diff. flow calculated with nested loops and those calculated from Q-vectors

 Compare correlations needed for diff. flow calculated with nested loops and those calculated from Q-vectors

 Print on the screen number of RPs and POIs in selected pt and eta bin for cross checkings.

 Evaluate reduced correlations with nested loops without using the particle weights.

 Evaluate with nested loops correction terms for non-uniform acceptance (both sin and cos terms) relevant for differential flow.

 Compare corrections temrs for non-uniform acceptance needed for diff. flow calculated with nested loops and those calculated from Q-vectors

 Calculate corrections using particle weights for non-uniform acceptance of the detector for no-name integrated flow (cos terms).

 calculate corrections using particle weights for non-uniform acceptance of the detector for no-name integrated flow (sin terms)

 Evaluate with nested loops correction terms for non-uniform acceptance for integrated flow (using the particle weights).

 Calculate correction terms for non-uniform acceptance for differential flow (cos terms) using particle weights.

 Calculate correction terms for non-uniform acceptance for differential flow (sin terms).

 Evaluate with nested loops correction terms for non-uniform acceptance
 with using particle weights (both sin and cos terms) relevant for differential flow.

 Check all pointers used in method Finish().

 Check all pointers used in method Make(). // to be improved - check other pointers as well

 **** SETTERS and GETTERS ****
 0.) base:

 1.) common:

 2a.) particle weights:

 2b.) event weights:

 3.) Reference flow:
 Flags:

 Reference flow profiles:

 integrated flow histograms holding all results:

 4.) Differential flow:
  Flags:

  Profiles:
   1D:

   2D:

   Other differential correlators:

 histograms:

  2D:

 5.) distributions of correlations:
 profile:

 flags:

 # of bins for correlation axis in fDistributions[4], fCorrelation2468VsMult[4] and fCorrelationProduct2468VsMult[1]:

 histograms:

 min and max values of correlations (ci is correlations index [0=<2>,1=<4>,2=<6>,3=<8>]):

 min and max values of correlation products:

 min and max values of QvectorTerms:

 x.) debugging and cross-checking:

 9.) Mixed harmonics: