Package org.eclipse.dawnsci.nexus

Interface NXdetector

All Superinterfaces:
GroupNode, Iterable<NodeLink>, Node, NXcomponent, NXobject
All Known Subinterfaces:
NXelectron_detector
public interface NXdetector extends NXcomponent
A detector, detector bank, or multidetector.

Symbols: These symbols will be used below to illustrate the coordination of the rank and sizes of datasets and the preferred ordering of the dimensions. Each of these are optional (so the rank of the datasets will vary according to the situation) and the general ordering principle is slowest to fastest. The type of each dimension should follow the order of scan points, detector output (e.g. pixels), then time-of-flight (i.e. spectroscopy, spectrometry). Note that the output of a detector is not limited to single values (0D), lists (1D) and images (2), but three or higher dimensional arrays can be produced by a detector at each trigger.

  • nP number of scan points (only present in scanning measurements)
  • i number of detector pixels in the first (slowest) direction
  • j number of detector pixels in the second (faster) direction
  • k number of detector pixels in the third (if necessary, fastest) direction
  • tof number of bins in the time-of-flight histogram

  • Field Details

  • Method Details

    • getTime_of_flight

      org.eclipse.january.dataset.Dataset getTime_of_flight()
      Total time of flight

      Type: NX_FLOAT Units: NX_TIME_OF_FLIGHT Dimensions: 1: tof+1;

      Returns:
      the value.

    • setTime_of_flight

      DataNode setTime_of_flight(org.eclipse.january.dataset.IDataset time_of_flightDataset)
      Total time of flight

      Type: NX_FLOAT Units: NX_TIME_OF_FLIGHT Dimensions: 1: tof+1;

      Parameters:
      time_of_flightDataset - the time_of_flightDataset

    • getTime_of_flightScalar

      Double getTime_of_flightScalar()
      Total time of flight

      Type: NX_FLOAT Units: NX_TIME_OF_FLIGHT Dimensions: 1: tof+1;

      Returns:
      the value.

    • setTime_of_flightScalar

      DataNode setTime_of_flightScalar(Double time_of_flightValue)
      Total time of flight

      Type: NX_FLOAT Units: NX_TIME_OF_FLIGHT Dimensions: 1: tof+1;

      Parameters:
      time_of_flight - the time_of_flight

    • getTime_of_flightAttributeAxis

      @Deprecated Long getTime_of_flightAttributeAxis()
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 3

      Returns:
      the value.

    • setTime_of_flightAttributeAxis

      @Deprecated void setTime_of_flightAttributeAxis(Long axisValue)
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 3

      Parameters:
      axisValue - the axisValue

    • getTime_of_flightAttributePrimary

      @Deprecated Long getTime_of_flightAttributePrimary()
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 1

      Returns:
      the value.

    • setTime_of_flightAttributePrimary

      @Deprecated void setTime_of_flightAttributePrimary(Long primaryValue)
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 1

      Parameters:
      primaryValue - the primaryValue

    • getTime_of_flightAttributeLong_name

      String getTime_of_flightAttributeLong_name()
      Total time of flight
      Returns:
      the value.

    • setTime_of_flightAttributeLong_name

      void setTime_of_flightAttributeLong_name(String long_nameValue)
      Total time of flight
      Parameters:
      long_nameValue - the long_nameValue

    • getRaw_time_of_flight

      org.eclipse.january.dataset.Dataset getRaw_time_of_flight()
      In DAQ clock pulses

      Type: NX_INT Units: NX_PULSES Dimensions: 1: tof+1;

      Returns:
      the value.

    • setRaw_time_of_flight

      DataNode setRaw_time_of_flight(org.eclipse.january.dataset.IDataset raw_time_of_flightDataset)
      In DAQ clock pulses

      Type: NX_INT Units: NX_PULSES Dimensions: 1: tof+1;

      Parameters:
      raw_time_of_flightDataset - the raw_time_of_flightDataset

    • getRaw_time_of_flightScalar

      Long getRaw_time_of_flightScalar()
      In DAQ clock pulses

      Type: NX_INT Units: NX_PULSES Dimensions: 1: tof+1;

      Returns:
      the value.

    • setRaw_time_of_flightScalar

      DataNode setRaw_time_of_flightScalar(Long raw_time_of_flightValue)
      In DAQ clock pulses

      Type: NX_INT Units: NX_PULSES Dimensions: 1: tof+1;

      Parameters:
      raw_time_of_flight - the raw_time_of_flight

    • getRaw_time_of_flightAttributeFrequency

      Number getRaw_time_of_flightAttributeFrequency()
      Clock frequency in Hz
      Returns:
      the value.

    • setRaw_time_of_flightAttributeFrequency

      void setRaw_time_of_flightAttributeFrequency(Number frequencyValue)
      Clock frequency in Hz
      Parameters:
      frequencyValue - the frequencyValue

    • getDetector_number

      org.eclipse.january.dataset.Dataset getDetector_number()
      Identifier for detector (pixels) Can be multidimensional, if needed

      Type: NX_INT

      Returns:
      the value.

    • setDetector_number

      DataNode setDetector_number(org.eclipse.january.dataset.IDataset detector_numberDataset)
      Identifier for detector (pixels) Can be multidimensional, if needed

      Type: NX_INT

      Parameters:
      detector_numberDataset - the detector_numberDataset

    • getDetector_numberScalar

      Long getDetector_numberScalar()
      Identifier for detector (pixels) Can be multidimensional, if needed

      Type: NX_INT

      Returns:
      the value.

    • setDetector_numberScalar

      DataNode setDetector_numberScalar(Long detector_numberValue)
      Identifier for detector (pixels) Can be multidimensional, if needed

      Type: NX_INT

      Parameters:
      detector_number - the detector_number

    • getData

      org.eclipse.january.dataset.Dataset getData()
      Data values from the detector. The rank and dimension ordering should follow a principle of slowest to fastest measurement axes and may be explicitly specified in application definitions. Mechanical scanning of objects (e.g. sample position/angle, incident beam energy, etc) tends to be the slowest part of an experiment and so any such scan axes should be allocated to the first dimensions of the array. Note that in some cases it may be useful to represent a 2D set of scan points as a single scan-axis in the data array, especially if the scan pattern doesn't fit a rectangular array nicely. Repetition of an experiment in a time series tends to be used similar to a slow scan axis and so will often be in the first dimension of the data array. The next fastest axes are typically the readout of the detector. A point detector will not add any dimensions (as it is just a single value per scan point) to the data array, a strip detector will add one dimension, an imaging detector will add two dimensions (e.g. X, Y axes) and detectors outputting higher dimensional data will add the corresponding number of dimensions. Note that the detector dimensions don't necessarily have to be written in order of the actual readout speeds - the slowest to fastest rule principle is only a guide. Finally, detectors that operate in a time-of-flight mode, such as a neutron spectrometer or a silicon drift detector (used for X-ray fluorescence) tend to have their dimension(s) added to the last dimensions in the data array. The type of each dimension should should follow the order of scan points, detector pixels, then time-of-flight (i.e. spectroscopy, spectrometry). The rank and dimension sizes (see symbol list) shown here are merely illustrative of coordination between related datasets.

      Type: NX_NUMBER Units: NX_ANY Dimensions: 1: nP; 2: i; 3: j; 4: tof;

      Returns:
      the value.

    • setData

      DataNode setData(org.eclipse.january.dataset.IDataset dataDataset)
      Data values from the detector. The rank and dimension ordering should follow a principle of slowest to fastest measurement axes and may be explicitly specified in application definitions. Mechanical scanning of objects (e.g. sample position/angle, incident beam energy, etc) tends to be the slowest part of an experiment and so any such scan axes should be allocated to the first dimensions of the array. Note that in some cases it may be useful to represent a 2D set of scan points as a single scan-axis in the data array, especially if the scan pattern doesn't fit a rectangular array nicely. Repetition of an experiment in a time series tends to be used similar to a slow scan axis and so will often be in the first dimension of the data array. The next fastest axes are typically the readout of the detector. A point detector will not add any dimensions (as it is just a single value per scan point) to the data array, a strip detector will add one dimension, an imaging detector will add two dimensions (e.g. X, Y axes) and detectors outputting higher dimensional data will add the corresponding number of dimensions. Note that the detector dimensions don't necessarily have to be written in order of the actual readout speeds - the slowest to fastest rule principle is only a guide. Finally, detectors that operate in a time-of-flight mode, such as a neutron spectrometer or a silicon drift detector (used for X-ray fluorescence) tend to have their dimension(s) added to the last dimensions in the data array. The type of each dimension should should follow the order of scan points, detector pixels, then time-of-flight (i.e. spectroscopy, spectrometry). The rank and dimension sizes (see symbol list) shown here are merely illustrative of coordination between related datasets.

      Type: NX_NUMBER Units: NX_ANY Dimensions: 1: nP; 2: i; 3: j; 4: tof;

      Parameters:
      dataDataset - the dataDataset

    • getDataScalar

      Number getDataScalar()
      Data values from the detector. The rank and dimension ordering should follow a principle of slowest to fastest measurement axes and may be explicitly specified in application definitions. Mechanical scanning of objects (e.g. sample position/angle, incident beam energy, etc) tends to be the slowest part of an experiment and so any such scan axes should be allocated to the first dimensions of the array. Note that in some cases it may be useful to represent a 2D set of scan points as a single scan-axis in the data array, especially if the scan pattern doesn't fit a rectangular array nicely. Repetition of an experiment in a time series tends to be used similar to a slow scan axis and so will often be in the first dimension of the data array. The next fastest axes are typically the readout of the detector. A point detector will not add any dimensions (as it is just a single value per scan point) to the data array, a strip detector will add one dimension, an imaging detector will add two dimensions (e.g. X, Y axes) and detectors outputting higher dimensional data will add the corresponding number of dimensions. Note that the detector dimensions don't necessarily have to be written in order of the actual readout speeds - the slowest to fastest rule principle is only a guide. Finally, detectors that operate in a time-of-flight mode, such as a neutron spectrometer or a silicon drift detector (used for X-ray fluorescence) tend to have their dimension(s) added to the last dimensions in the data array. The type of each dimension should should follow the order of scan points, detector pixels, then time-of-flight (i.e. spectroscopy, spectrometry). The rank and dimension sizes (see symbol list) shown here are merely illustrative of coordination between related datasets.

      Type: NX_NUMBER Units: NX_ANY Dimensions: 1: nP; 2: i; 3: j; 4: tof;

      Returns:
      the value.

    • setDataScalar

      DataNode setDataScalar(Number dataValue)
      Data values from the detector. The rank and dimension ordering should follow a principle of slowest to fastest measurement axes and may be explicitly specified in application definitions. Mechanical scanning of objects (e.g. sample position/angle, incident beam energy, etc) tends to be the slowest part of an experiment and so any such scan axes should be allocated to the first dimensions of the array. Note that in some cases it may be useful to represent a 2D set of scan points as a single scan-axis in the data array, especially if the scan pattern doesn't fit a rectangular array nicely. Repetition of an experiment in a time series tends to be used similar to a slow scan axis and so will often be in the first dimension of the data array. The next fastest axes are typically the readout of the detector. A point detector will not add any dimensions (as it is just a single value per scan point) to the data array, a strip detector will add one dimension, an imaging detector will add two dimensions (e.g. X, Y axes) and detectors outputting higher dimensional data will add the corresponding number of dimensions. Note that the detector dimensions don't necessarily have to be written in order of the actual readout speeds - the slowest to fastest rule principle is only a guide. Finally, detectors that operate in a time-of-flight mode, such as a neutron spectrometer or a silicon drift detector (used for X-ray fluorescence) tend to have their dimension(s) added to the last dimensions in the data array. The type of each dimension should should follow the order of scan points, detector pixels, then time-of-flight (i.e. spectroscopy, spectrometry). The rank and dimension sizes (see symbol list) shown here are merely illustrative of coordination between related datasets.

      Type: NX_NUMBER Units: NX_ANY Dimensions: 1: nP; 2: i; 3: j; 4: tof;

      Parameters:
      data - the data

    • getDataAttributeLong_name

      String getDataAttributeLong_name()
      Title of measurement
      Returns:
      the value.

    • setDataAttributeLong_name

      void setDataAttributeLong_name(String long_nameValue)
      Title of measurement
      Parameters:
      long_nameValue - the long_nameValue

    • getDataAttributeCheck_sum

      Long getDataAttributeCheck_sum()
      Integral of data as check of data integrity
      Returns:
      the value.

    • setDataAttributeCheck_sum

      void setDataAttributeCheck_sum(Long check_sumValue)
      Integral of data as check of data integrity
      Parameters:
      check_sumValue - the check_sumValue

    • getData_errors

      org.eclipse.january.dataset.Dataset getData_errors()
      The best estimate of the uncertainty in the data value (array size should match the data field). Where possible, this should be the standard deviation, which has the same units as the data. The form data_error is deprecated.

      Type: NX_NUMBER Units: NX_ANY Dimensions: 1: nP; 2: i; 3: j; 4: tof;

      Returns:
      the value.

    • setData_errors

      DataNode setData_errors(org.eclipse.january.dataset.IDataset data_errorsDataset)
      The best estimate of the uncertainty in the data value (array size should match the data field). Where possible, this should be the standard deviation, which has the same units as the data. The form data_error is deprecated.

      Type: NX_NUMBER Units: NX_ANY Dimensions: 1: nP; 2: i; 3: j; 4: tof;

      Parameters:
      data_errorsDataset - the data_errorsDataset

    • getData_errorsScalar

      Number getData_errorsScalar()
      The best estimate of the uncertainty in the data value (array size should match the data field). Where possible, this should be the standard deviation, which has the same units as the data. The form data_error is deprecated.

      Type: NX_NUMBER Units: NX_ANY Dimensions: 1: nP; 2: i; 3: j; 4: tof;

      Returns:
      the value.

    • setData_errorsScalar

      DataNode setData_errorsScalar(Number data_errorsValue)
      The best estimate of the uncertainty in the data value (array size should match the data field). Where possible, this should be the standard deviation, which has the same units as the data. The form data_error is deprecated.

      Type: NX_NUMBER Units: NX_ANY Dimensions: 1: nP; 2: i; 3: j; 4: tof;

      Parameters:
      data_errors - the data_errors

    • getX_pixel_offset

      org.eclipse.january.dataset.Dataset getX_pixel_offset()
      Offset from the detector center in x-direction. Can be multidimensional when needed.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setX_pixel_offset

      DataNode setX_pixel_offset(org.eclipse.january.dataset.IDataset x_pixel_offsetDataset)
      Offset from the detector center in x-direction. Can be multidimensional when needed.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Parameters:
      x_pixel_offsetDataset - the x_pixel_offsetDataset

    • getX_pixel_offsetScalar

      Double getX_pixel_offsetScalar()
      Offset from the detector center in x-direction. Can be multidimensional when needed.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setX_pixel_offsetScalar

      DataNode setX_pixel_offsetScalar(Double x_pixel_offsetValue)
      Offset from the detector center in x-direction. Can be multidimensional when needed.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Parameters:
      x_pixel_offset - the x_pixel_offset

    • getX_pixel_offsetAttributeAxis

      @Deprecated Long getX_pixel_offsetAttributeAxis()
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 1

      Returns:
      the value.

    • setX_pixel_offsetAttributeAxis

      @Deprecated void setX_pixel_offsetAttributeAxis(Long axisValue)
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 1

      Parameters:
      axisValue - the axisValue

    • getX_pixel_offsetAttributePrimary

      @Deprecated Long getX_pixel_offsetAttributePrimary()
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 1

      Returns:
      the value.

    • setX_pixel_offsetAttributePrimary

      @Deprecated void setX_pixel_offsetAttributePrimary(Long primaryValue)
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 1

      Parameters:
      primaryValue - the primaryValue

    • getX_pixel_offsetAttributeLong_name

      String getX_pixel_offsetAttributeLong_name()
      x-axis offset from detector center
      Returns:
      the value.

    • setX_pixel_offsetAttributeLong_name

      void setX_pixel_offsetAttributeLong_name(String long_nameValue)
      x-axis offset from detector center
      Parameters:
      long_nameValue - the long_nameValue

    • getY_pixel_offset

      org.eclipse.january.dataset.Dataset getY_pixel_offset()
      Offset from the detector center in the y-direction. Can be multidimensional when different values are required for each pixel.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setY_pixel_offset

      DataNode setY_pixel_offset(org.eclipse.january.dataset.IDataset y_pixel_offsetDataset)
      Offset from the detector center in the y-direction. Can be multidimensional when different values are required for each pixel.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Parameters:
      y_pixel_offsetDataset - the y_pixel_offsetDataset

    • getY_pixel_offsetScalar

      Double getY_pixel_offsetScalar()
      Offset from the detector center in the y-direction. Can be multidimensional when different values are required for each pixel.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setY_pixel_offsetScalar

      DataNode setY_pixel_offsetScalar(Double y_pixel_offsetValue)
      Offset from the detector center in the y-direction. Can be multidimensional when different values are required for each pixel.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Parameters:
      y_pixel_offset - the y_pixel_offset

    • getY_pixel_offsetAttributeAxis

      @Deprecated Long getY_pixel_offsetAttributeAxis()
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 2

      Returns:
      the value.

    • setY_pixel_offsetAttributeAxis

      @Deprecated void setY_pixel_offsetAttributeAxis(Long axisValue)
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 2

      Parameters:
      axisValue - the axisValue

    • getY_pixel_offsetAttributePrimary

      @Deprecated Long getY_pixel_offsetAttributePrimary()
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 1

      Returns:
      the value.

    • setY_pixel_offsetAttributePrimary

      @Deprecated void setY_pixel_offsetAttributePrimary(Long primaryValue)
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 1

      Parameters:
      primaryValue - the primaryValue

    • getY_pixel_offsetAttributeLong_name

      String getY_pixel_offsetAttributeLong_name()
      y-axis offset from detector center
      Returns:
      the value.

    • setY_pixel_offsetAttributeLong_name

      void setY_pixel_offsetAttributeLong_name(String long_nameValue)
      y-axis offset from detector center
      Parameters:
      long_nameValue - the long_nameValue

    • getZ_pixel_offset

      org.eclipse.january.dataset.Dataset getZ_pixel_offset()
      Offset from the detector center in the z-direction. Can be multidimensional when different values are required for each pixel.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setZ_pixel_offset

      DataNode setZ_pixel_offset(org.eclipse.january.dataset.IDataset z_pixel_offsetDataset)
      Offset from the detector center in the z-direction. Can be multidimensional when different values are required for each pixel.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Parameters:
      z_pixel_offsetDataset - the z_pixel_offsetDataset

    • getZ_pixel_offsetScalar

      Double getZ_pixel_offsetScalar()
      Offset from the detector center in the z-direction. Can be multidimensional when different values are required for each pixel.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setZ_pixel_offsetScalar

      DataNode setZ_pixel_offsetScalar(Double z_pixel_offsetValue)
      Offset from the detector center in the z-direction. Can be multidimensional when different values are required for each pixel.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Parameters:
      z_pixel_offset - the z_pixel_offset

    • getZ_pixel_offsetAttributeAxis

      @Deprecated Long getZ_pixel_offsetAttributeAxis()
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 3

      Returns:
      the value.

    • setZ_pixel_offsetAttributeAxis

      @Deprecated void setZ_pixel_offsetAttributeAxis(Long axisValue)
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 3

      Parameters:
      axisValue - the axisValue

    • getZ_pixel_offsetAttributePrimary

      @Deprecated Long getZ_pixel_offsetAttributePrimary()
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 1

      Returns:
      the value.

    • setZ_pixel_offsetAttributePrimary

      @Deprecated void setZ_pixel_offsetAttributePrimary(Long primaryValue)
      Deprecated.
      see: https://github.com/nexusformat/definitions/issues/436

      Enumeration:

      • 1

      Parameters:
      primaryValue - the primaryValue

    • getZ_pixel_offsetAttributeLong_name

      String getZ_pixel_offsetAttributeLong_name()
      y-axis offset from detector center
      Returns:
      the value.

    • setZ_pixel_offsetAttributeLong_name

      void setZ_pixel_offsetAttributeLong_name(String long_nameValue)
      y-axis offset from detector center
      Parameters:
      long_nameValue - the long_nameValue

    • getDistance

      org.eclipse.january.dataset.Dataset getDistance()
      This is the distance to the previous component in the instrument; most often the sample. The usage depends on the nature of the detector: Most often it is the distance of the detector assembly. But there are irregular detectors. In this case the distance must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: nP; 2: i; 3: j;

      Returns:
      the value.

    • setDistance

      DataNode setDistance(org.eclipse.january.dataset.IDataset distanceDataset)
      This is the distance to the previous component in the instrument; most often the sample. The usage depends on the nature of the detector: Most often it is the distance of the detector assembly. But there are irregular detectors. In this case the distance must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: nP; 2: i; 3: j;

      Parameters:
      distanceDataset - the distanceDataset

    • getDistanceScalar

      Double getDistanceScalar()
      This is the distance to the previous component in the instrument; most often the sample. The usage depends on the nature of the detector: Most often it is the distance of the detector assembly. But there are irregular detectors. In this case the distance must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: nP; 2: i; 3: j;

      Returns:
      the value.

    • setDistanceScalar

      DataNode setDistanceScalar(Double distanceValue)
      This is the distance to the previous component in the instrument; most often the sample. The usage depends on the nature of the detector: Most often it is the distance of the detector assembly. But there are irregular detectors. In this case the distance must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: nP; 2: i; 3: j;

      Parameters:
      distance - the distance

    • getPolar_angle

      org.eclipse.january.dataset.Dataset getPolar_angle()
      This is the polar angle of the detector towards the previous component in the instrument; most often the sample. The usage depends on the nature of the detector. Most often it is the polar_angle of the detector assembly. But there are irregular detectors. In this case, the polar_angle must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_ANGLE Dimensions: 1: nP; 2: i; 3: j;

      Returns:
      the value.

    • setPolar_angle

      DataNode setPolar_angle(org.eclipse.january.dataset.IDataset polar_angleDataset)
      This is the polar angle of the detector towards the previous component in the instrument; most often the sample. The usage depends on the nature of the detector. Most often it is the polar_angle of the detector assembly. But there are irregular detectors. In this case, the polar_angle must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_ANGLE Dimensions: 1: nP; 2: i; 3: j;

      Parameters:
      polar_angleDataset - the polar_angleDataset

    • getPolar_angleScalar

      Double getPolar_angleScalar()
      This is the polar angle of the detector towards the previous component in the instrument; most often the sample. The usage depends on the nature of the detector. Most often it is the polar_angle of the detector assembly. But there are irregular detectors. In this case, the polar_angle must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_ANGLE Dimensions: 1: nP; 2: i; 3: j;

      Returns:
      the value.

    • setPolar_angleScalar

      DataNode setPolar_angleScalar(Double polar_angleValue)
      This is the polar angle of the detector towards the previous component in the instrument; most often the sample. The usage depends on the nature of the detector. Most often it is the polar_angle of the detector assembly. But there are irregular detectors. In this case, the polar_angle must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_ANGLE Dimensions: 1: nP; 2: i; 3: j;

      Parameters:
      polar_angle - the polar_angle

    • getAzimuthal_angle

      org.eclipse.january.dataset.Dataset getAzimuthal_angle()
      This is the azimuthal angle angle of the detector towards the previous component in the instrument; most often the sample. The usage depends on the nature of the detector. Most often it is the azimuthal_angle of the detector assembly. But there are irregular detectors. In this case, the azimuthal_angle must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_ANGLE Dimensions: 1: nP; 2: i; 3: j;

      Returns:
      the value.

    • setAzimuthal_angle

      DataNode setAzimuthal_angle(org.eclipse.january.dataset.IDataset azimuthal_angleDataset)
      This is the azimuthal angle angle of the detector towards the previous component in the instrument; most often the sample. The usage depends on the nature of the detector. Most often it is the azimuthal_angle of the detector assembly. But there are irregular detectors. In this case, the azimuthal_angle must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_ANGLE Dimensions: 1: nP; 2: i; 3: j;

      Parameters:
      azimuthal_angleDataset - the azimuthal_angleDataset

    • getAzimuthal_angleScalar

      Double getAzimuthal_angleScalar()
      This is the azimuthal angle angle of the detector towards the previous component in the instrument; most often the sample. The usage depends on the nature of the detector. Most often it is the azimuthal_angle of the detector assembly. But there are irregular detectors. In this case, the azimuthal_angle must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_ANGLE Dimensions: 1: nP; 2: i; 3: j;

      Returns:
      the value.

    • setAzimuthal_angleScalar

      DataNode setAzimuthal_angleScalar(Double azimuthal_angleValue)
      This is the azimuthal angle angle of the detector towards the previous component in the instrument; most often the sample. The usage depends on the nature of the detector. Most often it is the azimuthal_angle of the detector assembly. But there are irregular detectors. In this case, the azimuthal_angle must be specified for each detector pixel. Note, it is recommended to use NXtransformations instead.

      Type: NX_FLOAT Units: NX_ANGLE Dimensions: 1: nP; 2: i; 3: j;

      Parameters:
      azimuthal_angle - the azimuthal_angle

    • getDescription

      org.eclipse.january.dataset.Dataset getDescription()
      name/manufacturer/model/etc. information
      Specified by:
      getDescription in interface NXcomponent
      Returns:
      the value.

    • setDescription

      DataNode setDescription(org.eclipse.january.dataset.IDataset descriptionDataset)
      name/manufacturer/model/etc. information
      Specified by:
      setDescription in interface NXcomponent
      Parameters:
      descriptionDataset - the descriptionDataset

    • getDescriptionScalar

      String getDescriptionScalar()
      name/manufacturer/model/etc. information
      Specified by:
      getDescriptionScalar in interface NXcomponent
      Returns:
      the value.

    • setDescriptionScalar

      DataNode setDescriptionScalar(String descriptionValue)
      name/manufacturer/model/etc. information
      Specified by:
      setDescriptionScalar in interface NXcomponent
      Parameters:
      description - the description

    • getSerial_number

      org.eclipse.january.dataset.Dataset getSerial_number()
      Serial number for the detector
      Returns:
      the value.

    • setSerial_number

      DataNode setSerial_number(org.eclipse.january.dataset.IDataset serial_numberDataset)
      Serial number for the detector
      Parameters:
      serial_numberDataset - the serial_numberDataset

    • getSerial_numberScalar

      String getSerial_numberScalar()
      Serial number for the detector
      Returns:
      the value.

    • setSerial_numberScalar

      DataNode setSerial_numberScalar(String serial_numberValue)
      Serial number for the detector
      Parameters:
      serial_number - the serial_number

    • getLocal_name

      org.eclipse.january.dataset.Dataset getLocal_name()
      Local name for the detector
      Returns:
      the value.

    • setLocal_name

      DataNode setLocal_name(org.eclipse.january.dataset.IDataset local_nameDataset)
      Local name for the detector
      Parameters:
      local_nameDataset - the local_nameDataset

    • getLocal_nameScalar

      String getLocal_nameScalar()
      Local name for the detector
      Returns:
      the value.

    • setLocal_nameScalar

      DataNode setLocal_nameScalar(String local_nameValue)
      Local name for the detector
      Parameters:
      local_name - the local_name

    • getGeometry

      @Deprecated NXgeometry getGeometry()
      Deprecated.
      Use the field `depends_on` and :ref:`NXtransformations` to position the detector and NXoff_geometry to describe its shape instead
      Position and orientation of detector
      Returns:
      the value.

    • setGeometry

      @Deprecated void setGeometry(NXgeometry geometryGroup)
      Deprecated.
      Use the field `depends_on` and :ref:`NXtransformations` to position the detector and NXoff_geometry to describe its shape instead
      Position and orientation of detector
      Parameters:
      geometryGroup - the geometryGroup

    • getGeometry

      @Deprecated NXgeometry getGeometry(String name)
      Deprecated.
      Use the field `depends_on` and :ref:`NXtransformations` to position the detector and NXoff_geometry to describe its shape instead
      Get a NXgeometry node by name:
      • Position and orientation of detector
      Parameters:
      name - the name of the node.
      Returns:
      a map from node names to the NXgeometry for that node.

    • setGeometry

      @Deprecated void setGeometry(String name, NXgeometry geometry)
      Deprecated.
      Use the field `depends_on` and :ref:`NXtransformations` to position the detector and NXoff_geometry to describe its shape instead
      Set a NXgeometry node by name:
      • Position and orientation of detector
      Parameters:
      name - the name of the node
      geometry - the value to set

    • getAllGeometry

      @Deprecated Map<String,NXgeometry> getAllGeometry()
      Deprecated.
      Use the field `depends_on` and :ref:`NXtransformations` to position the detector and NXoff_geometry to describe its shape instead
      Get all NXgeometry nodes:
      • Position and orientation of detector
      Returns:
      a map from node names to the NXgeometry for that node.

    • setAllGeometry

      @Deprecated void setAllGeometry(Map<String,NXgeometry> geometry)
      Deprecated.
      Use the field `depends_on` and :ref:`NXtransformations` to position the detector and NXoff_geometry to describe its shape instead
      Set multiple child nodes of a particular type.
      • Position and orientation of detector
      Parameters:
      geometry - the child nodes to add

    • getSolid_angle

      org.eclipse.january.dataset.Dataset getSolid_angle()
      Solid angle subtended by the detector at the sample

      Type: NX_FLOAT Units: NX_SOLID_ANGLE Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setSolid_angle

      DataNode setSolid_angle(org.eclipse.january.dataset.IDataset solid_angleDataset)
      Solid angle subtended by the detector at the sample

      Type: NX_FLOAT Units: NX_SOLID_ANGLE Dimensions: 1: i; 2: j;

      Parameters:
      solid_angleDataset - the solid_angleDataset

    • getSolid_angleScalar

      Double getSolid_angleScalar()
      Solid angle subtended by the detector at the sample

      Type: NX_FLOAT Units: NX_SOLID_ANGLE Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setSolid_angleScalar

      DataNode setSolid_angleScalar(Double solid_angleValue)
      Solid angle subtended by the detector at the sample

      Type: NX_FLOAT Units: NX_SOLID_ANGLE Dimensions: 1: i; 2: j;

      Parameters:
      solid_angle - the solid_angle

    • getX_pixel_size

      org.eclipse.january.dataset.Dataset getX_pixel_size()
      Size of each detector pixel. If it is scalar all pixels are the same size.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setX_pixel_size

      DataNode setX_pixel_size(org.eclipse.january.dataset.IDataset x_pixel_sizeDataset)
      Size of each detector pixel. If it is scalar all pixels are the same size.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Parameters:
      x_pixel_sizeDataset - the x_pixel_sizeDataset

    • getX_pixel_sizeScalar

      Double getX_pixel_sizeScalar()
      Size of each detector pixel. If it is scalar all pixels are the same size.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setX_pixel_sizeScalar

      DataNode setX_pixel_sizeScalar(Double x_pixel_sizeValue)
      Size of each detector pixel. If it is scalar all pixels are the same size.

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Parameters:
      x_pixel_size - the x_pixel_size

    • getY_pixel_size

      org.eclipse.january.dataset.Dataset getY_pixel_size()
      Size of each detector pixel. If it is scalar all pixels are the same size

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setY_pixel_size

      DataNode setY_pixel_size(org.eclipse.january.dataset.IDataset y_pixel_sizeDataset)
      Size of each detector pixel. If it is scalar all pixels are the same size

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Parameters:
      y_pixel_sizeDataset - the y_pixel_sizeDataset

    • getY_pixel_sizeScalar

      Double getY_pixel_sizeScalar()
      Size of each detector pixel. If it is scalar all pixels are the same size

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setY_pixel_sizeScalar

      DataNode setY_pixel_sizeScalar(Double y_pixel_sizeValue)
      Size of each detector pixel. If it is scalar all pixels are the same size

      Type: NX_FLOAT Units: NX_LENGTH Dimensions: 1: i; 2: j;

      Parameters:
      y_pixel_size - the y_pixel_size

    • getDead_time

      org.eclipse.january.dataset.Dataset getDead_time()
      Detector dead time

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP; 2: i; 3: j;

      Returns:
      the value.

    • setDead_time

      DataNode setDead_time(org.eclipse.january.dataset.IDataset dead_timeDataset)
      Detector dead time

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP; 2: i; 3: j;

      Parameters:
      dead_timeDataset - the dead_timeDataset

    • getDead_timeScalar

      Double getDead_timeScalar()
      Detector dead time

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP; 2: i; 3: j;

      Returns:
      the value.

    • setDead_timeScalar

      DataNode setDead_timeScalar(Double dead_timeValue)
      Detector dead time

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP; 2: i; 3: j;

      Parameters:
      dead_time - the dead_time

    • getGas_pressure

      org.eclipse.january.dataset.Dataset getGas_pressure()
      Detector gas pressure

      Type: NX_FLOAT Units: NX_PRESSURE Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setGas_pressure

      DataNode setGas_pressure(org.eclipse.january.dataset.IDataset gas_pressureDataset)
      Detector gas pressure

      Type: NX_FLOAT Units: NX_PRESSURE Dimensions: 1: i; 2: j;

      Parameters:
      gas_pressureDataset - the gas_pressureDataset

    • getGas_pressureScalar

      Double getGas_pressureScalar()
      Detector gas pressure

      Type: NX_FLOAT Units: NX_PRESSURE Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setGas_pressureScalar

      DataNode setGas_pressureScalar(Double gas_pressureValue)
      Detector gas pressure

      Type: NX_FLOAT Units: NX_PRESSURE Dimensions: 1: i; 2: j;

      Parameters:
      gas_pressure - the gas_pressure

    • getDetection_gas_path

      org.eclipse.january.dataset.Dataset getDetection_gas_path()
      maximum drift space dimension

      Type: NX_FLOAT Units: NX_LENGTH

      Returns:
      the value.

    • setDetection_gas_path

      DataNode setDetection_gas_path(org.eclipse.january.dataset.IDataset detection_gas_pathDataset)
      maximum drift space dimension

      Type: NX_FLOAT Units: NX_LENGTH

      Parameters:
      detection_gas_pathDataset - the detection_gas_pathDataset

    • getDetection_gas_pathScalar

      Double getDetection_gas_pathScalar()
      maximum drift space dimension

      Type: NX_FLOAT Units: NX_LENGTH

      Returns:
      the value.

    • setDetection_gas_pathScalar

      DataNode setDetection_gas_pathScalar(Double detection_gas_pathValue)
      maximum drift space dimension

      Type: NX_FLOAT Units: NX_LENGTH

      Parameters:
      detection_gas_path - the detection_gas_path

    • getCrate

      org.eclipse.january.dataset.Dataset getCrate()
      Crate number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setCrate

      DataNode setCrate(org.eclipse.january.dataset.IDataset crateDataset)
      Crate number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Parameters:
      crateDataset - the crateDataset

    • getCrateScalar

      Long getCrateScalar()
      Crate number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setCrateScalar

      DataNode setCrateScalar(Long crateValue)
      Crate number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Parameters:
      crate - the crate

    • getCrateAttributeLocal_name

      String getCrateAttributeLocal_name()
      Equivalent local term
      Returns:
      the value.

    • setCrateAttributeLocal_name

      void setCrateAttributeLocal_name(String local_nameValue)
      Equivalent local term
      Parameters:
      local_nameValue - the local_nameValue

    • getSlot

      org.eclipse.january.dataset.Dataset getSlot()
      Slot number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setSlot

      DataNode setSlot(org.eclipse.january.dataset.IDataset slotDataset)
      Slot number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Parameters:
      slotDataset - the slotDataset

    • getSlotScalar

      Long getSlotScalar()
      Slot number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setSlotScalar

      DataNode setSlotScalar(Long slotValue)
      Slot number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Parameters:
      slot - the slot

    • getSlotAttributeLocal_name

      String getSlotAttributeLocal_name()
      Equivalent local term
      Returns:
      the value.

    • setSlotAttributeLocal_name

      void setSlotAttributeLocal_name(String local_nameValue)
      Equivalent local term
      Parameters:
      local_nameValue - the local_nameValue

    • getInput

      org.eclipse.january.dataset.Dataset getInput()
      Input number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setInput

      DataNode setInput(org.eclipse.january.dataset.IDataset inputDataset)
      Input number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Parameters:
      inputDataset - the inputDataset

    • getInputScalar

      Long getInputScalar()
      Input number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setInputScalar

      DataNode setInputScalar(Long inputValue)
      Input number of detector

      Type: NX_INT Dimensions: 1: i; 2: j;

      Parameters:
      input - the input

    • getInputAttributeLocal_name

      String getInputAttributeLocal_name()
      Equivalent local term
      Returns:
      the value.

    • setInputAttributeLocal_name

      void setInputAttributeLocal_name(String local_nameValue)
      Equivalent local term
      Parameters:
      local_nameValue - the local_nameValue

    • getType

      org.eclipse.january.dataset.Dataset getType()
      Description of type such as He3 gas cylinder, He3 PSD, scintillator, fission chamber, proportion counter, ion chamber, ccd, pixel, image plate, CMOS, ...
      Returns:
      the value.

    • setType

      DataNode setType(org.eclipse.january.dataset.IDataset typeDataset)
      Description of type such as He3 gas cylinder, He3 PSD, scintillator, fission chamber, proportion counter, ion chamber, ccd, pixel, image plate, CMOS, ...
      Parameters:
      typeDataset - the typeDataset

    • getTypeScalar

      String getTypeScalar()
      Description of type such as He3 gas cylinder, He3 PSD, scintillator, fission chamber, proportion counter, ion chamber, ccd, pixel, image plate, CMOS, ...
      Returns:
      the value.

    • setTypeScalar

      DataNode setTypeScalar(String typeValue)
      Description of type such as He3 gas cylinder, He3 PSD, scintillator, fission chamber, proportion counter, ion chamber, ccd, pixel, image plate, CMOS, ...
      Parameters:
      type - the type

    • getChannelname_channel

      NXdetector_channel getChannelname_channel()
      Group containing the description and metadata for a single channel from a multi-channel detector. Given an :ref:`NXdata` group linked as part of an NXdetector group that has an axis with named channels (see the example in :ref:`NXdata invalid input: '<'/NXdata@default_slice-attribute>`), the NXdetector will have a series of NXdetector_channel groups, one for each channel, named CHANNELNAME_channel.
      Returns:
      the value.

    • setChannelname_channel

      void setChannelname_channel(NXdetector_channel channelname_channelGroup)
      Group containing the description and metadata for a single channel from a multi-channel detector. Given an :ref:`NXdata` group linked as part of an NXdetector group that has an axis with named channels (see the example in :ref:`NXdata invalid input: '<'/NXdata@default_slice-attribute>`), the NXdetector will have a series of NXdetector_channel groups, one for each channel, named CHANNELNAME_channel.
      Parameters:
      channelname_channelGroup - the channelname_channelGroup

    • getEfficiency

      NXdata getEfficiency()
      Spectral efficiency of detector with respect to e.g. wavelength
      Returns:
      the value.

    • setEfficiency

      void setEfficiency(NXdata efficiencyGroup)
      Spectral efficiency of detector with respect to e.g. wavelength
      Parameters:
      efficiencyGroup - the efficiencyGroup

    • getReal_time

      org.eclipse.january.dataset.Dataset getReal_time()
      Real-time of the exposure (use this if exposure time varies for each array element, otherwise use ``count_time`` field). Most often there is a single real time value that is constant across an entire image frame. In such cases, only a 1-D array is needed. But there are detectors in which the real time changes per pixel. In that case, more than one dimension is needed. Therefore the rank of this field should be less than or equal to (detector rank + 1).

      Type: NX_NUMBER Units: NX_TIME Dimensions: 1: nP; 2: i; 3: j;

      Returns:
      the value.

    • setReal_time

      DataNode setReal_time(org.eclipse.january.dataset.IDataset real_timeDataset)
      Real-time of the exposure (use this if exposure time varies for each array element, otherwise use ``count_time`` field). Most often there is a single real time value that is constant across an entire image frame. In such cases, only a 1-D array is needed. But there are detectors in which the real time changes per pixel. In that case, more than one dimension is needed. Therefore the rank of this field should be less than or equal to (detector rank + 1).

      Type: NX_NUMBER Units: NX_TIME Dimensions: 1: nP; 2: i; 3: j;

      Parameters:
      real_timeDataset - the real_timeDataset

    • getReal_timeScalar

      Number getReal_timeScalar()
      Real-time of the exposure (use this if exposure time varies for each array element, otherwise use ``count_time`` field). Most often there is a single real time value that is constant across an entire image frame. In such cases, only a 1-D array is needed. But there are detectors in which the real time changes per pixel. In that case, more than one dimension is needed. Therefore the rank of this field should be less than or equal to (detector rank + 1).

      Type: NX_NUMBER Units: NX_TIME Dimensions: 1: nP; 2: i; 3: j;

      Returns:
      the value.

    • setReal_timeScalar

      DataNode setReal_timeScalar(Number real_timeValue)
      Real-time of the exposure (use this if exposure time varies for each array element, otherwise use ``count_time`` field). Most often there is a single real time value that is constant across an entire image frame. In such cases, only a 1-D array is needed. But there are detectors in which the real time changes per pixel. In that case, more than one dimension is needed. Therefore the rank of this field should be less than or equal to (detector rank + 1).

      Type: NX_NUMBER Units: NX_TIME Dimensions: 1: nP; 2: i; 3: j;

      Parameters:
      real_time - the real_time

    • getStart_time

      org.eclipse.january.dataset.Dataset getStart_time()
      start time for each frame, with the ``start`` attribute as absolute reference

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Returns:
      the value.

    • setStart_time

      DataNode setStart_time(org.eclipse.january.dataset.IDataset start_timeDataset)
      start time for each frame, with the ``start`` attribute as absolute reference

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Parameters:
      start_timeDataset - the start_timeDataset

    • getStart_timeScalar

      Double getStart_timeScalar()
      start time for each frame, with the ``start`` attribute as absolute reference

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Returns:
      the value.

    • setStart_timeScalar

      DataNode setStart_timeScalar(Double start_timeValue)
      start time for each frame, with the ``start`` attribute as absolute reference

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Parameters:
      start_time - the start_time

    • getStart_timeAttributeStart

      Date getStart_timeAttributeStart()
      Returns:
      the value.

    • setStart_timeAttributeStart

      void setStart_timeAttributeStart(Date startValue)
      Parameters:
      startValue - the startValue

    • getStop_time

      org.eclipse.january.dataset.Dataset getStop_time()
      stop time for each frame, with the ``start`` attribute as absolute reference

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Returns:
      the value.

    • setStop_time

      DataNode setStop_time(org.eclipse.january.dataset.IDataset stop_timeDataset)
      stop time for each frame, with the ``start`` attribute as absolute reference

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Parameters:
      stop_timeDataset - the stop_timeDataset

    • getStop_timeScalar

      Double getStop_timeScalar()
      stop time for each frame, with the ``start`` attribute as absolute reference

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Returns:
      the value.

    • setStop_timeScalar

      DataNode setStop_timeScalar(Double stop_timeValue)
      stop time for each frame, with the ``start`` attribute as absolute reference

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Parameters:
      stop_time - the stop_time

    • getStop_timeAttributeStart

      Date getStop_timeAttributeStart()
      Returns:
      the value.

    • setStop_timeAttributeStart

      void setStop_timeAttributeStart(Date startValue)
      Parameters:
      startValue - the startValue

    • getCalibration_date

      org.eclipse.january.dataset.Dataset getCalibration_date()
      date of last calibration (geometry and/or efficiency) measurements

      Type: NX_DATE_TIME

      Returns:
      the value.

    • setCalibration_date

      DataNode setCalibration_date(org.eclipse.january.dataset.IDataset calibration_dateDataset)
      date of last calibration (geometry and/or efficiency) measurements

      Type: NX_DATE_TIME

      Parameters:
      calibration_dateDataset - the calibration_dateDataset

    • getCalibration_dateScalar

      Date getCalibration_dateScalar()
      date of last calibration (geometry and/or efficiency) measurements

      Type: NX_DATE_TIME

      Returns:
      the value.

    • setCalibration_dateScalar

      DataNode setCalibration_dateScalar(Date calibration_dateValue)
      date of last calibration (geometry and/or efficiency) measurements

      Type: NX_DATE_TIME

      Parameters:
      calibration_date - the calibration_date

    • getCalibration_method

      NXnote getCalibration_method()
      summary of conversion of array data to pixels (e.g. polynomial approximations) and location of details of the calibrations
      Returns:
      the value.

    • setCalibration_method

      void setCalibration_method(NXnote calibration_methodGroup)
      summary of conversion of array data to pixels (e.g. polynomial approximations) and location of details of the calibrations
      Parameters:
      calibration_methodGroup - the calibration_methodGroup

    • getLayout

      org.eclipse.january.dataset.Dataset getLayout()
      How the detector is represented

      Enumeration:

      • point
      • linear
      • area

      Returns:
      the value.

    • setLayout

      DataNode setLayout(org.eclipse.january.dataset.IDataset layoutDataset)
      How the detector is represented

      Enumeration:

      • point
      • linear
      • area

      Parameters:
      layoutDataset - the layoutDataset

    • getLayoutScalar

      String getLayoutScalar()
      How the detector is represented

      Enumeration:

      • point
      • linear
      • area

      Returns:
      the value.

    • setLayoutScalar

      DataNode setLayoutScalar(String layoutValue)
      How the detector is represented

      Enumeration:

      • point
      • linear
      • area

      Parameters:
      layout - the layout

    • getCount_time

      org.eclipse.january.dataset.Dataset getCount_time()
      Elapsed actual counting time

      Type: NX_NUMBER Units: NX_TIME Dimensions: 1: nP;

      Returns:
      the value.

    • setCount_time

      DataNode setCount_time(org.eclipse.january.dataset.IDataset count_timeDataset)
      Elapsed actual counting time

      Type: NX_NUMBER Units: NX_TIME Dimensions: 1: nP;

      Parameters:
      count_timeDataset - the count_timeDataset

    • getCount_timeScalar

      Number getCount_timeScalar()
      Elapsed actual counting time

      Type: NX_NUMBER Units: NX_TIME Dimensions: 1: nP;

      Returns:
      the value.

    • setCount_timeScalar

      DataNode setCount_timeScalar(Number count_timeValue)
      Elapsed actual counting time

      Type: NX_NUMBER Units: NX_TIME Dimensions: 1: nP;

      Parameters:
      count_time - the count_time

    • getData_file

      NXnote getData_file()
      Returns:
      the value.

    • setData_file

      void setData_file(NXnote data_fileGroup)
      Parameters:
      data_fileGroup - the data_fileGroup

    • getCollection

      NXcollection getCollection()
      Use this group to provide other data related to this NXdetector group.
      Returns:
      the value.

    • setCollection

      void setCollection(NXcollection collectionGroup)
      Use this group to provide other data related to this NXdetector group.
      Parameters:
      collectionGroup - the collectionGroup

    • getCollection

      NXcollection getCollection(String name)
      Get a NXcollection node by name:
      • Use this group to provide other data related to this NXdetector group.
      Parameters:
      name - the name of the node.
      Returns:
      a map from node names to the NXcollection for that node.

    • setCollection

      void setCollection(String name, NXcollection collection)
      Set a NXcollection node by name:
      • Use this group to provide other data related to this NXdetector group.
      Parameters:
      name - the name of the node
      collection - the value to set

    • getAllCollection

      Map<String,NXcollection> getAllCollection()
      Get all NXcollection nodes:
      • Use this group to provide other data related to this NXdetector group.
      Returns:
      a map from node names to the NXcollection for that node.

    • setAllCollection

      void setAllCollection(Map<String,NXcollection> collection)
      Set multiple child nodes of a particular type.
      • Use this group to provide other data related to this NXdetector group.
      Parameters:
      collection - the child nodes to add

    • getSequence_number

      org.eclipse.january.dataset.Dataset getSequence_number()
      In order to properly sort the order of the images taken in (for example) a tomography experiment, a sequence number is stored with each image.

      Type: NX_INT Dimensions: 1: nP;

      Returns:
      the value.

    • setSequence_number

      DataNode setSequence_number(org.eclipse.january.dataset.IDataset sequence_numberDataset)
      In order to properly sort the order of the images taken in (for example) a tomography experiment, a sequence number is stored with each image.

      Type: NX_INT Dimensions: 1: nP;

      Parameters:
      sequence_numberDataset - the sequence_numberDataset

    • getSequence_numberScalar

      Long getSequence_numberScalar()
      In order to properly sort the order of the images taken in (for example) a tomography experiment, a sequence number is stored with each image.

      Type: NX_INT Dimensions: 1: nP;

      Returns:
      the value.

    • setSequence_numberScalar

      DataNode setSequence_numberScalar(Long sequence_numberValue)
      In order to properly sort the order of the images taken in (for example) a tomography experiment, a sequence number is stored with each image.

      Type: NX_INT Dimensions: 1: nP;

      Parameters:
      sequence_number - the sequence_number

    • getBeam_center_x

      org.eclipse.january.dataset.Dataset getBeam_center_x()
      This is the x position where the direct beam would hit the detector. This is a length and can be outside of the actual detector. The length can be in physical units or pixels as documented by the units attribute.

      Type: NX_FLOAT Units: NX_LENGTH

      Returns:
      the value.

    • setBeam_center_x

      DataNode setBeam_center_x(org.eclipse.january.dataset.IDataset beam_center_xDataset)
      This is the x position where the direct beam would hit the detector. This is a length and can be outside of the actual detector. The length can be in physical units or pixels as documented by the units attribute.

      Type: NX_FLOAT Units: NX_LENGTH

      Parameters:
      beam_center_xDataset - the beam_center_xDataset

    • getBeam_center_xScalar

      Double getBeam_center_xScalar()
      This is the x position where the direct beam would hit the detector. This is a length and can be outside of the actual detector. The length can be in physical units or pixels as documented by the units attribute.

      Type: NX_FLOAT Units: NX_LENGTH

      Returns:
      the value.

    • setBeam_center_xScalar

      DataNode setBeam_center_xScalar(Double beam_center_xValue)
      This is the x position where the direct beam would hit the detector. This is a length and can be outside of the actual detector. The length can be in physical units or pixels as documented by the units attribute.

      Type: NX_FLOAT Units: NX_LENGTH

      Parameters:
      beam_center_x - the beam_center_x

    • getBeam_center_y

      org.eclipse.january.dataset.Dataset getBeam_center_y()
      This is the y position where the direct beam would hit the detector. This is a length and can be outside of the actual detector. The length can be in physical units or pixels as documented by the units attribute.

      Type: NX_FLOAT Units: NX_LENGTH

      Returns:
      the value.

    • setBeam_center_y

      DataNode setBeam_center_y(org.eclipse.january.dataset.IDataset beam_center_yDataset)
      This is the y position where the direct beam would hit the detector. This is a length and can be outside of the actual detector. The length can be in physical units or pixels as documented by the units attribute.

      Type: NX_FLOAT Units: NX_LENGTH

      Parameters:
      beam_center_yDataset - the beam_center_yDataset

    • getBeam_center_yScalar

      Double getBeam_center_yScalar()
      This is the y position where the direct beam would hit the detector. This is a length and can be outside of the actual detector. The length can be in physical units or pixels as documented by the units attribute.

      Type: NX_FLOAT Units: NX_LENGTH

      Returns:
      the value.

    • setBeam_center_yScalar

      DataNode setBeam_center_yScalar(Double beam_center_yValue)
      This is the y position where the direct beam would hit the detector. This is a length and can be outside of the actual detector. The length can be in physical units or pixels as documented by the units attribute.

      Type: NX_FLOAT Units: NX_LENGTH

      Parameters:
      beam_center_y - the beam_center_y

    • getFrame_start_number

      org.eclipse.january.dataset.Dataset getFrame_start_number()
      This is the start number of the first frame of a scan. In protein crystallography measurements one often scans a couple of frames on a give sample, then does something else, then returns to the same sample and scans some more frames. Each time with a new data file. This number helps concatenating such measurements.

      Type: NX_INT

      Returns:
      the value.

    • setFrame_start_number

      DataNode setFrame_start_number(org.eclipse.january.dataset.IDataset frame_start_numberDataset)
      This is the start number of the first frame of a scan. In protein crystallography measurements one often scans a couple of frames on a give sample, then does something else, then returns to the same sample and scans some more frames. Each time with a new data file. This number helps concatenating such measurements.

      Type: NX_INT

      Parameters:
      frame_start_numberDataset - the frame_start_numberDataset

    • getFrame_start_numberScalar

      Long getFrame_start_numberScalar()
      This is the start number of the first frame of a scan. In protein crystallography measurements one often scans a couple of frames on a give sample, then does something else, then returns to the same sample and scans some more frames. Each time with a new data file. This number helps concatenating such measurements.

      Type: NX_INT

      Returns:
      the value.

    • setFrame_start_numberScalar

      DataNode setFrame_start_numberScalar(Long frame_start_numberValue)
      This is the start number of the first frame of a scan. In protein crystallography measurements one often scans a couple of frames on a give sample, then does something else, then returns to the same sample and scans some more frames. Each time with a new data file. This number helps concatenating such measurements.

      Type: NX_INT

      Parameters:
      frame_start_number - the frame_start_number

    • getDiameter

      org.eclipse.january.dataset.Dataset getDiameter()
      The diameter of a cylindrical detector

      Type: NX_FLOAT Units: NX_LENGTH

      Returns:
      the value.

    • setDiameter

      DataNode setDiameter(org.eclipse.january.dataset.IDataset diameterDataset)
      The diameter of a cylindrical detector

      Type: NX_FLOAT Units: NX_LENGTH

      Parameters:
      diameterDataset - the diameterDataset

    • getDiameterScalar

      Double getDiameterScalar()
      The diameter of a cylindrical detector

      Type: NX_FLOAT Units: NX_LENGTH

      Returns:
      the value.

    • setDiameterScalar

      DataNode setDiameterScalar(Double diameterValue)
      The diameter of a cylindrical detector

      Type: NX_FLOAT Units: NX_LENGTH

      Parameters:
      diameter - the diameter

    • getAcquisition_mode

      org.eclipse.january.dataset.Dataset getAcquisition_mode()
      The acquisition mode of the detector.

      Type: NX_CHAR

      Enumeration:

      • gated
      • triggered
      • summed
      • event
      • histogrammed
      • decimated
      • pulse counting

      Returns:
      the value.

    • setAcquisition_mode

      DataNode setAcquisition_mode(org.eclipse.january.dataset.IDataset acquisition_modeDataset)
      The acquisition mode of the detector.

      Type: NX_CHAR

      Enumeration:

      • gated
      • triggered
      • summed
      • event
      • histogrammed
      • decimated
      • pulse counting

      Parameters:
      acquisition_modeDataset - the acquisition_modeDataset

    • getAcquisition_modeScalar

      String getAcquisition_modeScalar()
      The acquisition mode of the detector.

      Type: NX_CHAR

      Enumeration:

      • gated
      • triggered
      • summed
      • event
      • histogrammed
      • decimated
      • pulse counting

      Returns:
      the value.

    • setAcquisition_modeScalar

      DataNode setAcquisition_modeScalar(String acquisition_modeValue)
      The acquisition mode of the detector.

      Type: NX_CHAR

      Enumeration:

      • gated
      • triggered
      • summed
      • event
      • histogrammed
      • decimated
      • pulse counting

      Parameters:
      acquisition_mode - the acquisition_mode

    • getAngular_calibration_applied

      org.eclipse.january.dataset.Dataset getAngular_calibration_applied()
      True when the angular calibration has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Returns:
      the value.

    • setAngular_calibration_applied

      DataNode setAngular_calibration_applied(org.eclipse.january.dataset.IDataset angular_calibration_appliedDataset)
      True when the angular calibration has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Parameters:
      angular_calibration_appliedDataset - the angular_calibration_appliedDataset

    • getAngular_calibration_appliedScalar

      Boolean getAngular_calibration_appliedScalar()
      True when the angular calibration has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Returns:
      the value.

    • setAngular_calibration_appliedScalar

      DataNode setAngular_calibration_appliedScalar(Boolean angular_calibration_appliedValue)
      True when the angular calibration has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Parameters:
      angular_calibration_applied - the angular_calibration_applied

    • getAngular_calibration

      org.eclipse.january.dataset.Dataset getAngular_calibration()
      Angular calibration data.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setAngular_calibration

      DataNode setAngular_calibration(org.eclipse.january.dataset.IDataset angular_calibrationDataset)
      Angular calibration data.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Parameters:
      angular_calibrationDataset - the angular_calibrationDataset

    • getAngular_calibrationScalar

      Double getAngular_calibrationScalar()
      Angular calibration data.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setAngular_calibrationScalar

      DataNode setAngular_calibrationScalar(Double angular_calibrationValue)
      Angular calibration data.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Parameters:
      angular_calibration - the angular_calibration

    • getFlatfield_applied

      org.eclipse.january.dataset.Dataset getFlatfield_applied()
      True when the flat field correction has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Returns:
      the value.

    • setFlatfield_applied

      DataNode setFlatfield_applied(org.eclipse.january.dataset.IDataset flatfield_appliedDataset)
      True when the flat field correction has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Parameters:
      flatfield_appliedDataset - the flatfield_appliedDataset

    • getFlatfield_appliedScalar

      Boolean getFlatfield_appliedScalar()
      True when the flat field correction has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Returns:
      the value.

    • setFlatfield_appliedScalar

      DataNode setFlatfield_appliedScalar(Boolean flatfield_appliedValue)
      True when the flat field correction has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Parameters:
      flatfield_applied - the flatfield_applied

    • getFlatfield

      org.eclipse.january.dataset.Dataset getFlatfield()
      Flat field correction data.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setFlatfield

      DataNode setFlatfield(org.eclipse.january.dataset.IDataset flatfieldDataset)
      Flat field correction data.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Parameters:
      flatfieldDataset - the flatfieldDataset

    • getFlatfieldScalar

      Double getFlatfieldScalar()
      Flat field correction data.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setFlatfieldScalar

      DataNode setFlatfieldScalar(Double flatfieldValue)
      Flat field correction data.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Parameters:
      flatfield - the flatfield

    • getFlatfield_errors

      org.eclipse.january.dataset.Dataset getFlatfield_errors()
      Errors of the flat field correction data. The form flatfield_error is deprecated.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setFlatfield_errors

      DataNode setFlatfield_errors(org.eclipse.january.dataset.IDataset flatfield_errorsDataset)
      Errors of the flat field correction data. The form flatfield_error is deprecated.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Parameters:
      flatfield_errorsDataset - the flatfield_errorsDataset

    • getFlatfield_errorsScalar

      Double getFlatfield_errorsScalar()
      Errors of the flat field correction data. The form flatfield_error is deprecated.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setFlatfield_errorsScalar

      DataNode setFlatfield_errorsScalar(Double flatfield_errorsValue)
      Errors of the flat field correction data. The form flatfield_error is deprecated.

      Type: NX_FLOAT Dimensions: 1: i; 2: j;

      Parameters:
      flatfield_errors - the flatfield_errors

    • getPixel_mask_applied

      org.eclipse.january.dataset.Dataset getPixel_mask_applied()
      True when the pixel mask correction has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Returns:
      the value.

    • setPixel_mask_applied

      DataNode setPixel_mask_applied(org.eclipse.january.dataset.IDataset pixel_mask_appliedDataset)
      True when the pixel mask correction has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Parameters:
      pixel_mask_appliedDataset - the pixel_mask_appliedDataset

    • getPixel_mask_appliedScalar

      Boolean getPixel_mask_appliedScalar()
      True when the pixel mask correction has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Returns:
      the value.

    • setPixel_mask_appliedScalar

      DataNode setPixel_mask_appliedScalar(Boolean pixel_mask_appliedValue)
      True when the pixel mask correction has been applied in the electronics, false otherwise.

      Type: NX_BOOLEAN

      Parameters:
      pixel_mask_applied - the pixel_mask_applied

    • getPixel_mask

      org.eclipse.january.dataset.Dataset getPixel_mask()
      The 32-bit pixel mask for the detector. Can be either one mask for the whole dataset (i.e. an array with indices i, j) or each frame can have its own mask (in which case it would be an array with indices np, i, j). Contains a bit field for each pixel to signal dead, blind or high or otherwise unwanted or undesirable pixels. They have the following meaning: .. can't make a table here, a bullet list will have to do for now * bit 0: gap (pixel with no sensor) * bit 1: dead * bit 2: under responding * bit 3: over responding * bit 4: noisy * bit 5: -undefined- * bit 6: pixel is part of a cluster of problematic pixels (bit set in addition to others) * bit 7: -undefined- * bit 8: user defined mask (e.g. around beamstop) * bits 9-30: -undefined- * bit 31: virtual pixel (corner pixel with interpolated value) Normal data analysis software would not take pixels into account when a bit in (mask invalid input: '&' 0x0000FFFF) is set. Tag bit in the upper two bytes would indicate special pixel properties that normally would not be a sole reason to reject the intensity value (unless lower bits are set. If the full bit depths is not required, providing a mask with fewer bits is permissible. If needed, additional pixel masks can be specified by including additional entries named pixel_mask_N, where N is an integer. For example, a general bad pixel mask could be specified in pixel_mask that indicates noisy and dead pixels, and an additional pixel mask from experiment-specific shadowing could be specified in pixel_mask_2. The cumulative mask is the bitwise OR of pixel_mask and any pixel_mask_N entries.

      Type: NX_INT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setPixel_mask

      DataNode setPixel_mask(org.eclipse.january.dataset.IDataset pixel_maskDataset)
      The 32-bit pixel mask for the detector. Can be either one mask for the whole dataset (i.e. an array with indices i, j) or each frame can have its own mask (in which case it would be an array with indices np, i, j). Contains a bit field for each pixel to signal dead, blind or high or otherwise unwanted or undesirable pixels. They have the following meaning: .. can't make a table here, a bullet list will have to do for now * bit 0: gap (pixel with no sensor) * bit 1: dead * bit 2: under responding * bit 3: over responding * bit 4: noisy * bit 5: -undefined- * bit 6: pixel is part of a cluster of problematic pixels (bit set in addition to others) * bit 7: -undefined- * bit 8: user defined mask (e.g. around beamstop) * bits 9-30: -undefined- * bit 31: virtual pixel (corner pixel with interpolated value) Normal data analysis software would not take pixels into account when a bit in (mask invalid input: '&' 0x0000FFFF) is set. Tag bit in the upper two bytes would indicate special pixel properties that normally would not be a sole reason to reject the intensity value (unless lower bits are set. If the full bit depths is not required, providing a mask with fewer bits is permissible. If needed, additional pixel masks can be specified by including additional entries named pixel_mask_N, where N is an integer. For example, a general bad pixel mask could be specified in pixel_mask that indicates noisy and dead pixels, and an additional pixel mask from experiment-specific shadowing could be specified in pixel_mask_2. The cumulative mask is the bitwise OR of pixel_mask and any pixel_mask_N entries.

      Type: NX_INT Dimensions: 1: i; 2: j;

      Parameters:
      pixel_maskDataset - the pixel_maskDataset

    • getPixel_maskScalar

      Long getPixel_maskScalar()
      The 32-bit pixel mask for the detector. Can be either one mask for the whole dataset (i.e. an array with indices i, j) or each frame can have its own mask (in which case it would be an array with indices np, i, j). Contains a bit field for each pixel to signal dead, blind or high or otherwise unwanted or undesirable pixels. They have the following meaning: .. can't make a table here, a bullet list will have to do for now * bit 0: gap (pixel with no sensor) * bit 1: dead * bit 2: under responding * bit 3: over responding * bit 4: noisy * bit 5: -undefined- * bit 6: pixel is part of a cluster of problematic pixels (bit set in addition to others) * bit 7: -undefined- * bit 8: user defined mask (e.g. around beamstop) * bits 9-30: -undefined- * bit 31: virtual pixel (corner pixel with interpolated value) Normal data analysis software would not take pixels into account when a bit in (mask invalid input: '&' 0x0000FFFF) is set. Tag bit in the upper two bytes would indicate special pixel properties that normally would not be a sole reason to reject the intensity value (unless lower bits are set. If the full bit depths is not required, providing a mask with fewer bits is permissible. If needed, additional pixel masks can be specified by including additional entries named pixel_mask_N, where N is an integer. For example, a general bad pixel mask could be specified in pixel_mask that indicates noisy and dead pixels, and an additional pixel mask from experiment-specific shadowing could be specified in pixel_mask_2. The cumulative mask is the bitwise OR of pixel_mask and any pixel_mask_N entries.

      Type: NX_INT Dimensions: 1: i; 2: j;

      Returns:
      the value.

    • setPixel_maskScalar

      DataNode setPixel_maskScalar(Long pixel_maskValue)
      The 32-bit pixel mask for the detector. Can be either one mask for the whole dataset (i.e. an array with indices i, j) or each frame can have its own mask (in which case it would be an array with indices np, i, j). Contains a bit field for each pixel to signal dead, blind or high or otherwise unwanted or undesirable pixels. They have the following meaning: .. can't make a table here, a bullet list will have to do for now * bit 0: gap (pixel with no sensor) * bit 1: dead * bit 2: under responding * bit 3: over responding * bit 4: noisy * bit 5: -undefined- * bit 6: pixel is part of a cluster of problematic pixels (bit set in addition to others) * bit 7: -undefined- * bit 8: user defined mask (e.g. around beamstop) * bits 9-30: -undefined- * bit 31: virtual pixel (corner pixel with interpolated value) Normal data analysis software would not take pixels into account when a bit in (mask invalid input: '&' 0x0000FFFF) is set. Tag bit in the upper two bytes would indicate special pixel properties that normally would not be a sole reason to reject the intensity value (unless lower bits are set. If the full bit depths is not required, providing a mask with fewer bits is permissible. If needed, additional pixel masks can be specified by including additional entries named pixel_mask_N, where N is an integer. For example, a general bad pixel mask could be specified in pixel_mask that indicates noisy and dead pixels, and an additional pixel mask from experiment-specific shadowing could be specified in pixel_mask_2. The cumulative mask is the bitwise OR of pixel_mask and any pixel_mask_N entries.

      Type: NX_INT Dimensions: 1: i; 2: j;

      Parameters:
      pixel_mask - the pixel_mask

    • getImage_key

      org.eclipse.january.dataset.Dataset getImage_key()
      This field allow to distinguish different types of exposure to the same detector "data" field. Some techniques require frequent (re-)calibration inbetween measurements and this way of recording the different measurements preserves the chronological order with is important for correct processing. This is used for example in tomography (:ref:`NXtomo`) sample projections, dark and flat images, a magic number is recorded per frame. The key is as follows: * projection (sample) = 0 * flat field = 1 * dark field = 2 * invalid = 3 * background (no sample, but buffer where applicable) = 4 In cases where the data is of type :ref:`NXlog` this can also be an NXlog.

      Type: NX_INT Dimensions: 1: np;

      Returns:
      the value.

    • setImage_key

      DataNode setImage_key(org.eclipse.january.dataset.IDataset image_keyDataset)
      This field allow to distinguish different types of exposure to the same detector "data" field. Some techniques require frequent (re-)calibration inbetween measurements and this way of recording the different measurements preserves the chronological order with is important for correct processing. This is used for example in tomography (:ref:`NXtomo`) sample projections, dark and flat images, a magic number is recorded per frame. The key is as follows: * projection (sample) = 0 * flat field = 1 * dark field = 2 * invalid = 3 * background (no sample, but buffer where applicable) = 4 In cases where the data is of type :ref:`NXlog` this can also be an NXlog.

      Type: NX_INT Dimensions: 1: np;

      Parameters:
      image_keyDataset - the image_keyDataset

    • getImage_keyScalar

      Long getImage_keyScalar()
      This field allow to distinguish different types of exposure to the same detector "data" field. Some techniques require frequent (re-)calibration inbetween measurements and this way of recording the different measurements preserves the chronological order with is important for correct processing. This is used for example in tomography (:ref:`NXtomo`) sample projections, dark and flat images, a magic number is recorded per frame. The key is as follows: * projection (sample) = 0 * flat field = 1 * dark field = 2 * invalid = 3 * background (no sample, but buffer where applicable) = 4 In cases where the data is of type :ref:`NXlog` this can also be an NXlog.

      Type: NX_INT Dimensions: 1: np;

      Returns:
      the value.

    • setImage_keyScalar

      DataNode setImage_keyScalar(Long image_keyValue)
      This field allow to distinguish different types of exposure to the same detector "data" field. Some techniques require frequent (re-)calibration inbetween measurements and this way of recording the different measurements preserves the chronological order with is important for correct processing. This is used for example in tomography (:ref:`NXtomo`) sample projections, dark and flat images, a magic number is recorded per frame. The key is as follows: * projection (sample) = 0 * flat field = 1 * dark field = 2 * invalid = 3 * background (no sample, but buffer where applicable) = 4 In cases where the data is of type :ref:`NXlog` this can also be an NXlog.

      Type: NX_INT Dimensions: 1: np;

      Parameters:
      image_key - the image_key

    • getCountrate_correction_applied

      org.eclipse.january.dataset.Dataset getCountrate_correction_applied()
      Counting detectors usually are not able to measure all incoming particles, especially at higher count-rates. Count-rate correction is applied to account for these errors. True when count-rate correction has been applied, false otherwise.

      Type: NX_BOOLEAN

      Returns:
      the value.

    • setCountrate_correction_applied

      DataNode setCountrate_correction_applied(org.eclipse.january.dataset.IDataset countrate_correction_appliedDataset)
      Counting detectors usually are not able to measure all incoming particles, especially at higher count-rates. Count-rate correction is applied to account for these errors. True when count-rate correction has been applied, false otherwise.

      Type: NX_BOOLEAN

      Parameters:
      countrate_correction_appliedDataset - the countrate_correction_appliedDataset

    • getCountrate_correction_appliedScalar

      Boolean getCountrate_correction_appliedScalar()
      Counting detectors usually are not able to measure all incoming particles, especially at higher count-rates. Count-rate correction is applied to account for these errors. True when count-rate correction has been applied, false otherwise.

      Type: NX_BOOLEAN

      Returns:
      the value.

    • setCountrate_correction_appliedScalar

      DataNode setCountrate_correction_appliedScalar(Boolean countrate_correction_appliedValue)
      Counting detectors usually are not able to measure all incoming particles, especially at higher count-rates. Count-rate correction is applied to account for these errors. True when count-rate correction has been applied, false otherwise.

      Type: NX_BOOLEAN

      Parameters:
      countrate_correction_applied - the countrate_correction_applied

    • getCountrate_correction_lookup_table

      org.eclipse.january.dataset.Dataset getCountrate_correction_lookup_table()
      The countrate_correction_lookup_table defines the LUT used for count-rate correction. It maps a measured count :math:`c` to its corrected value :math:`countrate\_correction\_lookup\_table[c]`. :math:`m` denotes the length of the table.

      Type: NX_NUMBER Dimensions: 1: m;

      Returns:
      the value.

    • setCountrate_correction_lookup_table

      DataNode setCountrate_correction_lookup_table(org.eclipse.january.dataset.IDataset countrate_correction_lookup_tableDataset)
      The countrate_correction_lookup_table defines the LUT used for count-rate correction. It maps a measured count :math:`c` to its corrected value :math:`countrate\_correction\_lookup\_table[c]`. :math:`m` denotes the length of the table.

      Type: NX_NUMBER Dimensions: 1: m;

      Parameters:
      countrate_correction_lookup_tableDataset - the countrate_correction_lookup_tableDataset

    • getCountrate_correction_lookup_tableScalar

      Number getCountrate_correction_lookup_tableScalar()
      The countrate_correction_lookup_table defines the LUT used for count-rate correction. It maps a measured count :math:`c` to its corrected value :math:`countrate\_correction\_lookup\_table[c]`. :math:`m` denotes the length of the table.

      Type: NX_NUMBER Dimensions: 1: m;

      Returns:
      the value.

    • setCountrate_correction_lookup_tableScalar

      DataNode setCountrate_correction_lookup_tableScalar(Number countrate_correction_lookup_tableValue)
      The countrate_correction_lookup_table defines the LUT used for count-rate correction. It maps a measured count :math:`c` to its corrected value :math:`countrate\_correction\_lookup\_table[c]`. :math:`m` denotes the length of the table.

      Type: NX_NUMBER Dimensions: 1: m;

      Parameters:
      countrate_correction_lookup_table - the countrate_correction_lookup_table

    • getVirtual_pixel_interpolation_applied

      org.eclipse.january.dataset.Dataset getVirtual_pixel_interpolation_applied()
      True when virtual pixel interpolation has been applied, false otherwise. When virtual pixel interpolation is applied, values of some pixels may contain interpolated values. For example, to account for space between readout chips on a module, physical pixels on edges and corners between chips may have larger sensor areas and counts may be distributed between their logical pixels.

      Type: NX_BOOLEAN

      Returns:
      the value.

    • setVirtual_pixel_interpolation_applied

      DataNode setVirtual_pixel_interpolation_applied(org.eclipse.january.dataset.IDataset virtual_pixel_interpolation_appliedDataset)
      True when virtual pixel interpolation has been applied, false otherwise. When virtual pixel interpolation is applied, values of some pixels may contain interpolated values. For example, to account for space between readout chips on a module, physical pixels on edges and corners between chips may have larger sensor areas and counts may be distributed between their logical pixels.

      Type: NX_BOOLEAN

      Parameters:
      virtual_pixel_interpolation_appliedDataset - the virtual_pixel_interpolation_appliedDataset

    • getVirtual_pixel_interpolation_appliedScalar

      Boolean getVirtual_pixel_interpolation_appliedScalar()
      True when virtual pixel interpolation has been applied, false otherwise. When virtual pixel interpolation is applied, values of some pixels may contain interpolated values. For example, to account for space between readout chips on a module, physical pixels on edges and corners between chips may have larger sensor areas and counts may be distributed between their logical pixels.

      Type: NX_BOOLEAN

      Returns:
      the value.

    • setVirtual_pixel_interpolation_appliedScalar

      DataNode setVirtual_pixel_interpolation_appliedScalar(Boolean virtual_pixel_interpolation_appliedValue)
      True when virtual pixel interpolation has been applied, false otherwise. When virtual pixel interpolation is applied, values of some pixels may contain interpolated values. For example, to account for space between readout chips on a module, physical pixels on edges and corners between chips may have larger sensor areas and counts may be distributed between their logical pixels.

      Type: NX_BOOLEAN

      Parameters:
      virtual_pixel_interpolation_applied - the virtual_pixel_interpolation_applied

    • getBit_depth_readout

      org.eclipse.january.dataset.Dataset getBit_depth_readout()
      How many bits the electronics reads per pixel. With CCD's and single photon counting detectors, this must not align with traditional integer sizes. This can be 4, 8, 12, 14, 16, ...

      Type: NX_INT

      Returns:
      the value.

    • setBit_depth_readout

      DataNode setBit_depth_readout(org.eclipse.january.dataset.IDataset bit_depth_readoutDataset)
      How many bits the electronics reads per pixel. With CCD's and single photon counting detectors, this must not align with traditional integer sizes. This can be 4, 8, 12, 14, 16, ...

      Type: NX_INT

      Parameters:
      bit_depth_readoutDataset - the bit_depth_readoutDataset

    • getBit_depth_readoutScalar

      Long getBit_depth_readoutScalar()
      How many bits the electronics reads per pixel. With CCD's and single photon counting detectors, this must not align with traditional integer sizes. This can be 4, 8, 12, 14, 16, ...

      Type: NX_INT

      Returns:
      the value.

    • setBit_depth_readoutScalar

      DataNode setBit_depth_readoutScalar(Long bit_depth_readoutValue)
      How many bits the electronics reads per pixel. With CCD's and single photon counting detectors, this must not align with traditional integer sizes. This can be 4, 8, 12, 14, 16, ...

      Type: NX_INT

      Parameters:
      bit_depth_readout - the bit_depth_readout

    • getDetector_readout_time

      org.eclipse.january.dataset.Dataset getDetector_readout_time()
      Time it takes to read the detector (typically milliseconds). This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Returns:
      the value.

    • setDetector_readout_time

      DataNode setDetector_readout_time(org.eclipse.january.dataset.IDataset detector_readout_timeDataset)
      Time it takes to read the detector (typically milliseconds). This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Parameters:
      detector_readout_timeDataset - the detector_readout_timeDataset

    • getDetector_readout_timeScalar

      Double getDetector_readout_timeScalar()
      Time it takes to read the detector (typically milliseconds). This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Returns:
      the value.

    • setDetector_readout_timeScalar

      DataNode setDetector_readout_timeScalar(Double detector_readout_timeValue)
      Time it takes to read the detector (typically milliseconds). This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Parameters:
      detector_readout_time - the detector_readout_time

    • getTrigger_delay_time

      org.eclipse.january.dataset.Dataset getTrigger_delay_time()
      Time it takes to start exposure after a trigger signal has been received. This is the reaction time of the detector firmware after receiving the trigger signal to when the detector starts to acquire the exposure, including any user set delay.. This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Returns:
      the value.

    • setTrigger_delay_time

      DataNode setTrigger_delay_time(org.eclipse.january.dataset.IDataset trigger_delay_timeDataset)
      Time it takes to start exposure after a trigger signal has been received. This is the reaction time of the detector firmware after receiving the trigger signal to when the detector starts to acquire the exposure, including any user set delay.. This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Parameters:
      trigger_delay_timeDataset - the trigger_delay_timeDataset

    • getTrigger_delay_timeScalar

      Double getTrigger_delay_timeScalar()
      Time it takes to start exposure after a trigger signal has been received. This is the reaction time of the detector firmware after receiving the trigger signal to when the detector starts to acquire the exposure, including any user set delay.. This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Returns:
      the value.

    • setTrigger_delay_timeScalar

      DataNode setTrigger_delay_timeScalar(Double trigger_delay_timeValue)
      Time it takes to start exposure after a trigger signal has been received. This is the reaction time of the detector firmware after receiving the trigger signal to when the detector starts to acquire the exposure, including any user set delay.. This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Parameters:
      trigger_delay_time - the trigger_delay_time

    • getTrigger_delay_time_set

      org.eclipse.january.dataset.Dataset getTrigger_delay_time_set()
      User-specified trigger delay.

      Type: NX_FLOAT Units: NX_TIME

      Returns:
      the value.

    • setTrigger_delay_time_set

      DataNode setTrigger_delay_time_set(org.eclipse.january.dataset.IDataset trigger_delay_time_setDataset)
      User-specified trigger delay.

      Type: NX_FLOAT Units: NX_TIME

      Parameters:
      trigger_delay_time_setDataset - the trigger_delay_time_setDataset

    • getTrigger_delay_time_setScalar

      Double getTrigger_delay_time_setScalar()
      User-specified trigger delay.

      Type: NX_FLOAT Units: NX_TIME

      Returns:
      the value.

    • setTrigger_delay_time_setScalar

      DataNode setTrigger_delay_time_setScalar(Double trigger_delay_time_setValue)
      User-specified trigger delay.

      Type: NX_FLOAT Units: NX_TIME

      Parameters:
      trigger_delay_time_set - the trigger_delay_time_set

    • getTrigger_internal_delay_time

      org.eclipse.january.dataset.Dataset getTrigger_internal_delay_time()
      Time it takes to start exposure after a trigger signal has been received. This is the reaction time of the detector hardware after receiving the trigger signal to when the detector starts to acquire the exposure. It forms the lower boundary of the trigger_delay_time when the user does not request an additional delay.

      Type: NX_FLOAT Units: NX_TIME

      Returns:
      the value.

    • setTrigger_internal_delay_time

      DataNode setTrigger_internal_delay_time(org.eclipse.january.dataset.IDataset trigger_internal_delay_timeDataset)
      Time it takes to start exposure after a trigger signal has been received. This is the reaction time of the detector hardware after receiving the trigger signal to when the detector starts to acquire the exposure. It forms the lower boundary of the trigger_delay_time when the user does not request an additional delay.

      Type: NX_FLOAT Units: NX_TIME

      Parameters:
      trigger_internal_delay_timeDataset - the trigger_internal_delay_timeDataset

    • getTrigger_internal_delay_timeScalar

      Double getTrigger_internal_delay_timeScalar()
      Time it takes to start exposure after a trigger signal has been received. This is the reaction time of the detector hardware after receiving the trigger signal to when the detector starts to acquire the exposure. It forms the lower boundary of the trigger_delay_time when the user does not request an additional delay.

      Type: NX_FLOAT Units: NX_TIME

      Returns:
      the value.

    • setTrigger_internal_delay_timeScalar

      DataNode setTrigger_internal_delay_timeScalar(Double trigger_internal_delay_timeValue)
      Time it takes to start exposure after a trigger signal has been received. This is the reaction time of the detector hardware after receiving the trigger signal to when the detector starts to acquire the exposure. It forms the lower boundary of the trigger_delay_time when the user does not request an additional delay.

      Type: NX_FLOAT Units: NX_TIME

      Parameters:
      trigger_internal_delay_time - the trigger_internal_delay_time

    • getTrigger_dead_time

      org.eclipse.january.dataset.Dataset getTrigger_dead_time()
      Time during which no new trigger signal can be accepted. Typically this is the trigger_delay_time + exposure_time + readout_time. This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Returns:
      the value.

    • setTrigger_dead_time

      DataNode setTrigger_dead_time(org.eclipse.january.dataset.IDataset trigger_dead_timeDataset)
      Time during which no new trigger signal can be accepted. Typically this is the trigger_delay_time + exposure_time + readout_time. This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Parameters:
      trigger_dead_timeDataset - the trigger_dead_timeDataset

    • getTrigger_dead_timeScalar

      Double getTrigger_dead_timeScalar()
      Time during which no new trigger signal can be accepted. Typically this is the trigger_delay_time + exposure_time + readout_time. This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Returns:
      the value.

    • setTrigger_dead_timeScalar

      DataNode setTrigger_dead_timeScalar(Double trigger_dead_timeValue)
      Time during which no new trigger signal can be accepted. Typically this is the trigger_delay_time + exposure_time + readout_time. This is important to know for time resolved experiments.

      Type: NX_FLOAT Units: NX_TIME

      Parameters:
      trigger_dead_time - the trigger_dead_time

    • getFrame_time

      org.eclipse.january.dataset.Dataset getFrame_time()
      This is time for each frame. This is exposure_time + readout time.

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Returns:
      the value.

    • setFrame_time

      DataNode setFrame_time(org.eclipse.january.dataset.IDataset frame_timeDataset)
      This is time for each frame. This is exposure_time + readout time.

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Parameters:
      frame_timeDataset - the frame_timeDataset

    • getFrame_timeScalar

      Double getFrame_timeScalar()
      This is time for each frame. This is exposure_time + readout time.

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Returns:
      the value.

    • setFrame_timeScalar

      DataNode setFrame_timeScalar(Double frame_timeValue)
      This is time for each frame. This is exposure_time + readout time.

      Type: NX_FLOAT Units: NX_TIME Dimensions: 1: nP;

      Parameters:
      frame_time - the frame_time

    • getGain_setting

      org.eclipse.january.dataset.Dataset getGain_setting()
      The gain setting of the detector. This is a detector-specific value meant to document the gain setting of the detector during data collection, for detectors with multiple available gain settings. Examples of gain settings include: * ``standard`` * ``fast`` * ``auto`` * ``high`` * ``medium`` * ``low`` * ``mixed high to medium`` * ``mixed medium to low`` Developers are encouraged to use one of these terms, or to submit additional terms to add to the list.

      Type: NX_CHAR

      Returns:
      the value.

    • setGain_setting

      DataNode setGain_setting(org.eclipse.january.dataset.IDataset gain_settingDataset)
      The gain setting of the detector. This is a detector-specific value meant to document the gain setting of the detector during data collection, for detectors with multiple available gain settings. Examples of gain settings include: * ``standard`` * ``fast`` * ``auto`` * ``high`` * ``medium`` * ``low`` * ``mixed high to medium`` * ``mixed medium to low`` Developers are encouraged to use one of these terms, or to submit additional terms to add to the list.

      Type: NX_CHAR

      Parameters:
      gain_settingDataset - the gain_settingDataset

    • getGain_settingScalar

      String getGain_settingScalar()
      The gain setting of the detector. This is a detector-specific value meant to document the gain setting of the detector during data collection, for detectors with multiple available gain settings. Examples of gain settings include: * ``standard`` * ``fast`` * ``auto`` * ``high`` * ``medium`` * ``low`` * ``mixed high to medium`` * ``mixed medium to low`` Developers are encouraged to use one of these terms, or to submit additional terms to add to the list.

      Type: NX_CHAR

      Returns:
      the value.

    • setGain_settingScalar

      DataNode setGain_settingScalar(String gain_settingValue)
      The gain setting of the detector. This is a detector-specific value meant to document the gain setting of the detector during data collection, for detectors with multiple available gain settings. Examples of gain settings include: * ``standard`` * ``fast`` * ``auto`` * ``high`` * ``medium`` * ``low`` * ``mixed high to medium`` * ``mixed medium to low`` Developers are encouraged to use one of these terms, or to submit additional terms to add to the list.

      Type: NX_CHAR

      Parameters:
      gain_setting - the gain_setting

    • getSaturation_value

      org.eclipse.january.dataset.Dataset getSaturation_value()
      The value at which the detector goes into saturation. Especially common to CCD detectors, the data is known to be invalid above this value. For example, given a saturation_value and an underload_value, the valid pixels are those less than or equal to the saturation_value and greater than or equal to the underload_value. The precise type should match the type of the data.

      Type: NX_NUMBER

      Returns:
      the value.

    • setSaturation_value

      DataNode setSaturation_value(org.eclipse.january.dataset.IDataset saturation_valueDataset)
      The value at which the detector goes into saturation. Especially common to CCD detectors, the data is known to be invalid above this value. For example, given a saturation_value and an underload_value, the valid pixels are those less than or equal to the saturation_value and greater than or equal to the underload_value. The precise type should match the type of the data.

      Type: NX_NUMBER

      Parameters:
      saturation_valueDataset - the saturation_valueDataset

    • getSaturation_valueScalar

      Number getSaturation_valueScalar()
      The value at which the detector goes into saturation. Especially common to CCD detectors, the data is known to be invalid above this value. For example, given a saturation_value and an underload_value, the valid pixels are those less than or equal to the saturation_value and greater than or equal to the underload_value. The precise type should match the type of the data.

      Type: NX_NUMBER

      Returns:
      the value.

    • setSaturation_valueScalar

      DataNode setSaturation_valueScalar(Number saturation_valueValue)
      The value at which the detector goes into saturation. Especially common to CCD detectors, the data is known to be invalid above this value. For example, given a saturation_value and an underload_value, the valid pixels are those less than or equal to the saturation_value and greater than or equal to the underload_value. The precise type should match the type of the data.

      Type: NX_NUMBER

      Parameters:
      saturation_value - the saturation_value

    • getUnderload_value

      org.eclipse.january.dataset.Dataset getUnderload_value()
      The lowest value at which pixels for this detector would be reasonably measured. The data is known to be invalid below this value. For example, given a saturation_value and an underload_value, the valid pixels are those less than or equal to the saturation_value and greater than or equal to the underload_value. The precise type should match the type of the data.

      Type: NX_NUMBER

      Returns:
      the value.

    • setUnderload_value

      DataNode setUnderload_value(org.eclipse.january.dataset.IDataset underload_valueDataset)
      The lowest value at which pixels for this detector would be reasonably measured. The data is known to be invalid below this value. For example, given a saturation_value and an underload_value, the valid pixels are those less than or equal to the saturation_value and greater than or equal to the underload_value. The precise type should match the type of the data.

      Type: NX_NUMBER

      Parameters:
      underload_valueDataset - the underload_valueDataset

    • getUnderload_valueScalar

      Number getUnderload_valueScalar()
      The lowest value at which pixels for this detector would be reasonably measured. The data is known to be invalid below this value. For example, given a saturation_value and an underload_value, the valid pixels are those less than or equal to the saturation_value and greater than or equal to the underload_value. The precise type should match the type of the data.

      Type: NX_NUMBER

      Returns:
      the value.

    • setUnderload_valueScalar

      DataNode setUnderload_valueScalar(Number underload_valueValue)
      The lowest value at which pixels for this detector would be reasonably measured. The data is known to be invalid below this value. For example, given a saturation_value and an underload_value, the valid pixels are those less than or equal to the saturation_value and greater than or equal to the underload_value. The precise type should match the type of the data.

      Type: NX_NUMBER

      Parameters:
      underload_value - the underload_value

    • getNumber_of_cycles

      org.eclipse.january.dataset.Dataset getNumber_of_cycles()
      CCD images are sometimes constructed by summing together multiple short exposures in the electronics. This reduces background etc. This is the number of short exposures used to sum images for an image.

      Type: NX_INT

      Returns:
      the value.

    • setNumber_of_cycles

      DataNode setNumber_of_cycles(org.eclipse.january.dataset.IDataset number_of_cyclesDataset)
      CCD images are sometimes constructed by summing together multiple short exposures in the electronics. This reduces background etc. This is the number of short exposures used to sum images for an image.

      Type: NX_INT

      Parameters:
      number_of_cyclesDataset - the number_of_cyclesDataset

    • getNumber_of_cyclesScalar

      Long getNumber_of_cyclesScalar()
      CCD images are sometimes constructed by summing together multiple short exposures in the electronics. This reduces background etc. This is the number of short exposures used to sum images for an image.

      Type: NX_INT

      Returns:
      the value.

    • setNumber_of_cyclesScalar

      DataNode setNumber_of_cyclesScalar(Long number_of_cyclesValue)
      CCD images are sometimes constructed by summing together multiple short exposures in the electronics. This reduces background etc. This is the number of short exposures used to sum images for an image.

      Type: NX_INT

      Parameters:
      number_of_cycles - the number_of_cycles

    • getSensor_material

      org.eclipse.january.dataset.Dataset getSensor_material()
      At times, radiation is not directly sensed by the detector. Rather, the detector might sense the output from some converter like a scintillator. This is the name of this converter material.

      Type: NX_CHAR

      Returns:
      the value.

    • setSensor_material

      DataNode setSensor_material(org.eclipse.january.dataset.IDataset sensor_materialDataset)
      At times, radiation is not directly sensed by the detector. Rather, the detector might sense the output from some converter like a scintillator. This is the name of this converter material.

      Type: NX_CHAR

      Parameters:
      sensor_materialDataset - the sensor_materialDataset

    • getSensor_materialScalar

      String getSensor_materialScalar()
      At times, radiation is not directly sensed by the detector. Rather, the detector might sense the output from some converter like a scintillator. This is the name of this converter material.

      Type: NX_CHAR

      Returns:
      the value.

    • setSensor_materialScalar

      DataNode setSensor_materialScalar(String sensor_materialValue)
      At times, radiation is not directly sensed by the detector. Rather, the detector might sense the output from some converter like a scintillator. This is the name of this converter material.

      Type: NX_CHAR

      Parameters:
      sensor_material - the sensor_material

    • getSensor_thickness

      org.eclipse.january.dataset.Dataset getSensor_thickness()
      At times, radiation is not directly sensed by the detector. Rather, the detector might sense the output from some converter like a scintillator. This is the thickness of this converter material.

      Type: NX_FLOAT Units: NX_LENGTH

      Returns:
      the value.

    • setSensor_thickness

      DataNode setSensor_thickness(org.eclipse.january.dataset.IDataset sensor_thicknessDataset)
      At times, radiation is not directly sensed by the detector. Rather, the detector might sense the output from some converter like a scintillator. This is the thickness of this converter material.

      Type: NX_FLOAT Units: NX_LENGTH

      Parameters:
      sensor_thicknessDataset - the sensor_thicknessDataset

    • getSensor_thicknessScalar

      Double getSensor_thicknessScalar()
      At times, radiation is not directly sensed by the detector. Rather, the detector might sense the output from some converter like a scintillator. This is the thickness of this converter material.

      Type: NX_FLOAT Units: NX_LENGTH

      Returns:
      the value.

    • setSensor_thicknessScalar

      DataNode setSensor_thicknessScalar(Double sensor_thicknessValue)
      At times, radiation is not directly sensed by the detector. Rather, the detector might sense the output from some converter like a scintillator. This is the thickness of this converter material.

      Type: NX_FLOAT Units: NX_LENGTH

      Parameters:
      sensor_thickness - the sensor_thickness

    • getThreshold_energy

      org.eclipse.january.dataset.Dataset getThreshold_energy()
      Single photon counter detectors can be adjusted for a certain energy range in which they work optimally. This is the energy setting for this.

      Type: NX_FLOAT Units: NX_ENERGY

      Returns:
      the value.

    • setThreshold_energy

      DataNode setThreshold_energy(org.eclipse.january.dataset.IDataset threshold_energyDataset)
      Single photon counter detectors can be adjusted for a certain energy range in which they work optimally. This is the energy setting for this.

      Type: NX_FLOAT Units: NX_ENERGY

      Parameters:
      threshold_energyDataset - the threshold_energyDataset

    • getThreshold_energyScalar

      Double getThreshold_energyScalar()
      Single photon counter detectors can be adjusted for a certain energy range in which they work optimally. This is the energy setting for this.

      Type: NX_FLOAT Units: NX_ENERGY

      Returns:
      the value.

    • setThreshold_energyScalar

      DataNode setThreshold_energyScalar(Double threshold_energyValue)
      Single photon counter detectors can be adjusted for a certain energy range in which they work optimally. This is the energy setting for this.

      Type: NX_FLOAT Units: NX_ENERGY

      Parameters:
      threshold_energy - the threshold_energy

    • getDetector_module

      NXdetector_module getDetector_module()
      For use in special cases where the data in NXdetector is represented in several parts, each with a separate geometry.
      Returns:
      the value.

    • setDetector_module

      void setDetector_module(NXdetector_module detector_moduleGroup)
      For use in special cases where the data in NXdetector is represented in several parts, each with a separate geometry.
      Parameters:
      detector_moduleGroup - the detector_moduleGroup

    • getDetector_module

      NXdetector_module getDetector_module(String name)
      Get a NXdetector_module node by name:
      • For use in special cases where the data in NXdetector is represented in several parts, each with a separate geometry.
      Parameters:
      name - the name of the node.
      Returns:
      a map from node names to the NXdetector_module for that node.

    • setDetector_module

      void setDetector_module(String name, NXdetector_module detector_module)
      Set a NXdetector_module node by name:
      • For use in special cases where the data in NXdetector is represented in several parts, each with a separate geometry.
      Parameters:
      name - the name of the node
      detector_module - the value to set

    • getAllDetector_module

      Map<String,NXdetector_module> getAllDetector_module()
      Get all NXdetector_module nodes:
      • For use in special cases where the data in NXdetector is represented in several parts, each with a separate geometry.
      Returns:
      a map from node names to the NXdetector_module for that node.

    • setAllDetector_module

      void setAllDetector_module(Map<String,NXdetector_module> detector_module)
      Set multiple child nodes of a particular type.
      • For use in special cases where the data in NXdetector is represented in several parts, each with a separate geometry.
      Parameters:
      detector_module - the child nodes to add

    • getDepends_on

      org.eclipse.january.dataset.Dataset getDepends_on()
      The reference point of the detector is the center of the first pixel. In complex geometries the NXoff_geometry groups can be used to provide an unambiguous reference.

      Type: NX_CHAR

      Specified by:
      getDepends_on in interface NXcomponent
      Returns:
      the value.

    • setDepends_on

      DataNode setDepends_on(org.eclipse.january.dataset.IDataset depends_onDataset)
      The reference point of the detector is the center of the first pixel. In complex geometries the NXoff_geometry groups can be used to provide an unambiguous reference.

      Type: NX_CHAR

      Specified by:
      setDepends_on in interface NXcomponent
      Parameters:
      depends_onDataset - the depends_onDataset

    • getDepends_onScalar

      String getDepends_onScalar()
      The reference point of the detector is the center of the first pixel. In complex geometries the NXoff_geometry groups can be used to provide an unambiguous reference.

      Type: NX_CHAR

      Specified by:
      getDepends_onScalar in interface NXcomponent
      Returns:
      the value.

    • setDepends_onScalar

      DataNode setDepends_onScalar(String depends_onValue)
      The reference point of the detector is the center of the first pixel. In complex geometries the NXoff_geometry groups can be used to provide an unambiguous reference.

      Type: NX_CHAR

      Specified by:
      setDepends_onScalar in interface NXcomponent
      Parameters:
      depends_on - the depends_on