search_fixtures ¶

def search_fixtures(
    self,
    sport: "capo_elementalinference.types.data_source_sport.DataSourceSport",
    start_date: "capo_elementalinference.types.fixture_date.FixtureDate",
    *,
    config_overrides: Optional[ElementalInferenceClientConfig] = None,
    end_date: Optional[
        "capo_elementalinference.types.fixture_date.FixtureDate"
    ] = None,
    filters: Optional[
        "capo_elementalinference.types.search_filter_list.SearchFilterList"
    ] = None,
    max_results: Optional[int] = None,
    next_token: Optional[str] = None,
) -> (
    "capo_elementalinference.types.search_fixtures_response.SearchFixturesResponse"
):
    """<p>Searches for the fixtures (sports events, such as a specific basketball game) that are available for a sport in a date window. Each fixture in the response includes a fixtureId that you specify in the clipping output of a feed, so that Elemental Inference maps the event data for that fixture onto the clipping metadata. This operation is paginated: if there are more fixtures than fit in one page, the response includes a nextToken that you pass in a subsequent request. </p>

    Args:
        sport: <p>The sport to search for fixtures. Valid values: basketball (search for basketball fixtures), american-football (search for american-football fixtures). </p>
        start_date: <p>The first day of the search window, in UTC. The search includes fixtures that are scheduled on this day. </p> <p>Specify the date in ISO 8601 format, as <code>YYYY-MM-DD</code>. For example, 2026-03-14. </p>
        end_date: <p>The last day of the search window, in UTC. The search includes fixtures that are scheduled on this day. Specify the date in ISO 8601 format, as <code>YYYY-MM-DD</code>. </p> <p>If you omit this parameter, Elemental Inference searches only the day that you specified in startDate. The window from startDate through endDate must not exceed seven days. </p>
        filters: <p>An array of filters that narrow the results. Each filter applies to one dimension of a fixture, such as the competitor. You can specify up to 10 filters. </p> <p>A fixture must satisfy every filter in the array in order to appear in the results. Within one filter, a fixture must match at least one of the values. </p>
        max_results: <p>The maximum number of fixtures to return for each API request.</p> <p>The service might return fewer fixtures than the maxResults value. When more fixtures match the search, the response also includes a nextToken value that you can use to fetch the next batch of results. </p>
        next_token: <p>The token that identifies the batch of results that you want to see.</p> <p>For example, you submit a SearchFixtures request with maxResults set at 5. The service returns the first batch of results (up to 5) and a nextToken value. To see the next batch of results, you submit the SearchFixtures request a second time, with the same search criteria, and specify the nextToken value. </p>

    Raises:
        capo_elementalinference.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_elementalinference.errors.gateway_timed_out_exception.GatewayTimedOutException: <p>The request timed out before the service returned a response. This is a temporary condition. Retry the request. If the problem persists, contact AWS Support. </p>
        capo_elementalinference.errors.internal_server_error_exception.InternalServerErrorException: <p>An internal server error occurred. This is a temporary condition and the request can be retried. If the problem persists, contact AWS Support. </p>
        capo_elementalinference.errors.service_unavailable_exception.ServiceUnavailableException: <p>The service is temporarily unable to handle the request. Retry the request. If the problem persists, contact AWS Support. </p>
        capo_elementalinference.errors.too_many_request_exception.TooManyRequestException: <p>The request was denied due to request throttling. Too many requests have been made within a given time period. Reduce the frequency of requests and use exponential backoff when retrying. </p>
        capo_elementalinference.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by the service. Check the error message for details about which parameter or field is invalid and correct the request before retrying. </p>
        capo_elementalinference.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_elementalinference.types.search_fixtures_request.SearchFixturesRequest]",
    ) -> OperationResponse[
        "capo_elementalinference.types.search_fixtures_response.SearchFixturesResponse"
    ]:
        import capo_elementalinference._operations.elemental_inference.search_fixtures

        output, http_response = (
            capo_elementalinference._operations.elemental_inference.search_fixtures.search_fixtures(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("SearchFixtures")
    )
    input_: capo_elementalinference.types.search_fixtures_request.SearchFixturesRequest = {
        "sport": sport,
        "start_date": start_date,
    }
    if end_date is not None:
        input_["end_date"] = end_date
    if filters is not None:
        input_["filters"] = filters
    if max_results is not None:
        input_["max_results"] = max_results
    if next_token is not None:
        input_["next_token"] = next_token

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output