csdmpy:doc v0.4.1
  • Table Of Contents
      • Example Gallery
        • Scalar, 1D{1} datasets
        • Scalar, 2D{1} datasets
        • Vector datasets
        • Tensor datasets
        • Pixel datasets
        • Correlated datasets
        • Sparse datasets
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Table of Contents

  • Introduction to CSDM format
  • Installation
  • Getting started with csdmpy package
  • Example Gallery
    • Scalar, 1D{1} datasets
    • Scalar, 2D{1} datasets
    • Vector datasets
    • Tensor datasets
    • Pixel datasets
    • Correlated datasets
    • Sparse datasets
  • Serializing CSDM object to file
  • Using csdmpy objects
  • Interacting with csdmpy objects
  • Plotting CSDM object with matplotlib
  • Tutorial examples on generating CSDM datasets
  • An emoji 😁 example
  • API-Reference
  • Changelog

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.

https://img.shields.io/badge/Download-CSDM%20sample%20files-blueviolet

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.

Global Mean Sea Level rise dataset

Figure 1 Global Mean Sea Level rise dataset¶

Nuclear Magnetic Resonance (NMR) dataset

Figure 2 Nuclear Magnetic Resonance (NMR) dataset¶

Electron Paramagnetic Resonance (EPR) dataset

Figure 3 Electron Paramagnetic Resonance (EPR) dataset¶

Gas Chromatography dataset

Figure 4 Gas Chromatography dataset¶

Fourier Transform Infrared Spectroscopy (FTIR) dataset

Figure 5 Fourier Transform Infrared Spectroscopy (FTIR) dataset¶

Ultraviolet–visible (UV-vis) dataset

Figure 6 Ultraviolet–visible (UV-vis) dataset¶

Mass spectrometry (sparse) 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.

Astronomy dataset

Figure 8 Astronomy dataset¶

Nuclear Magnetic Resonance (NMR) dataset

Figure 9 Nuclear Magnetic Resonance (NMR) dataset¶

Transmission Electron Microscopy (TEM) dataset

Figure 10 Transmission Electron Microscopy (TEM) dataset¶

Labeled Dataset

Figure 11 Labeled Dataset¶

Vector datasets¶

Vector, 1D{2} dataset

Figure 12 Vector, 1D{2} dataset¶

Vector, 2D{2} dataset

Figure 13 Vector, 2D{2} dataset¶

Tensor datasets¶

Diffusion tensor MRI, 3D{6} dataset

Figure 14 Diffusion tensor MRI, 3D{6} dataset¶

Pixel datasets¶

Image, 2D{3} 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.

Scatter, 0D{1,1} dataset

Figure 16 Scatter, 0D{1,1} dataset¶

Meteorological, 2D{1,1,2,1,1} dataset

Figure 17 Meteorological, 2D{1,1,2,1,1} dataset¶

Astronomy, 2D{1,1,1} dataset (Creating image composition)

Figure 18 Astronomy, 2D{1,1,1} dataset (Creating image composition)¶

Sparse datasets¶

Sparse along one dimension, 2D{1,1} dataset

Figure 19 Sparse along one dimension, 2D{1,1} dataset¶

Sparse along two dimensions, 2D{1,1} dataset

Figure 20 Sparse along two dimensions, 2D{1,1} dataset¶

Download all examples in Python source code: auto_examples_python.zip

Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

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