Read and Write Data

When loading data with readMatrix()/readFrame() in a DaphneDSL script, the system expects a file with the same file name in the same directory as the data file with an additional extension .meta. This file contains a description of meta data stored in JSON format.

There are two slightly varying ways of specifying meta data depending on whether there is a schema for the columns (e.g., a data frame - the corresponding C++ type is the Frame class) or not (this data can currently be loaded as DenseMatrix<VT> or CSRMatrix<VT> where VT is the value type template parameter).

If data is written from a DaphneDSL script via write()/writeMatrix()/writeFrame(), the meta data file will be written to the corresponding filename.meta.

Currently Supported JSON Fields

Name	Expected Data	Allowed values
numRows	Integer	number of rows
numCols	Integer	number of columns
valueType	String	si8, si32, si64, // signed integers (intX_t) ui8, ui32, ui64, // unsigned integers (uintX_t) f32, f64, // floating point (float, double) str // string Contained within schema this may be an empty string. In this case all columns of a data frame will have the same valueType defined outside of the schema data field
numNonZeros	Integer	number of non-zeros (optional)
schema	JSON	nested elements of "label" and "valueType" fields
label	String	column name/header (optional, may be empty string "")

Matrix Example

The example below describes a 2x4 matrix of double-precision values.

Matrix CSV

-0.1,-0.2,0.1,0.2
3.14,5.41,6.22216,5

Matrix Metadata

{
    "numRows": 2,
    "numCols": 4,
    "valueType": "f64",
    "numNonZeros": 0
}

Data Frame Example

The example below describes a 2x2 frame with signed integers in the first column named foo and double-precision values in the second column named bar.

Data Frame CSV

1,0.5
2,1.0

Data Frame Metadata

{
  "numRows": 2,
  "numCols": 2,
  "schema": [
    {
      "label": "foo",
      "valueType": "si64"
    },
    {
      "label": "bar",
      "valueType": "f64"
    }
  ]
}

Data Frame Meta Data with Default ValueType

{
    "numRows": 5,
    "numCols": 3,
    "valueType": "f32",
    "schema": [
        {
            "label": "a",
            "valueType": ""
        },
        {
            "label": "bc",
            "valueType": ""
        },
        {
            "label": "def",
            "valueType": ""
        }
    ]    
}

Data Frame Meta Data with Empty Labels

 {
     "numRows": 5,
     "numCols": 3,
     "schema": [
         {
             "label": "",
             "valueType": "si64"
         },
         {
             "label": "",
             "valueType": "f64"
         },
         {
             "label": "",
             "valueType": "ui32"
         }
     ]
 }