NXdata: Stricter Suggestion On When AXISNAME_indices Can Be Omitted

Introduction

In the context of the Nexus format, the AXISNAME_indices attribute plays a crucial role in defining the indices of a particular axis. However, there has been a long-standing discussion regarding when this attribute can be omitted. In this article, we will delve into the nuances of AXISNAME_indices and provide a clearer understanding of when it can be safely omitted.

Understanding AXISNAME_indices

AXISNAME_indices is an attribute that is used to specify the indices of a particular axis in a dataset. For instance, if we have a dataset with three axes, x, y, and z, the x_indices attribute would specify the indices of the x axis. The x_indices attribute is typically used in conjunction with the axes attribute, which defines the axes of the dataset.

The Ambiguity

The ambiguity surrounding AXISNAME_indices arises when the indices of an axis are the same as the index of that axis in the axes attribute. For example, if we have a dataset with the following axes attribute: @axes=["x", "x", "z"], it is unclear whether the x_indices attribute should be provided or not.

PeterC-DLS's Observation

PeterC-DLS noted that if a writer is creating a dataset with the following axes attribute: @axes=["x", "x", "z"], then the x_indices attribute could be either @x_indices=[0,1] or @x_indices=[1,0]. This observation highlights the ambiguity surrounding AXISNAME_indices and the need for a clearer understanding of when it can be omitted.

Stricter Suggestion

To remove any ambiguity, a stricter suggestion can be made regarding when AXISNAME_indices can be omitted. Specifically, for one-dimensional “AXISNAME” fields, the “AXISNAME_indices” can be omitted when the index “AXISNAME” in the “axes" is the same as “AXISNAME_indices”.

Implications

The stricter suggestion has several implications for writers and readers of Nexus format datasets. For writers, it provides a clearer understanding of when AXISNAME_indices can be omitted, reducing the risk of ambiguity and errors. For readers, it ensures that the AXISNAME_indices attribute is provided when necessary, allowing for accurate interpretation of the dataset.

Example Use Cases

To illustrate the stricter suggestion, let's consider a few example use cases:

• One-dimensional axis: Suppose we have a dataset with a single axis x and the following axes attribute: @axes=["x"]. In this case, the x_indices attribute can be omitted because the index x in the axes attribute is the same as the x_indices attribute.
• Multi-dimensional axis: Suppose we have a dataset with three axes x, y, and z and the following axes attribute: @axes=["x", "y", "z"]. In this case, the x_indices attribute cannot be omitted because the index x in the axes attribute is not the same as the x_indices attribute.

Conclusion

Introduction

In our previous article, we discussed the stricter suggestion regarding when AXISNAME_indices can be omitted in the Nexus format. To further clarify this topic, we have compiled a list of frequently asked questions (FAQs) and answers.

Q&A

Q: What is the purpose of AXISNAME_indices in the Nexus format?

A: The AXISNAME_indices attribute is used to specify the indices of a particular axis in a dataset. It is typically used in conjunction with the axes attribute, which defines the axes of the dataset.

Q: When can AXISNAME_indices be omitted?

A: For one-dimensional “AXISNAME” fields, the “AXISNAME_indices” can be omitted when the index “AXISNAME” in the “axes" is the same as “AXISNAME_indices”.

Q: What are the implications of omitting AXISNAME_indices?

A: Omitting AXISNAME_indices when it is not necessary can lead to ambiguity and errors. It is essential to follow the stricter suggestion to ensure accurate interpretation of datasets.

Q: How does the stricter suggestion affect multi-dimensional axes?

A: For multi-dimensional axes, the AXISNAME_indices attribute cannot be omitted. The index of the axis in the axes attribute must be different from the AXISNAME_indices attribute.

Q: What are the benefits of following the stricter suggestion?

A: Following the stricter suggestion reduces the risk of ambiguity and errors, ensuring accurate interpretation of datasets. It also provides a clearer understanding of when AXISNAME_indices can be omitted.

Q: Can AXISNAME_indices be omitted for axes with the same name but different indices?

A: No, AXISNAME_indices cannot be omitted for axes with the same name but different indices. The index of the axis in the axes attribute must be different from the AXISNAME_indices attribute.

Q: How does the stricter suggestion affect the interpretation of datasets?

A: The stricter suggestion ensures that the AXISNAME_indices attribute is provided when necessary, allowing for accurate interpretation of datasets. It also reduces the risk of ambiguity and errors.

Q: Can the stricter suggestion be applied to all Nexus format datasets?

A: Yes, the stricter suggestion can be applied to all Nexus format datasets. However, it is essential to follow the specific rules and guidelines for each dataset to ensure accurate interpretation.

Conclusion

In conclusion, the stricter suggestion regarding when AXISNAME_indices can be omitted provides a clearer understanding of this attribute in the Nexus format. By following this suggestion, writers and readers can reduce the risk of ambiguity and errors, ensuring accurate interpretation of datasets.