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The csdmpy package is a Python support for the core scientific dataset (CSD) model file exchange-format. The package is based on the core scientific dataset (CSD) model which is designed as a building block in the development of a more sophisticated portable scientific dataset file standard. The CSD model is capable of handling a wide variety of scientific datasets both within and across disciplinary fields.

The main objective of this python package is to facilitate an easy import and export of the CSD model serialized JSON files for Python users. The package utilizes Numpy library and, therefore, offers the end users versatility to process or visualize the imported datasets with any third party package(s) compatible with Numpy.

For further reading, refer to the documentation.

See example gallery

The core scientific dataset (CSD) model is a light-weight, portable, versatile, and standalone data model capable of handling a variety of scientific datasets. The model only encapsulates data values and the minimum metadata, to accurately represent a p-component dependent variable, discretely sampled at M unique points in a d-dimensional coordinate space. The model is not intended to encapsulate any information on how the data might be acquired, processed, or visualized.

The data model is versatile in allowing many use cases for most spectroscopy, diffraction, and imaging techniques.

The model supports multi-component datasets associated with continuous physical quantities that are discretely sampled in a multi-dimensional space associated with other carefully controlled quantities, for e.g., a mass as a function of temperature, a current as a function of voltage and time, a signal voltage as a function of magnetic field gradient strength, a color image with a red, green, and blue (RGB) light intensity components as a function of two independent spatial dimensions, or the six components of the symmetric second-rank diffusion tensor MRI as a function of three independent spatial dimensions. Additionally, the model supports multiple dependent variables sharing the same d-dimensional coordinate space. For instance, the simultaneous measurement of current and voltage as a function of time. Another example would be the simultaneous acquisition of air temperature, pressure, wind velocity, and solar-flux as a function of Earth’s latitude and longitude coordinates. We refer to these dependent variables as correlated-datasets.

Example

"csdm": {
  "version": "1.0",
  # A list of Linear, Monotonic, or Labeled dimensions of the multi-dimensional space.
  "dimensions": [{
    "type": "linear",
    "count": 1608,
    "increment": "0.08333333333 yr",
    "coordinates_offset": "1880.0416666667 yr",
  }],
  # A list of dependent variables sampling the multi-dimensional space.
  "dependent_variables": [{
    "type": "internal",
    "unit": "mm",
    "numeric_type": "float32",
    "quantity_type": "scalar",
    "component_labels": ["GMSL"],
    "components": [
      ["-183.0, -171.125, ..., 59.6875, 58.5"]
    ]
  }]
}

$ pip install csdmpy

Please cite the following when used in publication.

1. Srivastava D.J., Vosegaard T., Massiot D., Grandinetti P.J. (2020) Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data. PLOS ONE 15(1): e0225953.

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