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¶