#include <Goptical/Data/Set1d>
namespace Goptical {
namespace Data {
class Set1d;
};
};
This class is a member of the Data namespace.
This abstract class contains pure virtuals.
See also the full member list section for this class.
No documentation available
Get total number of data stored in data set
This virtual function implements the get_count pure function declared in the Set base abstract class.
Documentation inherited from base class:
Get total number of data stored for dimension n in data set
This virtual function implements the get_dimensions pure function declared in the Set base abstract class.
Documentation inherited from base class:
Get total number of dimensions
Get minimal and maximal x values on found in data set
This virtual function implements the get_x_range pure function declared in the Set base abstract class.
Documentation inherited from base class:
Get minimal and maximal x values on dimension n found in data set
Get x data at index n in data set
This virtual function implements the get_x_value pure function declared in the Set base abstract class.
Documentation inherited from base class:
Get data stored at position n on dimension dim in data set
Get y data stored at index n in data set
Get modifiable reference to y data stored at index n in data set
This virtual function implements the get_y_value pure function declared in the Set base abstract class.
Documentation inherited from base class:
Get y data stored at position (x0, x1, ...) in data set
Interpolate y value corresponding to given x value in data set.
Interpolate y value corresponding to given x value in data set. Data may be differentiated several times.
This virtual function implements the interpolate pure function declared in the Set base abstract class.
Documentation inherited from base class:
Interpolate y value corresponding to given x value(s) in data set.
This virtual function implements the interpolate pure function declared in the Set base abstract class.
Documentation inherited from base class:
Interpolate y value corresponding to given x value in data set. Data may be differentiated several times along the requested dimension.
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