csdmpy 0.2.1 documentation
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      • Example Gallery
        • Scalar, 1D{1} datasets
        • Scalar, 2D{1} datasets
        • Vector datasets
        • Pixel datasets
        • Correlated datasets
        • Sparse datasets
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Table of Contents

  • Example Gallery
    • Scalar, 1D{1} datasets
    • Scalar, 2D{1} datasets
    • Vector datasets
    • Pixel datasets
    • Correlated datasets
    • Sparse datasets

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Getting started with csdmpy package

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Global Mean Sea Level rise dataset

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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.

../_images/sphx_glr_plot_0_gmsl_thumb.png

Global Mean Sea Level rise dataset¶

../_images/sphx_glr_plot_1_NMR_bloch_thumb.png

Nuclear Magnetic Resonance (NMR) dataset¶

../_images/sphx_glr_plot_2_EPR_thumb.png

Electron Paramagnetic Resonance (EPR) dataset¶

../_images/sphx_glr_plot_3_GS_thumb.png

Gas Chromatography dataset¶

../_images/sphx_glr_plot_4_FTIR_thumb.png

Fourier Transform Infrared Spectroscopy (FTIR) dataset¶

../_images/sphx_glr_plot_5_UV-vis_thumb.png

Ultraviolet–visible (UV-vis) dataset¶

../_images/sphx_glr_plot_6_Mass_thumb.png

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.

../_images/sphx_glr_plot_0_astronomy_thumb.png

Astronomy dataset¶

../_images/sphx_glr_plot_1_NMR_satrec_thumb.png

Nuclear Magnetic Resonance (NMR) dataset¶

../_images/sphx_glr_plot_2_TEM_thumb.png

Transmission Electron Microscopy (TEM) dataset¶

../_images/sphx_glr_plot_3_labeled_thumb.png

Labeled Dataset¶

Vector datasets¶

../_images/sphx_glr_plot_0_vector_thumb.png

Vector, 1D{2} dataset¶

../_images/sphx_glr_plot_1_vector_thumb.png

Vector, 2D{2} dataset¶

Pixel datasets¶

../_images/sphx_glr_plot_0_image_thumb.png

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.

../_images/sphx_glr_plot_0_0D11_dataset_thumb.png

Scatter, 0D{1,1} dataset¶

../_images/sphx_glr_plot_1_meteorology_thumb.png

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

../_images/sphx_glr_plot_2_astronomy_thumb.png

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

Sparse datasets¶

../_images/sphx_glr_plot_0_1D_sparse_thumb.png

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

../_images/sphx_glr_plot_1_2D_sparse_thumb.png

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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