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., \(d\mathrm{D}\{p_0, p_1, p_2, ...\}\).
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.

Fourier Transform Infrared Spectroscopy (FTIR) 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.