ckanext-transmute 1.6.0

Converts a dataset based on a specific schema

ckanext-transmute

The extension helps to validate and converts a dataset based on a specific schema.

Working with transmute

ckanext-transmute provides an action tsm_transmute It helps us to transmute data with the provided convertion scheme. The action doesn't change the original data, but creates a new data dict. There are two mandatory arguments - data and schema. data is a data dict you have and schema helps you to validate/change data in it.

Example: We have a data dict:

{
            "title": "Test-dataset",
            "email": "[e-mail]",
            "metadata_created": "",
            "metadata_modified": "",
            "metadata_reviewed": "",
            "resources": [
                {
                    "title": "test-res",
                    "extension": "xml",
                    "web": "https://stackoverflow.com/",
                    "sub-resources": [
                        {
                            "title": "sub-res",
                            "extension": "csv",
                            "extra": "should-be-removed",
                        }
                    ],
                },
                {
                    "title": "test-res2",
                    "extension": "csv",
                    "web": "https://stackoverflow.com/",
                },
            ],
        }

And we want to achieve this:

{
            "name": "test-dataset",
            "email": "[e-mail]",
            "metadata_created": datetime.datetime(2022, 2, 3, 15, 54, 26, 359453),
            "metadata_modified": datetime.datetime(2022, 2, 3, 15, 54, 26, 359453),
            "metadata_reviewed": datetime.datetime(2022, 2, 3, 15, 54, 26, 359453),
            "attachments": [
                {
                    "name": "test-res",
                    "format": "XML",
                    "url": "https://stackoverflow.com/",
                    "sub-resources": [{"name": "SUB-RES", "format": "CSV"}],
                },
                {
                    "name": "test-res2",
                    "format": "CSV",
                    "url": "https://stackoverflow.com/",
                },
            ],
        }

Then, our schema must be something like that:

{
        "root": "Dataset",
        "types": {
            "Dataset": {
                "fields": {
                    "title": {
                        "validators": [
                            "tsm_string_only",
                            "tsm_to_lowercase",
                            "tsm_name_validator",
                        ],
                        "map": "name",
                    },
                    "resources": {
                        "type": "Resource",
                        "multiple": True,
                        "map": "attachments",
                    },
                    "metadata_created": {
                        "validators": ["tsm_isodate"],
                        "default": "2022-02-03T15:54:26.359453",
                    },
                    "metadata_modified": {
                        "validators": ["tsm_isodate"],
                        "default_from": "metadata_created",
                    },
                    "metadata_reviewed": {
                        "validators": ["tsm_isodate"],
                        "replace_from": "metadata_modified",
                    },
                }
            },
            "Resource": {
                "fields": {
                    "title": {
                        "validators": ["tsm_string_only"],
                        "map": "name",
                    },
                    "extension": {
                        "validators": ["tsm_string_only", "tsm_to_uppercase"],
                        "map": "format",
                    },
                    "web": {
                        "validators": ["tsm_string_only"],
                        "map": "url",
                    },
                    "sub-resources": {
                        "type": "Sub-Resource",
                        "multiple": True,
                    },
                },
            },
            "Sub-Resource": {
                "fields": {
                    "title": {
                        "validators": ["tsm_string_only", "tsm_to_uppercase"],
                        "map": "name",
                    },
                    "extension": {
                        "validators": ["tsm_string_only", "tsm_to_uppercase"],
                        "map": "format",
                    },
                    "extra": {
                        "remove": True,
                    },
                }
            },
        },
    }

There is an example of schema with nested types. The root field is mandatory, it's must contain a main type name, from which the scheme starts. As you can see, Dataset type contains Resource type which contans Sub-Resource.

Transmutators

There are few default transmutators you can use in your schema. Of course, you can define a custom transmutator with the ITransmute interface.

• tsm_name_validator - Wrapper over CKAN default name_validator validator
• tsm_to_lowercase - Casts string value to a lowercase
• tsm_to_uppercase - Casts string value to a uppercase
• tsm_string_only - Validates if field.value is string
• tsm_isodate - Validates datetime string. Mutates an iso-like string to datetime object
• tsm_to_string - Casts a field.value to str
• tsm_get_nested - Allows you to pick up a value from a nested structure. Example:

data = "title_translated": [
    {"nested_field": {"en": "en title", "ar": "العنوان ar"}},
]

schema = ...
    "title": {
        "replace_from": "title_translated",
        "validators": [
            ["tsm_get_nested", 0, "nested_field", "en"],
            "tsm_to_uppercase",
        ],
    },
    ...

This will take a value for a title field from title_translated field. Because title_translated is an array with nested objects, we are using the tsm_get_nested transmutator to achieve the value from it.

• tsm_trim_string - Trim string with max lenght. Example to trim hello world to hello:

data = {"field_name": "hello world}

schema = ...
    "field_name": {
        "validators": [
            ["tsm_trim_string", 5]
        ],
    },
    ...

• tsm_concat - Trim string with max lenght. Use $self to point on field value. Example:

data = {"id": "dataset-1}

schema = ...
    "package_url": {
        "replace_from": "id",
        "validators": [
            [
                "tsm_concat",
                "https://site.url/dataset/",
                "$self",
            ]
        ],
    },
    ...

• tsm_unique_only - Preserve only unique values from a list. Works only with lists.

The default transmutator must receive at least one mandatory argument - field object. Field contains few properties: field_name, value and type.

There is a possibility to provide more arguments to a validator like in tsm_get_nested. For this use a nested array with first item transmutator and other - arguments to it.

Keywords

1. map_to (str) - changes the field.name in result dict.
2. validators (list[str]) - a list of transmutators that will be applied to a field.value. A transmutator could be a string or a list where the first item must be transmutator name and others are arbitrary values. Example:

   ...
   "validators": [
       ["tsm_get_nested", "nested_field", "en"],
       "tsm_to_uppercase",
   ,
   ...
   

   There are two transmutators: tsm_get_nested and tsm_to_uppercase.
3. multiple (bool, default: False) - if the field could have multiple items, e.g resources field in dataset, mark it as multiple to transmute all the items successively.

   ...
   "resources": {
       "type": "Resource",
       "multiple": True
   },
   ...
   

4. remove (bool, default: False) - removes a field from a result dict if True.
5. default (Any) - the default value that will be used if the original field.value evaluates to False.
6. default_from (str | list) - acts similar to default but accepts a field.name of a sibling field from which we want to take its value. Sibling field is a field that located in the same type. The current implementation doesn't allow to point on fields from other types. Could take a string that represents the field.name or an array of strings, to use multiple fields. See inherit_mode keyword for details.

   ...
   "metadata_modified": {
       "validators": ["tsm_isodate"],
       "default_from": "metadata_created",
   },
   ...
   

7. replace_from (str| list) - acts similar to default_from but replaces the origin value whenever it's empty or not.
8. inherit_mode (str, default: combine) - defines the mode for default_from and replace_from. By default we are combining values from all the fields, but we could just use first non-false value, in case if the field might be empty.
9. value (Any) - a value that will be used for a field. This keyword has the highest priority. Could be used to create a new field with an arbitrary value.
10. update (bool, default: False) - if the original value is mutable (array, object`) - you can update it. You can only update field values of the same types.

Installation

To install ckanext-transmute:

1. Activate your CKAN virtual environment, for example:

   . /usr/lib/ckan/default/bin/activate

2. Clone the source and install it on the virtualenv

   git clone https://github.com/mutantsan/ckanext-transmute.git cd ckanext-transmute pip install -e . pip install -r requirements.txt

3. Add transmute to the ckan.plugins setting in your CKAN config file (by default the config file is located at /etc/ckan/default/ckan.ini).

4. Restart CKAN. For example if you've deployed CKAN with Apache on Ubuntu:

   sudo service apache2 reload

Developer installation

To install ckanext-transmute for development, activate your CKAN virtualenv and do:

git clone https://github.com/mutantsan/ckanext-transmute.git
cd ckanext-transmute
python setup.py develop
pip install -r dev-requirements.txt

Tests

I've used TDD to write this extension, so if you changing something be sure that all the tests are valid. To run the tests, do:

pytest --ckan-ini=test.ini

License

AGPL