An emoji 😁 example

Let’s make use of what we learned so far and create a simple 1D{1} dataset. To make it interesting, let’s create an emoji dataset.

Start by importing the csdmpy package.

>>> import csdmpy as cp

Create a new dataset with the new() method.

>>> fundata = cp.new(description='An emoji dataset')

Here, fundata is an instance of the CSDM class with a 0D{0} dataset. The data structure of this instance is

>>> print(fundata.data_structure)
{
  "csdm": {
    "version": "1.0",
    "description": "An emoji dataset",
    "dimensions": [],
    "dependent_variables": []
  }
}

Add a labeled dimension to the fundata instance. Here, we’ll make use of python dictionary.

>>> x = dict(type='labeled', labels=['🍈','🍉','🍋','🍌','🥑','🍍'])

The above python dictionary contains two keys. The type key identifies the dimension as a labeled dimension while the labels key holds an array of labels. In this example, the labels are emojis. Add this dictionary as an argument of the add_dimension() method of the fundata instance.

>>> fundata.add_dimension(x)
>>> print(fundata.data_structure)
{
  "csdm": {
    "version": "1.0",
    "description": "An emoji dataset",
    "dimensions": [
      {
        "type": "labeled",
        "labels": [
          "🍈",
          "🍉",
          "🍋",
          "🍌",
          "🥑",
          "🍍"
        ]
      }
    ],
    "dependent_variables": []
  }
}

We have successfully added a labeled dimension to the fundata instance.

Next, add a dependent variable. Set up a python dictionary corresponding to the dependent variable object and add this dictionary as an argument of the add_dependent_variable() method of the fundata instance.

>>> y =dict(type='internal', numeric_type='float32', quantity_type='scalar',
...     components=[[0.5, 0.25, 1, 2, 1, 0.25]])
>>> fundata.add_dependent_variable(y)

Here, the python dictionary contains type, numeric_type, and components key. The value of the components key holds an array of data values corresponding to the labels from the labeled dimension.

Now, we have a 😂 dataset…

>>> print(fundata.data_structure)
{
  "csdm": {
    "version": "1.0",
    "description": "An emoji dataset",
    "dimensions": [
      {
        "type": "labeled",
        "labels": [
          "🍈",
          "🍉",
          "🍋",
          "🍌",
          "🥑",
          "🍍"
        ]
      }
    ],
    "dependent_variables": [
      {
        "type": "internal",
        "numeric_type": "float32",
        "quantity_type": "scalar",
        "components": [
          [
            "0.5, 0.25, ..., 1.0, 0.25"
          ]
        ]
      }
    ]
  }
}

To serialize this file, use the save() method of the fundata instance as

>>> fundata.dependent_variables[0].encoding = 'base64'
>>> fundata.save('my_file.csdf')

In the above code, the components from the dependent_variables attribute at index zero, are encoded as base64 strings before serializing to the my_file.csdf file.

You may also save the components as a binary file, in which case, the file is serialized with a .csdfe file extension.

>>> fundata.dependent_variables[0].encoding = 'raw'
>>> fundata.save('my_file_raw.csdfe')