The Precision package offers a set of functions defining precision criteria for use in conventional situations when comparing two numbers. Generalities It is not advisable to use floating number equality. Instead, the difference between numbers must be compared with a given precision, i.e. : Standard_Real x1, x2 ; x1 = ... x2 = ... If ( x1 == x2 ) ... should not be used and must be written as indicated below: Standard_Real x1, x2 ; Standard_Real Precision = ... x1 = ... x2 = ... If ( Abs ( x1  x2 ) < Precision ) ... Likewise, when ordering floating numbers, you must take the following into account : Standard_Real x1, x2 ; Standard_Real Precision = ... x1 = ... ! a large number x2 = ... ! another large number If ( x1 < x2  Precision ) ... is incorrect when x1 and x2 are large numbers ; it is better to write : Standard_Real x1, x2 ; Standard_Real Precision = ... x1 = ... ! a large number x2 = ... ! another large number If ( x2  x1 > Precision ) ... Precision in Cas.Cade Generally speaking, the precision criterion is not implicit in Cas.Cade. Lowlevel geometric algorithms accept precision criteria as arguments. As a rule, they should not refer directly to the precision criteria provided by the Precision package. On the other hand, highlevel modeling algorithms have to provide the lowlevel geometric algorithms that they call, with a precision criteria. One way of doing this is to use the above precision criteria. Alternatively, the highlevel algorithms can have their own system for precision management. For example, the Topology Data Structure stores precision criteria for each elementary shape (as a vertex, an edge or a face). When a new topological object is constructed, the precision criteria are taken from those provided by the Precision package, and stored in the related data structure. Later, a topological algorithm which analyses these objects will work with the values stored in the data structure. Also, if this algorithm is to build a new topological object, from these precision criteria, it will compute a new precision criterion for the new topological object, and write it into the data structure of the new topological object. The different precision criteria offered by the Precision package, cover the most common requirements of geometric algorithms, such as intersections, approximations, and so on. The choice of precision depends on the algorithm and on the geometric space. The geometric space may be :
More...
#include <Precision.hxx>

static Standard_Real  Angular () 
 Returns the recommended precision value when checking the equality of two angles (given in radians). Standard_Real Angle1 = ... , Angle2 = ... ; If ( Abs( Angle2  Angle1 ) < Precision::Angular() ) ... The tolerance of angular equality may be used to check the parallelism of two vectors : gp_Vec V1, V2 ; V1 = ... V2 = ... If ( V1.IsParallel (V2, Precision::Angular() ) ) ... The tolerance of angular equality is equal to 1.e12. Note : The tolerance of angular equality can be used when working with scalar products or cross products since sines and angles are equivalent for small angles. Therefore, in order to check whether two unit vectors are perpendicular : gp_Dir D1, D2 ; D1 = ... D2 = ... you can use : If ( Abs( D1.D2 ) < Precision::Angular() ) ... (although the function IsNormal does exist). More...


static Standard_Real  Confusion () 
 Returns the recommended precision value when checking coincidence of two points in real space. The tolerance of confusion is used for testing a 3D distance : More...


static Standard_Real  SquareConfusion () 
 Returns square of Confusion. Created for speed and convenience. More...


static Standard_Real  Intersection () 
 Returns the precision value in real space, frequently used by intersection algorithms to decide that a solution is reached. This function provides an acceptable level of precision for an intersection process to define the adjustment limits. The tolerance of intersection is designed to ensure that a point computed by an iterative algorithm as the intersection between two curves is indeed on the intersection. It is obvious that two tangent curves are close to each other, on a large distance. An iterative algorithm of intersection may find points on these curves within the scope of the confusion tolerance, but still far from the true intersection point. In order to force the intersection algorithm to continue the iteration process until a correct point is found on the tangent objects, the tolerance of intersection must be smaller than the tolerance of confusion. On the other hand, the tolerance of intersection must be large enough to minimize the time required by the process to converge to a solution. The tolerance of intersection is equal to : Precision::Confusion() / 100. (that is, 1.e9). More...


static Standard_Real  Approximation () 
 Returns the precision value in real space, frequently used by approximation algorithms. This function provides an acceptable level of precision for an approximation process to define adjustment limits. The tolerance of approximation is designed to ensure an acceptable computation time when performing an approximation process. That is why the tolerance of approximation is greater than the tolerance of confusion. The tolerance of approximation is equal to : Precision::Confusion() * 10. (that is, 1.e6). You may use a smaller tolerance in an approximation algorithm, but this option might be costly. More...


static Standard_Real  Parametric (const Standard_Real P, const Standard_Real T) 
 Convert a real space precision to a parametric space precision. <T> is the mean value of the length of the tangent of the curve or the surface. More...


static Standard_Real  PConfusion (const Standard_Real T) 
 Returns a precision value in parametric space, which may be used : More...


static Standard_Real  SquarePConfusion () 
 Returns square of PConfusion. Created for speed and convenience. More...


static Standard_Real  PIntersection (const Standard_Real T) 
 Returns a precision value in parametric space, which may be used by intersection algorithms, to decide that a solution is reached. The purpose of this function is to provide an acceptable level of precision in parametric space, for an intersection process to define the adjustment limits. The parametric tolerance of intersection is designed to give a mean value in relation with the dimension of the curve or the surface. It considers that a variation of parameter equal to 1. along a curve (or an isoparametric curve of a surface) generates a segment whose length is equal to 100. (default value), or T. The parametric tolerance of intersection is equal to : More...


static Standard_Real  PApproximation (const Standard_Real T) 
 Returns a precision value in parametric space, which may be used by approximation algorithms. The purpose of this function is to provide an acceptable level of precision in parametric space, for an approximation process to define the adjustment limits. The parametric tolerance of approximation is designed to give a mean value in relation with the dimension of the curve or the surface. It considers that a variation of parameter equal to 1. along a curve (or an isoparametric curve of a surface) generates a segment whose length is equal to 100. (default value), or T. The parametric tolerance of intersection is equal to : More...


static Standard_Real  Parametric (const Standard_Real P) 
 Convert a real space precision to a parametric space precision on a default curve. More...


static Standard_Real  PConfusion () 
 Used to test distances in parametric space on a default curve. More...


static Standard_Real  PIntersection () 
 Used for Intersections in parametric space on a default curve. More...


static Standard_Real  PApproximation () 
 Used for Approximations in parametric space on a default curve. More...


static Standard_Boolean  IsInfinite (const Standard_Real R) 
 Returns True if R may be considered as an infinite number. Currently Abs(R) > 1e100. More...


static Standard_Boolean  IsPositiveInfinite (const Standard_Real R) 
 Returns True if R may be considered as a positive infinite number. Currently R > 1e100. More...


static Standard_Boolean  IsNegativeInfinite (const Standard_Real R) 
 Returns True if R may be considered as a negative infinite number. Currently R < 1e100. More...


static Standard_Real  Infinite () 
 Returns a big number that can be considered as infinite. Use Infinite() for a negative big number. More...


The Precision package offers a set of functions defining precision criteria for use in conventional situations when comparing two numbers. Generalities It is not advisable to use floating number equality. Instead, the difference between numbers must be compared with a given precision, i.e. : Standard_Real x1, x2 ; x1 = ... x2 = ... If ( x1 == x2 ) ... should not be used and must be written as indicated below: Standard_Real x1, x2 ; Standard_Real Precision = ... x1 = ... x2 = ... If ( Abs ( x1  x2 ) < Precision ) ... Likewise, when ordering floating numbers, you must take the following into account : Standard_Real x1, x2 ; Standard_Real Precision = ... x1 = ... ! a large number x2 = ... ! another large number If ( x1 < x2  Precision ) ... is incorrect when x1 and x2 are large numbers ; it is better to write : Standard_Real x1, x2 ; Standard_Real Precision = ... x1 = ... ! a large number x2 = ... ! another large number If ( x2  x1 > Precision ) ... Precision in Cas.Cade Generally speaking, the precision criterion is not implicit in Cas.Cade. Lowlevel geometric algorithms accept precision criteria as arguments. As a rule, they should not refer directly to the precision criteria provided by the Precision package. On the other hand, highlevel modeling algorithms have to provide the lowlevel geometric algorithms that they call, with a precision criteria. One way of doing this is to use the above precision criteria. Alternatively, the highlevel algorithms can have their own system for precision management. For example, the Topology Data Structure stores precision criteria for each elementary shape (as a vertex, an edge or a face). When a new topological object is constructed, the precision criteria are taken from those provided by the Precision package, and stored in the related data structure. Later, a topological algorithm which analyses these objects will work with the values stored in the data structure. Also, if this algorithm is to build a new topological object, from these precision criteria, it will compute a new precision criterion for the new topological object, and write it into the data structure of the new topological object. The different precision criteria offered by the Precision package, cover the most common requirements of geometric algorithms, such as intersections, approximations, and so on. The choice of precision depends on the algorithm and on the geometric space. The geometric space may be :
 a "real" 2D or 3D space, where the lengths are measured in meters, millimeters, microns, inches, etc ..., or
 a "parametric" space, 1D on a curve or 2D on a surface, where lengths have no dimension. The choice of precision criteria for real space depends on the choice of the product, as it is based on the accuracy of the machine and the unit of measurement. The choice of precision criteria for parametric space depends on both the accuracy of the machine and the dimensions of the curve or the surface, since the parametric precision criterion and the real precision criterion are linked : if the curve is defined by the equation P(t), the inequation : Abs ( t2  t1 ) < ParametricPrecision means that the parameters t1 and t2 are considered to be equal, and the inequation : Distance ( P(t2) , P(t1) ) < RealPrecision means that the points P(t1) and P(t2) are considered to be coincident. It seems to be the same idea, and it would be wonderful if these two inequations were equivalent. Note that this is rarely the case ! What is provided in this package? The Precision package provides :
 a set of real space precision criteria for the algorithms, in view of checking distances and angles,
 a set of parametric space precision criteria for the algorithms, in view of checking both :
 the equality of parameters in a parametric space,
 or the coincidence of points in the real space, by using parameter values,
 the notion of infinite value, composed of a value assumed to be infinite, and checking tests designed to verify if any value could be considered as infinite. All the provided functions are very simple. The returned values result from the adaptation of the applications developed by the Open CASCADE company to Open CASCADE algorithms. The main interest of these functions lies in that it incites engineers developing applications to ask questions on precision factors. Which one is to be used in such or such case ? Tolerance criteria are context dependent. They must first choose :
 either to work in real space,
 or to work in parametric space,
 or to work in a combined real and parametric space. They must next decide which precision factor will give the best answer to the current problem. Within an application environment, it is crucial to master precision even though this process may take a great deal of time.
◆ Angular()
Returns the recommended precision value when checking the equality of two angles (given in radians). Standard_Real Angle1 = ... , Angle2 = ... ; If ( Abs( Angle2  Angle1 ) < Precision::Angular() ) ... The tolerance of angular equality may be used to check the parallelism of two vectors : gp_Vec V1, V2 ; V1 = ... V2 = ... If ( V1.IsParallel (V2, Precision::Angular() ) ) ... The tolerance of angular equality is equal to 1.e12. Note : The tolerance of angular equality can be used when working with scalar products or cross products since sines and angles are equivalent for small angles. Therefore, in order to check whether two unit vectors are perpendicular : gp_Dir D1, D2 ; D1 = ... D2 = ... you can use : If ( Abs( D1.D2 ) < Precision::Angular() ) ... (although the function IsNormal does exist).
◆ Approximation()
Returns the precision value in real space, frequently used by approximation algorithms. This function provides an acceptable level of precision for an approximation process to define adjustment limits. The tolerance of approximation is designed to ensure an acceptable computation time when performing an approximation process. That is why the tolerance of approximation is greater than the tolerance of confusion. The tolerance of approximation is equal to : Precision::Confusion() * 10. (that is, 1.e6). You may use a smaller tolerance in an approximation algorithm, but this option might be costly.
◆ Confusion()
Returns the recommended precision value when checking coincidence of two points in real space. The tolerance of confusion is used for testing a 3D distance :
 Two points are considered to be coincident if their distance is smaller than the tolerance of confusion. gp_Pnt P1, P2 ; P1 = ... P2 = ... if ( P1.IsEqual ( P2 , Precision::Confusion() ) ) then ...
 A vector is considered to be null if it has a null length : gp_Vec V ; V = ... if ( V.Magnitude() < Precision::Confusion() ) then ... The tolerance of confusion is equal to 1.e7. The value of the tolerance of confusion is also used to define :
 the tolerance of intersection, and
 the tolerance of approximation. Note : As a rule, coordinate values in Cas.Cade are not dimensioned, so 1. represents one user unit, whatever value the unit may have : the millimeter, the meter, the inch, or any other unit. Let's say that Cas.Cade algorithms are written to be tuned essentially with mechanical design applications, on the basis of the millimeter. However, these algorithms may be used with any other unit but the tolerance criterion does no longer have the same signification. So pay particular attention to the type of your application, in relation with the impact of your unit on the precision criterion.
 For example in mechanical design, if the unit is the millimeter, the tolerance of confusion corresponds to a distance of 1 / 10000 micron, which is rather difficult to measure.
 However in other types of applications, such as cartography, where the kilometer is frequently used, the tolerance of confusion corresponds to a greater distance (1 / 10 millimeter). This distance becomes easily measurable, but only within a restricted space which contains some small objects of the complete scene.
◆ Infinite()
Returns a big number that can be considered as infinite. Use Infinite() for a negative big number.
◆ Intersection()
Returns the precision value in real space, frequently used by intersection algorithms to decide that a solution is reached. This function provides an acceptable level of precision for an intersection process to define the adjustment limits. The tolerance of intersection is designed to ensure that a point computed by an iterative algorithm as the intersection between two curves is indeed on the intersection. It is obvious that two tangent curves are close to each other, on a large distance. An iterative algorithm of intersection may find points on these curves within the scope of the confusion tolerance, but still far from the true intersection point. In order to force the intersection algorithm to continue the iteration process until a correct point is found on the tangent objects, the tolerance of intersection must be smaller than the tolerance of confusion. On the other hand, the tolerance of intersection must be large enough to minimize the time required by the process to converge to a solution. The tolerance of intersection is equal to : Precision::Confusion() / 100. (that is, 1.e9).
◆ IsInfinite()
Returns True if R may be considered as an infinite number. Currently Abs(R) > 1e100.
◆ IsNegativeInfinite()
Returns True if R may be considered as a negative infinite number. Currently R < 1e100.
◆ IsPositiveInfinite()
Returns True if R may be considered as a positive infinite number. Currently R > 1e100.
◆ PApproximation() [1/2]
Returns a precision value in parametric space, which may be used by approximation algorithms. The purpose of this function is to provide an acceptable level of precision in parametric space, for an approximation process to define the adjustment limits. The parametric tolerance of approximation is designed to give a mean value in relation with the dimension of the curve or the surface. It considers that a variation of parameter equal to 1. along a curve (or an isoparametric curve of a surface) generates a segment whose length is equal to 100. (default value), or T. The parametric tolerance of intersection is equal to :
◆ PApproximation() [2/2]
Used for Approximations in parametric space on a default curve.
This is Precision::Parametric(Precision::Approximation())
◆ Parametric() [1/2]
Convert a real space precision to a parametric space precision. <T> is the mean value of the length of the tangent of the curve or the surface.
Value is P / T
◆ Parametric() [2/2]
Convert a real space precision to a parametric space precision on a default curve.
Value is Parametric(P,1.e+2)
◆ PConfusion() [1/2]
Returns a precision value in parametric space, which may be used :
 to test the coincidence of two points in the real space, by using parameter values, or
 to test the equality of two parameter values in a parametric space. The parametric tolerance of confusion is designed to give a mean value in relation with the dimension of the curve or the surface. It considers that a variation of parameter equal to 1. along a curve (or an isoparametric curve of a surface) generates a segment whose length is equal to 100. (default value), or T. The parametric tolerance of confusion is equal to :
 Precision::Confusion() / 100., or Precision::Confusion() / T. The value of the parametric tolerance of confusion is also used to define :
 the parametric tolerance of intersection, and
 the parametric tolerance of approximation. Warning It is rather difficult to define a unique precision value in parametric space.
 First consider a curve (c) ; if M is the point of parameter u and M' the point of parameter u+du on the curve, call 'parametric tangent' at point M, for the variation du of the parameter, the quantity : T(u,du)=MM'/du (where MM' represents the distance between the two points M and M', in the real space).
 Consider the other curve resulting from a scaling transformation of (c) with a scale factor equal to
 The 'parametric tangent' at the point of parameter u of this curve is ten times greater than the previous one. This shows that for two different curves, the distance between two points on the curve, resulting from the same variation of parameter du, may vary considerably.
 Moreover, the variation of the parameter along the curve is generally not proportional to the curvilinear abscissa along the curve. So the distance between two points resulting from the same variation of parameter du, at two different points of a curve, may completely differ.
 Moreover, the parameterization of a surface may generate two quite different 'parametric tangent' values in the u or in the v parametric direction.
 Last, close to the poles of a sphere (the points which correspond to the values Pi/2. and Pi/2. of the v parameter) the u parameter may change from 0 to 2.Pi without impacting on the resulting point. Therefore, take great care when adjusting a parametric tolerance to your own algorithm.
◆ PConfusion() [2/2]
Used to test distances in parametric space on a default curve.
This is Precision::Parametric(Precision::Confusion())
◆ PIntersection() [1/2]
Returns a precision value in parametric space, which may be used by intersection algorithms, to decide that a solution is reached. The purpose of this function is to provide an acceptable level of precision in parametric space, for an intersection process to define the adjustment limits. The parametric tolerance of intersection is designed to give a mean value in relation with the dimension of the curve or the surface. It considers that a variation of parameter equal to 1. along a curve (or an isoparametric curve of a surface) generates a segment whose length is equal to 100. (default value), or T. The parametric tolerance of intersection is equal to :
◆ PIntersection() [2/2]
Used for Intersections in parametric space on a default curve.
This is Precision::Parametric(Precision::Intersection())
◆ SquareConfusion()
Returns square of Confusion. Created for speed and convenience.
◆ SquarePConfusion()
Returns square of PConfusion. Created for speed and convenience.
The documentation for this class was generated from the following file: