Example Gallery¶
In this section, we present illustrative examples for importing files serialized with the CSD model, using the csdmpy package. Because the CSD model allows multi-dimensional datasets with multiple dependent variables, we use a shorthand notation of \(d\mathrm{D}\{p\}\) to indicate that a dataset has a \(p\)-component dependent variable defined on a \(d\)-dimensional coordinate grid. In the case of correlated datasets, the number of components in each dependent variable is given as a list within the curly braces, i.e., \(d\mathrm{D}\{p_0, p_1, p_2, ...\}\).
The sample CSDM compliant files used in this documentation are available online.
Scalar, 1D{1} datasets¶
The 1D{1} datasets are one dimensional, \(d=1\), with one single-component, \(p=1\), dependent variable. These datasets are the most common, and we, therefore, provide a few examples from various fields of science.
Figure 1 Global Mean Sea Level rise dataset¶
Figure 4 Gas Chromatography dataset¶
Figure 7 Mass spectrometry (sparse) dataset¶
Scalar, 2D{1} datasets¶
The 2D{1} datasets are two dimensional, \(d=2\), with one single-component dependent variable, \(p=1\). Following are some 2D{1} example datasets from various scientific fields expressed in CSDM format.
Figure 8 Astronomy dataset¶
Figure 11 Labeled Dataset¶
Vector datasets¶
Figure 12 Vector, 1D{2} dataset¶
Figure 13 Vector, 2D{2} dataset¶
Tensor datasets¶
Figure 14 Diffusion tensor MRI, 3D{6} dataset¶
Pixel datasets¶
Figure 15 Image, 2D{3} datasets¶
Correlated datasets¶
The Core Scientific Dataset Model (CSDM) supports multiple dependent variables that share the same d-dimensional coordinate grid, where \(d>=0\). We call the dependent variables from these datasets as correlated datasets. Following are a few examples of the correlated dataset.
Figure 16 Scatter, 0D{1,1} dataset¶
Figure 17 Meteorological, 2D{1,1,2,1,1} dataset¶
