Scientific datasets from global-scale earth science models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. Water data are frequently stored as multidimensional arrays, also called gridded or raster data, and span two or three spatial dimensions, the time dimension, and other dimensions which vary by the specific dataset. Water engineers and scientists need these data as inputs for models and generate data in these formats as results. A myriad of file formats and organizational conventions exist for storing these array datasets. The variety does not make the data unusable but does add considerable difficulty in using them because the structure can vary. These storage formats are largely incompatible with common geographic information system (GIS) software. This introduces additional complexity in extracting values, analyzing results, and otherwise working with multidimensional data since they are often spatial data. We present a Python package which provides a central interface for efficient access to multidimensional water data regardless of the file format. This research builds on and unifies existing file formats and software rather than suggesting entirely new alternatives. We present a summary of the code design and validate the results using common water-related datasets and software.

中文翻译：

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用于查询时空多维水数据的网格 Python 工具

来自全球尺度地球科学模型和遥感仪器的科学数据集以更高的空间和时间分辨率和更短的滞后时间变得可用。水数据通常存储为多维数组，也称为网格或栅格数据，跨越两个或三个空间维度、时间维度和其他维度，这些维度因特定数据集而异。水工程师和科学家需要这些数据作为模型的输入，并以这些格式生成数据作为结果。存在用于存储这些阵列数据集的无数文件格式和组织约定。多样性不会使数据不可用，但确实增加了使用它们的相当大的难度，因为结构可能会有所不同。这些存储格式在很大程度上与常见的地理信息系统 (GIS) 软件不兼容。这在提取值、分析结果和处理多维数据方面带来了额外的复杂性，因为它们通常是空间数据。我们提供了一个 Python 包，它提供了一个中央接口，可以有效访问多维水数据，而不管文件格式如何。这项研究建立在并统一现有文件格式和软件的基础上，而不是提出全新的替代方案。我们总结了代码设计并使用常见的与水相关的数据集和软件来验证结果。这项研究建立在并统一现有文件格式和软件的基础上，而不是提出全新的替代方案。我们总结了代码设计并使用常见的与水相关的数据集和软件来验证结果。这项研究建立在并统一现有文件格式和软件的基础上，而不是提出全新的替代方案。我们总结了代码设计并使用常见的与水相关的数据集和软件来验证结果。