<filename>goodtables/validate.py
# -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals

from .inspector import Inspector


# Module API

def validate(source, **options):
    """Validates a source file and returns a report.

    # Arguments

        source (Union[str, Dict, List[Dict], IO]):
            The source to be validated.
            It can be a local file path, URL, dict, list of dicts, or a
            file-like object. If it's a list of dicts and the `preset` is
            "nested", each of the dict key's will be used as if it was passed
            as a keyword argument to this method.

            The file can be a CSV, XLS, JSON, and any other format supported by
            `tabulator`_.

        checks (List[str]):
            List of checks names to be enabled. They can be
            individual check names (e.g. `blank-headers`), or check types (e.g.
            `structure`).

        skip_checks (List[str]):
            List of checks names to be skipped. They can
            be individual check names (e.g. `blank-headers`), or check types
            (e.g.  `structure`).

        infer_schema (bool):
            Infer schema if one wasn't passed as an argument.

        infer_fields (bool):
            Infer schema for columns not present in the received schema.

        order_fields (bool):
            Order source columns based on schema fields order.
            This is useful when you don't want to validate that the data
            columns' order is the same as the schema's.

        error_limit (int):
            Stop validation if the number of errors per table exceeds this value.

        table_limit (int):
            Maximum number of tables to validate.

        row_limit (int):
            Maximum number of rows to validate.

        preset (str):
            Dataset type could be `table` (default), `datapackage`,
            `nested` or custom. Usually, the preset can be inferred from the
            source, so you don't need to define it.

        Any (Any):
            Any additional arguments not defined here will be passed on,
            depending on the chosen `preset`. If the `preset` is `table`, the
            extra arguments will be passed on to `tabulator`_, if it is
            `datapackage`, they will be passed on to the `datapackage`_
            constructor.

    # Raises
        GoodtablesException: Raised on any non-tabular error.

    # Returns
        dict: The validation report.

    """
    source, options, inspector_settings = _parse_arguments(source, **options)

    # Validate
    inspector = Inspector(**inspector_settings)
    report = inspector.inspect(source, **options)

    return report


def _parse_arguments(source, **options):
    # Extract settings
    validation_options = set((
        'checks',
        'skip_checks',
        'infer_schema',
        'infer_fields',
        'order_fields',
        'error_limit',
        'table_limit',
        'row_limit',
        'custom_presets',
        'custom_checks',
    ))
    settings = dict((
        (key, options.pop(key)) for key in validation_options
        if key in options
    ))

    # Support for pathlib.Path
    if hasattr(source, 'joinpath'):
        source = str(source)
    if isinstance(source, list):
        if source and isinstance(source[0], dict) and 'source' in source[0]:
            for index, item in enumerate(source):
                if hasattr(item['source'], 'joinpath'):
                    source[index]['source'] = str(item['source'])

    return source, options, settings

<filename>goodtables/validate.py
# -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals

from .inspector import Inspector


# Module API

def validate(source, **options):
    """Validates a source file and returns a report.

    # Arguments

        source (Union[str, Dict, List[Dict], IO]):
            The source to be validated.
            It can be a local file path, URL, dict, list of dicts, or a
            file-like object. If it's a list of dicts and the `preset` is
            "nested", each of the dict key's will be used as if it was passed
            as a keyword argument to this method.

            The file can be a CSV, XLS, JSON, and any other format supported by
            `tabulator`_.

        checks (List[str]):
            List of checks names to be enabled. They can be
            individual check names (e.g. `blank-headers`), or check types (e.g.
            `structure`).

        skip_checks (List[str]):
            List of checks names to be skipped. They can
            be individual check names (e.g. `blank-headers`), or check types
            (e.g.  `structure`).

        infer_schema (bool):
            Infer schema if one wasn't passed as an argument.

        infer_fields (bool):
            Infer schema for columns not present in the received schema.

        order_fields (bool):
            Order source columns based on schema fields order.
            This is useful when you don't want to validate that the data
            columns' order is the same as the schema's.

        error_limit (int):
            Stop validation if the number of errors per table exceeds this value.

        table_limit (int):
            Maximum number of tables to validate.

        row_limit (int):
            Maximum number of rows to validate.

        preset (str):
            Dataset type could be `table` (default), `datapackage`,
            `nested` or custom. Usually, the preset can be inferred from the
            source, so you don't need to define it.

        Any (Any):
            Any additional arguments not defined here will be passed on,
            depending on the chosen `preset`. If the `preset` is `table`, the
            extra arguments will be passed on to `tabulator`_, if it is
            `datapackage`, they will be passed on to the `datapackage`_
            constructor.

    # Raises
        GoodtablesException: Raised on any non-tabular error.

    # Returns
        dict: The validation report.

    """
    source, options, inspector_settings = _parse_arguments(source, **options)

    # Validate
    inspector = Inspector(**inspector_settings)
    report = inspector.inspect(source, **options)

    return report


def _parse_arguments(source, **options):
    # Extract settings
    validation_options = set((
        'checks',
        'skip_checks',
        'infer_schema',
        'infer_fields',
        'order_fields',
        'error_limit',
        'table_limit',
        'row_limit',
        'custom_presets',
        'custom_checks',
    ))
    settings = dict((
        (key, options.pop(key)) for key in validation_options
        if key in options
    ))

    # Support for pathlib.Path
    if hasattr(source, 'joinpath'):
        source = str(source)
    if isinstance(source, list):
        if source and isinstance(source[0], dict) and 'source' in source[0]:
            for index, item in enumerate(source):
                if hasattr(item['source'], 'joinpath'):
                    source[index]['source'] = str(item['source'])

    return source, options, settings

# -*- coding: utf-8 -*- # Module API Validates a source file and returns a report.      # Arguments          source (Union[str, Dict, List[Dict], IO]):             The source to be validated.             It can be a local file path, URL, dict, list of dicts, or a             file-like object. If it's a list of dicts and the `preset` is             "nested", each of the dict key's will be used as if it was passed             as a keyword argument to this method.              The file can be a CSV, XLS, JSON, and any other format supported by             `tabulator`_.          checks (List[str]):             List of checks names to be enabled. They can be             individual check names (e.g. `blank-headers`), or check types (e.g.             `structure`).          skip_checks (List[str]):             List of checks names to be skipped. They can             be individual check names (e.g. `blank-headers`), or check types             (e.g.  `structure`).          infer_schema (bool):             Infer schema if one wasn't passed as an argument.          infer_fields (bool):             Infer schema for columns not present in the received schema.          order_fields (bool):             Order source columns based on schema fields order.             This is useful when you don't want to validate that the data             columns' order is the same as the schema's.          error_limit (int):             Stop validation if the number of errors per table exceeds this value.          table_limit (int):             Maximum number of tables to validate.          row_limit (int):             Maximum number of rows to validate.          preset (str):             Dataset type could be `table` (default), `datapackage`,             `nested` or custom. Usually, the preset can be inferred from the             source, so you don't need to define it.          Any (Any):             Any additional arguments not defined here will be passed on,             depending on the chosen `preset`. If the `preset` is `table`, the             extra arguments will be passed on to `tabulator`_, if it is             `datapackage`, they will be passed on to the `datapackage`_             constructor.      # Raises         GoodtablesException: Raised on any non-tabular error.      # Returns         dict: The validation report. # Validate # Extract settings # Support for pathlib.Path