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package org.rcsb.cif.schema.core;
import org.rcsb.cif.model.*;
import org.rcsb.cif.schema.*;
import javax.annotation.Generated;
/**
* The CATEGORY of data items used to specify information about the
* refinement of the structural model.
*/
@Generated("org.rcsb.cif.schema.generator.SchemaGenerator")
public class Refine extends DelegatingCategory.DelegatingCifCoreCategory {
private static final String NAME = "refine";
public Refine(CifCoreBlock parentBlock) {
super(NAME, parentBlock);
}
/**
* Details of the refinement not specified by other data items.
* @return StrColumn
*/
public StrColumn getDetails() {
return new DelegatingStrColumn(parentBlock.getAliasedColumn("refine_details", "refine_special_details"));
}
/**
* Details of the refinement not specified by other data items.
* @return StrColumn
*/
public StrColumn getSpecialDetails() {
return new DelegatingStrColumn(parentBlock.getAliasedColumn("refine_details", "refine_special_details"));
}
/**
* Maximum density value in a difference Fourier map.
* @return FloatColumn
*/
public FloatColumn getDiffDensityMax() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_diff_density_max"));
}
/**
* Maximum density value in a difference Fourier map.
* @return FloatColumn
*/
public FloatColumn getDensityMax() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_diff_density_max"));
}
/**
* Standard uncertainty of the maximum density value
* in a difference Fourier map.
* @return FloatColumn
*/
public FloatColumn getDiffDensityMaxEsd() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_diff_density_max_esd", "refine_diff_density_max_su"));
}
/**
* Standard uncertainty of the maximum density value
* in a difference Fourier map.
* @return FloatColumn
*/
public FloatColumn getDensityMaxSu() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_diff_density_max_esd", "refine_diff_density_max_su"));
}
/**
* Minimum density value in a difference Fourier map.
* @return FloatColumn
*/
public FloatColumn getDiffDensityMin() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_diff_density_min"));
}
/**
* Minimum density value in a difference Fourier map.
* @return FloatColumn
*/
public FloatColumn getDensityMin() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_diff_density_min"));
}
/**
* Standard uncertainty of the minimum density value
* in a difference Fourier map.
* @return FloatColumn
*/
public FloatColumn getDiffDensityMinEsd() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_diff_density_min_esd", "refine_diff_density_min_su"));
}
/**
* Standard uncertainty of the minimum density value
* in a difference Fourier map.
* @return FloatColumn
*/
public FloatColumn getDensityMinSu() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_diff_density_min_esd", "refine_diff_density_min_su"));
}
/**
* Root mean square density value in a difference Fourier map.
* This value is measured with respect to the arithmetic mean
* density and is derived from summations over each grid point
* in the asymmetric unit of the cell. This quantity is useful
* for assessing the significance of *_min and *_max values,
* and also for defining suitable contour levels.
* @return FloatColumn
*/
public FloatColumn getDiffDensityRMS() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_diff_density_RMS", "refine_diff_density_rms"));
}
/**
* Root mean square density value in a difference Fourier map.
* This value is measured with respect to the arithmetic mean
* density and is derived from summations over each grid point
* in the asymmetric unit of the cell. This quantity is useful
* for assessing the significance of *_min and *_max values,
* and also for defining suitable contour levels.
* @return FloatColumn
*/
public FloatColumn getDensityRms() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_diff_density_RMS", "refine_diff_density_rms"));
}
/**
* Standard uncertainty of the root mean square density value
* in a difference Fourier map.
* @return FloatColumn
*/
public FloatColumn getDiffDensityRMSEsd() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_diff_density_RMS_esd", "refine_diff_density_rms_su"));
}
/**
* Standard uncertainty of the root mean square density value
* in a difference Fourier map.
* @return FloatColumn
*/
public FloatColumn getDensityRmsSu() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_diff_density_RMS_esd", "refine_diff_density_rms_su"));
}
/**
* Details on the absolute structure and how it was determined.
* @return StrColumn
*/
public StrColumn getLsAbsStructureDetails() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_abs_structure_details"));
}
/**
* Details on the absolute structure and how it was determined.
* @return StrColumn
*/
public StrColumn getAbsStructureDetails() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_abs_structure_details"));
}
/**
* The measure of absolute structure as defined by Flack (1983).
* For centrosymmetric structures, the only permitted value, if
* the data item is present, is 'inapplicable', represented by '.' .
* For noncentrosymmetric structures, the value must lie in the
* 99.97% Gaussian confidence interval -3u =< x =< 1 + 3u and a
* standard uncertainty (e.s.d.) u must be supplied. The
* _enumeration.range of 0.0:1.0 is correctly interpreted as
* meaning (0.0 - 3u) =< x =< (1.0 + 3u).
* Ref: Flack, H. D. (1983). Acta Cryst. A39, 876-881.
* @return FloatColumn
*/
public FloatColumn getLsAbsStructureFlack() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_abs_structure_Flack", "refine_ls_abs_structure_flack"));
}
/**
* The measure of absolute structure as defined by Flack (1983).
* For centrosymmetric structures, the only permitted value, if
* the data item is present, is 'inapplicable', represented by '.' .
* For noncentrosymmetric structures, the value must lie in the
* 99.97% Gaussian confidence interval -3u =< x =< 1 + 3u and a
* standard uncertainty (e.s.d.) u must be supplied. The
* _enumeration.range of 0.0:1.0 is correctly interpreted as
* meaning (0.0 - 3u) =< x =< (1.0 + 3u).
* Ref: Flack, H. D. (1983). Acta Cryst. A39, 876-881.
* @return FloatColumn
*/
public FloatColumn getAbsStructureFlack() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_abs_structure_Flack", "refine_ls_abs_structure_flack"));
}
/**
* Standard uncertainty of the measure of absolute structure
* as defined by Flack (1983).
* @return FloatColumn
*/
public FloatColumn getLsAbsStructureFlackEsd() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_abs_structure_Flack_esd", "refine_ls_abs_structure_flack_su"));
}
/**
* Standard uncertainty of the measure of absolute structure
* as defined by Flack (1983).
* @return FloatColumn
*/
public FloatColumn getAbsStructureFlackSu() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_abs_structure_Flack_esd", "refine_ls_abs_structure_flack_su"));
}
/**
* The measure of absolute structure as defined by Rogers (1981).
* The value must lie in the 99.97% Gaussian confidence interval
* -1 -3u =< \h =< 1 + 3u and a standard uncertainty (e.s.d.) u must
* be supplied. The _enumeration.range of -1.0:1.0 is correctly
* interpreted as meaning (-1.0 - 3u) =< \h =< (1.0 + 3u).
* Ref: Rogers, D. (1981). Acta Cryst. A37, 734-741.
* @return FloatColumn
*/
public FloatColumn getLsAbsStructureRogers() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_abs_structure_Rogers", "refine_ls_abs_structure_rogers"));
}
/**
* The measure of absolute structure as defined by Rogers (1981).
* The value must lie in the 99.97% Gaussian confidence interval
* -1 -3u =< \h =< 1 + 3u and a standard uncertainty (e.s.d.) u must
* be supplied. The _enumeration.range of -1.0:1.0 is correctly
* interpreted as meaning (-1.0 - 3u) =< \h =< (1.0 + 3u).
* Ref: Rogers, D. (1981). Acta Cryst. A37, 734-741.
* @return FloatColumn
*/
public FloatColumn getAbsStructureRogers() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_abs_structure_Rogers", "refine_ls_abs_structure_rogers"));
}
/**
* Standard uncertainty of the measure of absolute structure
* as defined by Rogers (1981).
* @return FloatColumn
*/
public FloatColumn getLsAbsStructureRogersEsd() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_abs_structure_Rogers_esd", "refine_ls_abs_structure_rogers_su"));
}
/**
* Standard uncertainty of the measure of absolute structure
* as defined by Rogers (1981).
* @return FloatColumn
*/
public FloatColumn getAbsStructureRogersSu() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_abs_structure_Rogers_esd", "refine_ls_abs_structure_rogers_su"));
}
/**
* Highest resolution for the reflections used in refinement.
* This corresponds to the smallest interplanar d value.
* @return FloatColumn
*/
public FloatColumn getLsDResHigh() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_d_res_high"));
}
/**
* Highest resolution for the reflections used in refinement.
* This corresponds to the smallest interplanar d value.
* @return FloatColumn
*/
public FloatColumn getDResHigh() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_d_res_high"));
}
/**
* Lowest resolution for the reflections used in refinement.
* This corresponds to the largest interplanar d value.
* @return FloatColumn
*/
public FloatColumn getLsDResLow() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_d_res_low"));
}
/**
* Lowest resolution for the reflections used in refinement.
* This corresponds to the largest interplanar d value.
* @return FloatColumn
*/
public FloatColumn getDResLow() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_d_res_low"));
}
/**
* The extinction coefficient used to calculate the correction
* factor applied to the structure-factor data. The nature of the
* extinction coefficient is given in the definitions of
* _refine_ls.extinction_expression and _refine_ls.extinction_method.
* For the 'Zachariasen' method it is the r* value; for the
* 'Becker-Coppens type 1 isotropic' method it is the 'g' value.
* For 'Becker-Coppens type 2 isotropic' corrections it is
* the 'rho' value. Note that the magnitude of these values is
* usually of the order of 10000.
* Ref: Becker, P. J. & Coppens, P. (1974). Acta Cryst. A30,
* 129-147, 148-153.
* Zachariasen, W. H. (1967). Acta Cryst. 23, 558-564.
* Larson, A. C. (1967). Acta Cryst. 23, 664-665.
* @return FloatColumn
*/
public FloatColumn getLsExtinctionCoef() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_extinction_coef"));
}
/**
* The extinction coefficient used to calculate the correction
* factor applied to the structure-factor data. The nature of the
* extinction coefficient is given in the definitions of
* _refine_ls.extinction_expression and _refine_ls.extinction_method.
* For the 'Zachariasen' method it is the r* value; for the
* 'Becker-Coppens type 1 isotropic' method it is the 'g' value.
* For 'Becker-Coppens type 2 isotropic' corrections it is
* the 'rho' value. Note that the magnitude of these values is
* usually of the order of 10000.
* Ref: Becker, P. J. & Coppens, P. (1974). Acta Cryst. A30,
* 129-147, 148-153.
* Zachariasen, W. H. (1967). Acta Cryst. 23, 558-564.
* Larson, A. C. (1967). Acta Cryst. 23, 664-665.
* @return FloatColumn
*/
public FloatColumn getExtinctionCoef() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_extinction_coef"));
}
/**
* Standard uncertainty of the extinction coefficient.
* @return FloatColumn
*/
public FloatColumn getLsExtinctionCoefEsd() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_extinction_coef_esd", "refine_ls_extinction_coef_su"));
}
/**
* Standard uncertainty of the extinction coefficient.
* @return FloatColumn
*/
public FloatColumn getExtinctionCoefSu() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_extinction_coef_esd", "refine_ls_extinction_coef_su"));
}
/**
* Description of or reference to the extinction-correction equation
* used to apply the data item _refine_ls.extinction_coef. This
* information should be sufficient to reproduce the extinction-
* correction factors applied to the structure factors.
* @return StrColumn
*/
public StrColumn getLsExtinctionExpression() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_extinction_expression"));
}
/**
* Description of or reference to the extinction-correction equation
* used to apply the data item _refine_ls.extinction_coef. This
* information should be sufficient to reproduce the extinction-
* correction factors applied to the structure factors.
* @return StrColumn
*/
public StrColumn getExtinctionExpression() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_extinction_expression"));
}
/**
* Description of the extinction correction method applied with the
* data item _refine_ls.extinction_coef. This description should
* include information about the correction method, either 'Becker-
* Coppens' or 'Zachariasen'. The latter is sometimes referred to as
* the 'Larson' method even though it employs Zachariasen's formula.
*
* The Becker-Coppens procedure is referred to as 'type 1' when
* correcting secondary extinction dominated by the mosaic spread;
* as 'type 2' when secondary extinction is dominated by particle
* size and includes a primary extinction component; and as 'mixed'
* when there are types 1 and 2.
*
* For the Becker-Coppens method it is also necessary to set the
* mosaic distribution as either 'Gaussian' or 'Lorentzian'; and the
* nature of the extinction as 'isotropic' or 'anisotropic'. Note
* that if either the 'mixed' or 'anisotropic' corrections are applied
* the multiple coefficients cannot be contained in the
* _refine_ls.extinction_coef and must be listed in _refine.special_details.
*
* Ref: Becker, P. J. & Coppens, P. (1974). Acta Cryst. A30, 129-153.
* Zachariasen, W. H. (1967). Acta Cryst. 23, 558-564.
* Larson, A. C. (1967). Acta Cryst. 23, 664-665.
* @return StrColumn
*/
public StrColumn getLsExtinctionMethod() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_extinction_method"));
}
/**
* Description of the extinction correction method applied with the
* data item _refine_ls.extinction_coef. This description should
* include information about the correction method, either 'Becker-
* Coppens' or 'Zachariasen'. The latter is sometimes referred to as
* the 'Larson' method even though it employs Zachariasen's formula.
*
* The Becker-Coppens procedure is referred to as 'type 1' when
* correcting secondary extinction dominated by the mosaic spread;
* as 'type 2' when secondary extinction is dominated by particle
* size and includes a primary extinction component; and as 'mixed'
* when there are types 1 and 2.
*
* For the Becker-Coppens method it is also necessary to set the
* mosaic distribution as either 'Gaussian' or 'Lorentzian'; and the
* nature of the extinction as 'isotropic' or 'anisotropic'. Note
* that if either the 'mixed' or 'anisotropic' corrections are applied
* the multiple coefficients cannot be contained in the
* _refine_ls.extinction_coef and must be listed in _refine.special_details.
*
* Ref: Becker, P. J. & Coppens, P. (1974). Acta Cryst. A30, 129-153.
* Zachariasen, W. H. (1967). Acta Cryst. 23, 558-564.
* Larson, A. C. (1967). Acta Cryst. 23, 664-665.
* @return StrColumn
*/
public StrColumn getExtinctionMethod() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_extinction_method"));
}
/**
* Least-squares goodness-of-fit parameter S for all reflections after
* the final cycle of refinement. Ideally, account should be taken of
* parameters restrained in the least squares.
*
* { sum { w [ Y(meas) - Y(calc) ]^2^ } }^1/2^
* S = { ------------------------------------ }
* { Nref - Nparam }
*
* Y(meas) = the measured coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/(u^2^)]
* u = standard uncertainty
* Nref = the number of reflections used in the refinement
* Nparam = the number of refined parameters
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsGoodnessOfFitAll() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_goodness_of_fit_all"));
}
/**
* Least-squares goodness-of-fit parameter S for all reflections after
* the final cycle of refinement. Ideally, account should be taken of
* parameters restrained in the least squares.
*
* { sum { w [ Y(meas) - Y(calc) ]^2^ } }^1/2^
* S = { ------------------------------------ }
* { Nref - Nparam }
*
* Y(meas) = the measured coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/(u^2^)]
* u = standard uncertainty
* Nref = the number of reflections used in the refinement
* Nparam = the number of refined parameters
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getGoodnessOfFitAll() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_goodness_of_fit_all"));
}
/**
* Standard uncertainty of the least-squares goodness-of-fit
* parameter S for all reflections after the final cycle of refinement.
* @return FloatColumn
*/
public FloatColumn getLsGoodnessOfFitAllEsd() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_goodness_of_fit_all_esd", "refine_ls_goodness_of_fit_all_su"));
}
/**
* Standard uncertainty of the least-squares goodness-of-fit
* parameter S for all reflections after the final cycle of refinement.
* @return FloatColumn
*/
public FloatColumn getGoodnessOfFitAllSu() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_goodness_of_fit_all_esd", "refine_ls_goodness_of_fit_all_su"));
}
/**
* Least-squares goodness-of-fit parameter S for significantly
* intense reflections, (i.e. 'observed' reflections with values
* greater-than the threshold set in _reflns.threshold_expression),
* after the final cycle. Ideally, account should be taken of
* parameters restrained in the least-squares refinement.
*
* { sum { w [ Y(meas_gt) - Y(calc) ]^2^ } }^1/2^
* S = { --------------------------------------- }
* { Nref - Nparam }
*
* Y(meas_gt) = the 'observed' coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/(u^2^)]
* u = standard uncertainty
* Nref = the number of reflections used in the refinement
* Nparam = the number of refined parameters
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsGoodnessOfFitObs() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_goodness_of_fit_obs", "refine_ls_goodness_of_fit_gt"));
}
/**
* Least-squares goodness-of-fit parameter S for significantly
* intense reflections, (i.e. 'observed' reflections with values
* greater-than the threshold set in _reflns.threshold_expression),
* after the final cycle. Ideally, account should be taken of
* parameters restrained in the least-squares refinement.
*
* { sum { w [ Y(meas_gt) - Y(calc) ]^2^ } }^1/2^
* S = { --------------------------------------- }
* { Nref - Nparam }
*
* Y(meas_gt) = the 'observed' coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/(u^2^)]
* u = standard uncertainty
* Nref = the number of reflections used in the refinement
* Nparam = the number of refined parameters
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsGoodnessOfFitGt() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_goodness_of_fit_obs", "refine_ls_goodness_of_fit_gt"));
}
/**
* Least-squares goodness-of-fit parameter S for significantly
* intense reflections, (i.e. 'observed' reflections with values
* greater-than the threshold set in _reflns.threshold_expression),
* after the final cycle. Ideally, account should be taken of
* parameters restrained in the least-squares refinement.
*
* { sum { w [ Y(meas_gt) - Y(calc) ]^2^ } }^1/2^
* S = { --------------------------------------- }
* { Nref - Nparam }
*
* Y(meas_gt) = the 'observed' coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/(u^2^)]
* u = standard uncertainty
* Nref = the number of reflections used in the refinement
* Nparam = the number of refined parameters
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getGoodnessOfFitGt() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_goodness_of_fit_obs", "refine_ls_goodness_of_fit_gt"));
}
/**
* Standard uncertainty of the least-squares goodness-of-fit
* parameter S for gt reflections after the final cycle of refinement.
* @return FloatColumn
*/
public FloatColumn getLsGoodnessOfFitGtEsd() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_goodness_of_fit_gt_esd", "refine_ls_goodness_of_fit_obs_esd", "refine_ls_goodness_of_fit_gt_su"));
}
/**
* Standard uncertainty of the least-squares goodness-of-fit
* parameter S for gt reflections after the final cycle of refinement.
* @return FloatColumn
*/
public FloatColumn getLsGoodnessOfFitObsEsd() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_goodness_of_fit_gt_esd", "refine_ls_goodness_of_fit_obs_esd", "refine_ls_goodness_of_fit_gt_su"));
}
/**
* Standard uncertainty of the least-squares goodness-of-fit
* parameter S for gt reflections after the final cycle of refinement.
* @return FloatColumn
*/
public FloatColumn getGoodnessOfFitGtSu() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_goodness_of_fit_gt_esd", "refine_ls_goodness_of_fit_obs_esd", "refine_ls_goodness_of_fit_gt_su"));
}
/**
* Least-squares goodness-of-fit parameter S for those reflections
* included in the final cycle of refinement. Account should be
* taken of restrained parameters.
*
* { sum { w [ Y(meas) - Y(calc) ]^2^ } }^1/2^
* S = { ------------------------------------ }
* { Nref - Nparam }
*
* Y(meas) = the measured coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/(u^2^)]
* u = standard uncertainty
* Nref = the number of reflections used in the refinement
* Nparam = the number of refined parameters
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsGoodnessOfFitRef() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_goodness_of_fit_ref"));
}
/**
* Least-squares goodness-of-fit parameter S for those reflections
* included in the final cycle of refinement. Account should be
* taken of restrained parameters.
*
* { sum { w [ Y(meas) - Y(calc) ]^2^ } }^1/2^
* S = { ------------------------------------ }
* { Nref - Nparam }
*
* Y(meas) = the measured coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/(u^2^)]
* u = standard uncertainty
* Nref = the number of reflections used in the refinement
* Nparam = the number of refined parameters
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getGoodnessOfFitRef() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_goodness_of_fit_ref"));
}
/**
* Code identifying how hydrogen atoms were treated in the refinement.
* @return StrColumn
*/
public StrColumn getLsHydrogenTreatment() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_hydrogen_treatment"));
}
/**
* Code identifying how hydrogen atoms were treated in the refinement.
* @return StrColumn
*/
public StrColumn getHydrogenTreatment() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_hydrogen_treatment"));
}
/**
* Code identifying the matrix type used for least-squares derivatives.
* @return StrColumn
*/
public StrColumn getLsMatrixType() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_matrix_type"));
}
/**
* Code identifying the matrix type used for least-squares derivatives.
* @return StrColumn
*/
public StrColumn getMatrixType() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_matrix_type"));
}
/**
* Number of constrained (non-refined or dependent) parameters
* in the least-squares process. These may be due to symmetry or any
* other constraint process (e.g. rigid-body refinement). See also
* _atom_site.constraints and _atom_site.refinement_flags. A general
* description of constraints may appear in _refine.special_details.
* @return IntColumn
*/
public IntColumn getLsNumberConstraints() {
return new DelegatingIntColumn(parentBlock.getColumn("refine_ls_number_constraints"));
}
/**
* Number of constrained (non-refined or dependent) parameters
* in the least-squares process. These may be due to symmetry or any
* other constraint process (e.g. rigid-body refinement). See also
* _atom_site.constraints and _atom_site.refinement_flags. A general
* description of constraints may appear in _refine.special_details.
* @return IntColumn
*/
public IntColumn getNumberConstraints() {
return new DelegatingIntColumn(parentBlock.getColumn("refine_ls_number_constraints"));
}
/**
* Number of parameters refined in the least-squares process. If
* possible this number should include the restrained parameters.
* The restrained parameters are distinct from the constrained
* parameters (where one or more parameters are linearly dependent
* on the refined value of another). Least-squares restraints
* often depend on geometry or energy considerations and this
* makes their direct contribution to this number, and to the
* goodness-of-fit calculation, difficult to assess.
* @return IntColumn
*/
public IntColumn getLsNumberParameters() {
return new DelegatingIntColumn(parentBlock.getColumn("refine_ls_number_parameters"));
}
/**
* Number of parameters refined in the least-squares process. If
* possible this number should include the restrained parameters.
* The restrained parameters are distinct from the constrained
* parameters (where one or more parameters are linearly dependent
* on the refined value of another). Least-squares restraints
* often depend on geometry or energy considerations and this
* makes their direct contribution to this number, and to the
* goodness-of-fit calculation, difficult to assess.
* @return IntColumn
*/
public IntColumn getNumberParameters() {
return new DelegatingIntColumn(parentBlock.getColumn("refine_ls_number_parameters"));
}
/**
* Number of unique reflections used in the least-squares refinement.
* @return IntColumn
*/
public IntColumn getLsNumberReflnsAll() {
return new DelegatingIntColumn(parentBlock.getAliasedColumn("refine_ls_number_reflns_all", "refine_ls_number_reflns"));
}
/**
* Number of unique reflections used in the least-squares refinement.
* @return IntColumn
*/
public IntColumn getNumberReflns() {
return new DelegatingIntColumn(parentBlock.getAliasedColumn("refine_ls_number_reflns_all", "refine_ls_number_reflns"));
}
/**
* The number of reflections that satisfy the resolution limits
* established by _refine_ls.d_res_high and _refine_ls.d_res_low
* and the observation limit established by
* _reflns.observed_criterion.
* @return IntColumn
*/
public IntColumn getLsNumberReflnsObs() {
return new DelegatingIntColumn(parentBlock.getAliasedColumn("refine_ls_number_reflns_obs", "refine_ls_number_reflns_gt"));
}
/**
* The number of reflections that satisfy the resolution limits
* established by _refine_ls.d_res_high and _refine_ls.d_res_low
* and the observation limit established by
* _reflns.observed_criterion.
* @return IntColumn
*/
public IntColumn getNumberReflnsGt() {
return new DelegatingIntColumn(parentBlock.getAliasedColumn("refine_ls_number_reflns_obs", "refine_ls_number_reflns_gt"));
}
/**
* Number of restrained parameters in the least-squares refinement. These
* parameters do not directly dependent on another refined parameter. Often
* restrained parameters involve geometry or energy dependencies. See also
* _atom_site.constraints and _atom_site.refinement_flags. A description
* of refinement constraints may appear in _refine.special_details.
* @return IntColumn
*/
public IntColumn getLsNumberRestraints() {
return new DelegatingIntColumn(parentBlock.getColumn("refine_ls_number_restraints"));
}
/**
* Number of restrained parameters in the least-squares refinement. These
* parameters do not directly dependent on another refined parameter. Often
* restrained parameters involve geometry or energy dependencies. See also
* _atom_site.constraints and _atom_site.refinement_flags. A description
* of refinement constraints may appear in _refine.special_details.
* @return IntColumn
*/
public IntColumn getNumberRestraints() {
return new DelegatingIntColumn(parentBlock.getColumn("refine_ls_number_restraints"));
}
/**
* Residual factor for all reflections satisfying the resolution limits
* specified by _refine_ls.d_res_high and _refine_ls.d_res_low. This is
* the conventional R factor. See also wR factor definitions.
*
* sum | F(meas) - F(calc) |
* R = ------------------------
* sum | F(meas) |
*
* F(meas) = the measured structure-factor amplitudes
* F(calc) = the calculated structure-factor amplitudes
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsRFactorAll() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_R_factor_all", "refine_ls_r_factor_all"));
}
/**
* Residual factor for all reflections satisfying the resolution limits
* specified by _refine_ls.d_res_high and _refine_ls.d_res_low. This is
* the conventional R factor. See also wR factor definitions.
*
* sum | F(meas) - F(calc) |
* R = ------------------------
* sum | F(meas) |
*
* F(meas) = the measured structure-factor amplitudes
* F(calc) = the calculated structure-factor amplitudes
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getRFactorAll() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_R_factor_all", "refine_ls_r_factor_all"));
}
/**
* Residual factor for the reflections judged significantly intense
* (see _reflns.number_gt and _reflns.threshold_expression) and included
* in the refinement. The reflections also satisfy the resolution limits
* specified by _refine_ls.d_res_high and _refine_ls.d_res_low. This is
* the conventional R factor.
*
* sum | F(meas_gt) - F(calc) |
* R = -----------------------------
* sum | F(meas_gt) |
*
* F(meas_gt) = the 'observed' structure-factor amplitudes
* F(calc) = the calculated structure-factor amplitudes
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsRFactorObs() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_R_factor_obs", "refine_ls_R_factor_gt", "refine_ls_r_factor_gt"));
}
/**
* Residual factor for the reflections judged significantly intense
* (see _reflns.number_gt and _reflns.threshold_expression) and included
* in the refinement. The reflections also satisfy the resolution limits
* specified by _refine_ls.d_res_high and _refine_ls.d_res_low. This is
* the conventional R factor.
*
* sum | F(meas_gt) - F(calc) |
* R = -----------------------------
* sum | F(meas_gt) |
*
* F(meas_gt) = the 'observed' structure-factor amplitudes
* F(calc) = the calculated structure-factor amplitudes
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsRFactorGt() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_R_factor_obs", "refine_ls_R_factor_gt", "refine_ls_r_factor_gt"));
}
/**
* Residual factor for the reflections judged significantly intense
* (see _reflns.number_gt and _reflns.threshold_expression) and included
* in the refinement. The reflections also satisfy the resolution limits
* specified by _refine_ls.d_res_high and _refine_ls.d_res_low. This is
* the conventional R factor.
*
* sum | F(meas_gt) - F(calc) |
* R = -----------------------------
* sum | F(meas_gt) |
*
* F(meas_gt) = the 'observed' structure-factor amplitudes
* F(calc) = the calculated structure-factor amplitudes
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getRFactorGt() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_R_factor_obs", "refine_ls_R_factor_gt", "refine_ls_r_factor_gt"));
}
/**
* Residual factor R(Fsqd), calculated on the squared amplitudes of the
* measured and calculated structure factors, for significantly intense
* reflections (satisfying _reflns.threshold_expression) and included in
* the refinement. The reflections also satisfy the resolution limits
* specified by _refine_ls.d_res_high and _refine_ls.d_res_low.
*
* sum | F(meas_gt)^2^ - F(calc)^2^ |
* R(Fsqd) = ------------------------------------
* sum F(meas_gt)^2^
*
* F(meas_gt)^2^ = squares of the 'observed' structure-factor
* F(calc)^2^ = squares of the calculated structure-factor
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsRFsqdFactorObs() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_R_Fsqd_factor_obs", "refine_ls_r_fsqd_factor"));
}
/**
* Residual factor R(Fsqd), calculated on the squared amplitudes of the
* measured and calculated structure factors, for significantly intense
* reflections (satisfying _reflns.threshold_expression) and included in
* the refinement. The reflections also satisfy the resolution limits
* specified by _refine_ls.d_res_high and _refine_ls.d_res_low.
*
* sum | F(meas_gt)^2^ - F(calc)^2^ |
* R(Fsqd) = ------------------------------------
* sum F(meas_gt)^2^
*
* F(meas_gt)^2^ = squares of the 'observed' structure-factor
* F(calc)^2^ = squares of the calculated structure-factor
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getRFsqdFactor() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_R_Fsqd_factor_obs", "refine_ls_r_fsqd_factor"));
}
/**
* Residual factor R(I) for significantly intense reflections (satisfying
* _reflns.threshold_expression) and included in the refinement. This is
* most often calculated in Rietveld refinements of powder data, where it
* is referred to as R~B~ or R~Bragg~.
*
* sum | I(meas_gt) - I(calc) |
* R(I) = -----------------------------
* sum | I(meas_gt) |
*
* I(meas_gt) = the net 'observed' intensities
* I(calc) = the net calculated intensities
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsRIFactorObs() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_R_I_factor_obs", "refine_ls_r_i_factor"));
}
/**
* Residual factor R(I) for significantly intense reflections (satisfying
* _reflns.threshold_expression) and included in the refinement. This is
* most often calculated in Rietveld refinements of powder data, where it
* is referred to as R~B~ or R~Bragg~.
*
* sum | I(meas_gt) - I(calc) |
* R(I) = -----------------------------
* sum | I(meas_gt) |
*
* I(meas_gt) = the net 'observed' intensities
* I(calc) = the net calculated intensities
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getRIFactor() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_R_I_factor_obs", "refine_ls_r_i_factor"));
}
/**
* Least-squares goodness-of-fit parameter S' for all reflections after
* the final cycle of least squares. This parameter explicitly includes
* the restraints applied in the least-squares process. See also
* _refine_ls.goodness_of_fit_all definition.
*
* {sum { w [ Y(meas) - Y(calc) ]^2^ } }^1/2^
* { + sum~r~ { w~r~ [ P(calc) - P(targ) ]^2^ } }
* S' = { -------------------------------------------------- }
* { N~ref~ + N~restr~ - N~param~ }
*
* Y(meas) = the measured coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/square of standard uncertainty (e.s.d.)]
* P(calc) = the calculated restraint values
* P(targ) = the target restraint values
* w~r~ = the restraint weight
*
* N~ref~ = the number of reflections used in the refinement
* (see _refine_ls.number_reflns)
* N~restr~ = the number of restraints
* (see _refine_ls.number_restraints)
* N~param~ = the number of refined parameters
* (see _refine_ls.number_parameters)
*
* sum is taken over the specified reflections
* sum~r~ is taken over the restraints
* @return FloatColumn
*/
public FloatColumn getLsRestrainedSAll() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_restrained_S_all", "refine_ls_restrained_s_all"));
}
/**
* Least-squares goodness-of-fit parameter S' for all reflections after
* the final cycle of least squares. This parameter explicitly includes
* the restraints applied in the least-squares process. See also
* _refine_ls.goodness_of_fit_all definition.
*
* {sum { w [ Y(meas) - Y(calc) ]^2^ } }^1/2^
* { + sum~r~ { w~r~ [ P(calc) - P(targ) ]^2^ } }
* S' = { -------------------------------------------------- }
* { N~ref~ + N~restr~ - N~param~ }
*
* Y(meas) = the measured coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/square of standard uncertainty (e.s.d.)]
* P(calc) = the calculated restraint values
* P(targ) = the target restraint values
* w~r~ = the restraint weight
*
* N~ref~ = the number of reflections used in the refinement
* (see _refine_ls.number_reflns)
* N~restr~ = the number of restraints
* (see _refine_ls.number_restraints)
* N~param~ = the number of refined parameters
* (see _refine_ls.number_parameters)
*
* sum is taken over the specified reflections
* sum~r~ is taken over the restraints
* @return FloatColumn
*/
public FloatColumn getRestrainedSAll() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_restrained_S_all", "refine_ls_restrained_s_all"));
}
/**
* Least-squares goodness-of-fit parameter S' for significantly intense
* reflections (satisfying _reflns.threshold_expression) after the final
* cycle of least squares. This parameter explicitly includes the restraints
* applied. The expression for S' is given in _refine_ls.restrained_S_all.
*
* {sum { w [ Y(meas_gt) - Y(calc) ]^2^ } }^1/2^
* { + sum~r~ { w~r~ [ P(calc) - P(targ) ]^2^ } }
* S' = { -------------------------------------------------- }
* { N~ref~ + N~restr~ - N~param~ }
*
* Y(meas_gt) = the 'observed' coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/square of standard uncertainty (e.s.d.)]
* P(calc) = the calculated restraint values
* P(targ) = the target restraint values
* w~r~ = the restraint weight
*
* N~ref~ = the number of reflections used in the refinement
* (see _refine_ls.number_reflns)
* N~restr~ = the number of restraints
* (see _refine_ls.number_restraints)
* N~param~ = the number of refined parameters
* (see _refine_ls.number_parameters)
*
* sum is taken over the specified reflections
* sum~r~ is taken over the restraints
* @return FloatColumn
*/
public FloatColumn getLsRestrainedSObs() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_restrained_S_obs", "refine_ls_restrained_s_gt"));
}
/**
* Least-squares goodness-of-fit parameter S' for significantly intense
* reflections (satisfying _reflns.threshold_expression) after the final
* cycle of least squares. This parameter explicitly includes the restraints
* applied. The expression for S' is given in _refine_ls.restrained_S_all.
*
* {sum { w [ Y(meas_gt) - Y(calc) ]^2^ } }^1/2^
* { + sum~r~ { w~r~ [ P(calc) - P(targ) ]^2^ } }
* S' = { -------------------------------------------------- }
* { N~ref~ + N~restr~ - N~param~ }
*
* Y(meas_gt) = the 'observed' coefficients
* (see _refine_ls.structure_factor_coef)
* Y(calc) = the calculated coefficients
* (see _refine_ls.structure_factor_coef)
* w = the least-squares reflection weight
* [1/square of standard uncertainty (e.s.d.)]
* P(calc) = the calculated restraint values
* P(targ) = the target restraint values
* w~r~ = the restraint weight
*
* N~ref~ = the number of reflections used in the refinement
* (see _refine_ls.number_reflns)
* N~restr~ = the number of restraints
* (see _refine_ls.number_restraints)
* N~param~ = the number of refined parameters
* (see _refine_ls.number_parameters)
*
* sum is taken over the specified reflections
* sum~r~ is taken over the restraints
* @return FloatColumn
*/
public FloatColumn getRestrainedSGt() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_restrained_S_obs", "refine_ls_restrained_s_gt"));
}
/**
* The largest ratio of the final least-squares parameter shift
* to the final standard uncertainty (s.u., formerly described
* as estimated standard deviation, e.s.d.).
* @return FloatColumn
*/
public FloatColumn getLsShiftOverEsdMax() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_shift_over_esd_max", "refine_ls_shift_over_su_max"));
}
/**
* The largest ratio of the final least-squares parameter shift
* to the final standard uncertainty (s.u., formerly described
* as estimated standard deviation, e.s.d.).
* @return FloatColumn
*/
public FloatColumn getLsShiftOverSuMax() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_shift_over_esd_max", "refine_ls_shift_over_su_max"));
}
/**
* The largest ratio of the final least-squares parameter shift
* to the final standard uncertainty (s.u., formerly described
* as estimated standard deviation, e.s.d.).
* @return FloatColumn
*/
public FloatColumn getShiftOverSuMax() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_shift_over_esd_max", "refine_ls_shift_over_su_max"));
}
/**
* Upper limit for the largest ratio of the final l-s parameter
* shift divided by the final standard uncertainty. This item is
* used when the largest value of the shift divided by the final
* standard uncertainty is too small to measure.
* @return FloatColumn
*/
public FloatColumn getLsShiftOverSuMaxLt() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_shift_over_su_max_lt"));
}
/**
* Upper limit for the largest ratio of the final l-s parameter
* shift divided by the final standard uncertainty. This item is
* used when the largest value of the shift divided by the final
* standard uncertainty is too small to measure.
* @return FloatColumn
*/
public FloatColumn getShiftOverSuMaxLt() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_shift_over_su_max_lt"));
}
/**
* The average ratio of the final least-squares parameter shift
* to the final standard uncertainty (s.u., formerly described
* as estimated standard deviation, e.s.d.).
* @return FloatColumn
*/
public FloatColumn getLsShiftOverEsdMean() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_shift_over_esd_mean", "refine_ls_shift_over_su_mean"));
}
/**
* The average ratio of the final least-squares parameter shift
* to the final standard uncertainty (s.u., formerly described
* as estimated standard deviation, e.s.d.).
* @return FloatColumn
*/
public FloatColumn getLsShiftOverSuMean() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_shift_over_esd_mean", "refine_ls_shift_over_su_mean"));
}
/**
* The average ratio of the final least-squares parameter shift
* to the final standard uncertainty (s.u., formerly described
* as estimated standard deviation, e.s.d.).
* @return FloatColumn
*/
public FloatColumn getShiftOverSuMean() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_shift_over_esd_mean", "refine_ls_shift_over_su_mean"));
}
/**
* Upper limit for the average ratio of the final l-s parameter
* shift divided by the final standard uncertainty. This item is
* used when the average value of the shift divided by the final
* standard uncertainty is too small to measure.
* @return FloatColumn
*/
public FloatColumn getLsShiftOverSuMeanLt() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_shift_over_su_mean_lt"));
}
/**
* Upper limit for the average ratio of the final l-s parameter
* shift divided by the final standard uncertainty. This item is
* used when the average value of the shift divided by the final
* standard uncertainty is too small to measure.
* @return FloatColumn
*/
public FloatColumn getShiftOverSuMeanLt() {
return new DelegatingFloatColumn(parentBlock.getColumn("refine_ls_shift_over_su_mean_lt"));
}
/**
* Structure-factor coefficient used in the least-squares process.
* @return StrColumn
*/
public StrColumn getLsStructureFactorCoef() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_structure_factor_coef"));
}
/**
* Structure-factor coefficient used in the least-squares process.
* @return StrColumn
*/
public StrColumn getStructureFactorCoef() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_structure_factor_coef"));
}
/**
* Description of special aspects of the weighting scheme used in the
* least-squares refinement. Used to describe the weighting when the
* value of _refine_ls.weighting_scheme is specified as 'calc'.
* @return StrColumn
*/
public StrColumn getLsWeightingDetails() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_weighting_details"));
}
/**
* Description of special aspects of the weighting scheme used in the
* least-squares refinement. Used to describe the weighting when the
* value of _refine_ls.weighting_scheme is specified as 'calc'.
* @return StrColumn
*/
public StrColumn getWeightingDetails() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_weighting_details"));
}
/**
* General description of the weighting scheme used in the least-squares.
* An enumerated code should be contained in this description.
* @return StrColumn
*/
public StrColumn getLsWeightingScheme() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_weighting_scheme"));
}
/**
* General description of the weighting scheme used in the least-squares.
* An enumerated code should be contained in this description.
* @return StrColumn
*/
public StrColumn getWeightingScheme() {
return new DelegatingStrColumn(parentBlock.getColumn("refine_ls_weighting_scheme"));
}
/**
* Weighted residual factors for all reflections satisfying the resolution
* limits specified by _refine_ls.d_res_high and _refine_ls.d_res_low.
* See also the _refine_ls.R_factor_all definition.
*
* ( sum w [ Y(meas) - Y(calc) ]^2^ )^1/2^
* wR = ( ------------------------------ )
* ( sum w Y(meas)^2^ )
*
* Y(meas) = the measured amplitude _refine_ls.structure_factor_coef
* Y(calc) = the calculated amplitude _refine_ls.structure_factor_coef
* w = the least-squares weight
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsWRFactorAll() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_wR_factor_all", "refine_ls_wr_factor_all"));
}
/**
* Weighted residual factors for all reflections satisfying the resolution
* limits specified by _refine_ls.d_res_high and _refine_ls.d_res_low.
* See also the _refine_ls.R_factor_all definition.
*
* ( sum w [ Y(meas) - Y(calc) ]^2^ )^1/2^
* wR = ( ------------------------------ )
* ( sum w Y(meas)^2^ )
*
* Y(meas) = the measured amplitude _refine_ls.structure_factor_coef
* Y(calc) = the calculated amplitude _refine_ls.structure_factor_coef
* w = the least-squares weight
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getWrFactorAll() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_wR_factor_all", "refine_ls_wr_factor_all"));
}
/**
* Weighted residual factors for significantly intense reflections
* (satisfying _reflns.threshold_expression) included in the refinement.
* The reflections must also satisfy the resolution limits established by
* _refine_ls.d_res_high and _refine_ls.d_res_low.
*
* ( sum w [ Y(meas_gt) - Y(calc) ]^2^ )^1/2^
* wR = ( ---------------------------------- )
* ( sum w Y(meas_gt)^2^ )
*
* Y(meas_gt) = the 'observed' amplitude _refine_ls.structure_factor_coef
* Y(calc) = the calculated amplitude _refine_ls.structure_factor_coef
* w = the least-squares weight
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getLsWRFactorObs() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_wR_factor_obs", "refine_ls_wr_factor_gt"));
}
/**
* Weighted residual factors for significantly intense reflections
* (satisfying _reflns.threshold_expression) included in the refinement.
* The reflections must also satisfy the resolution limits established by
* _refine_ls.d_res_high and _refine_ls.d_res_low.
*
* ( sum w [ Y(meas_gt) - Y(calc) ]^2^ )^1/2^
* wR = ( ---------------------------------- )
* ( sum w Y(meas_gt)^2^ )
*
* Y(meas_gt) = the 'observed' amplitude _refine_ls.structure_factor_coef
* Y(calc) = the calculated amplitude _refine_ls.structure_factor_coef
* w = the least-squares weight
* and the sum is taken over the specified reflections
* @return FloatColumn
*/
public FloatColumn getWrFactorGt() {
return new DelegatingFloatColumn(parentBlock.getAliasedColumn("refine_ls_wR_factor_obs", "refine_ls_wr_factor_gt"));
}
}