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microdata-tools

Tools for the microdata.no platform

Installation

microdata-tools can be installed from PyPI using pip: 

pip install microdata-tools

Usage

Once you have your metadata and data files ready to go, they should be named and stored like this: 

my-input-directory/
    MY_DATASET_NAME/
        MY_DATASET_NAME.csv
        MY_DATASET_NAME.json
The CSV file is optional in some cases.

Package dataset

The package_dataset() function will encrypt and package your dataset as a tar archive. The process is as follows:

  1. Generate the symmetric key for a dataset.
  2. Encrypt the dataset data (CSV) using the symmetric key and store the encrypted file as <DATASET_NAME>.csv.encr
  3. Encrypt the symmetric key using the asymmetric RSA public key microdata_public_key.pem and store the encrypted file as <DATASET_NAME>.symkey.encr
  4. Gather the encrypted CSV, encrypted symmetric key and metadata (JSON) file in one tar file.

Unpackage dataset

The unpackage_dataset() function will untar and decrypt your dataset using the microdata_private_key.pem RSA private key.

The packaged file has to have the <DATASET_NAME>.tar extension. Its contents should be as follows:

<DATASET_NAME>.json : Required medata file.

<DATASET_NAME>.csv.encr : Optional encrypted dataset file.

<DATASET_NAME>.symkey.encr : Optional encrypted file containing the symmetrical key used to decrypt the dataset file. Required if the .csv.encr file is present.

Decryption uses the RSA private key located at RSA_KEY_DIR.

The packaged file is then stored in output_dir/archive/unpackaged after a successful run or output_dir/archive/failed after an unsuccessful run.

Example

Python script that uses a RSA public key named microdata_public_key.pem and packages a dataset:

from pathlib import Path
from microdata_tools import package_dataset

RSA_KEYS_DIRECTORY = Path("tests/resources/rsa_keys")
DATASET_DIRECTORY = Path("tests/resources/input_package/DATASET_1")
OUTPUT_DIRECTORY = Path("tests/resources/output")

package_dataset(
   rsa_keys_dir=RSA_KEYS_DIRECTORY,
   dataset_dir=DATASET_DIRECTORY,
   output_dir=OUTPUT_DIRECTORY,
)

Validation

Once you have your metadata and data files ready to go, they should be named and stored like this: 

my-input-directory/
    MY_DATASET_NAME/
        MY_DATASET_NAME.csv
        MY_DATASET_NAME.json
Note that the filename only allows upper case letters A-Z, number 0-9 and underscores.

Import microdata-tools in your script and validate your files: 

from microdata_tools import validate_dataset

validation_errors = validate_dataset(
    "MY_DATASET_NAME",
    input_directory="path/to/my-input-directory"
)

if not validation_errors:
    print("My dataset is valid")
else:
    print("Dataset is invalid :(")
    # You can print your errors like this:
    for error in validation_errors:
        print(error)

For a more in-depth explanation of usage visit the usage documentation.

Data format description

A dataset as defined in microdata consists of one data file, and one metadata file.

The data file is a csv file seperated by semicolons. A valid example would be: 

000000000000001;123;2020-01-01;2020-12-31;
000000000000002;123;2020-01-01;2020-12-31;
000000000000003;123;2020-01-01;2020-12-31;
000000000000004;123;2020-01-01;2020-12-31;

The metadata files should be in json format. The requirements for the metadata is best described through the Pydantic model and the examples