validate_inputs

Module for validating simulation inputs.

ska_sdp_resource_model.simulate.validate_inputs.capacity_storage_ok(observation, hardware_capacity_pb)

Check if capacity storage is sufficient for the observation.

ska_sdp_resource_model.simulate.validate_inputs.compute_nodes_ok(observation, hardware_config)

Check if compute nodes are sufficient for the pipeline.

ska_sdp_resource_model.simulate.validate_inputs.configuration_ok(observations, hardware_config)

Check storage and compute requirements are sufficient.

ska_sdp_resource_model.simulate.validate_inputs.data_product_storage_ok(observation, hardware_data_product_pb)

Check if data product storage is sufficient for the observation.

ska_sdp_resource_model.simulate.validate_inputs.inputs_ok(observations, hardware_config)

Validate simulation inputs.

Parameters:

• observations (list) – Sequence of scheduling blocks with pipeline configurations.

• hardware_config (dict) – Hardware configuration data.

Returns:

bool – True if simulation inputs are valid, False otherwise.

ska_sdp_resource_model.simulate.validate_inputs.performance_storage_ok(observation, hardware_performance_tb)

Check if performance storage is sufficient for the observation.

ska_sdp_resource_model.simulate.validate_inputs.scheduling_block_storage_ok(sb_id, scheduling_block, hardware_capacity_pb, hardware_data_product_pb)

Check if there is sufficient capacity storage for the scheduling block.

Parameters:

storage_limit (int) – The total capacity storage limit in GB.

Returns:

bool – True if there is sufficient capacity storage, False otherwise.

ska_sdp_resource_model.simulate.validate_inputs.storage_ok(observation, hardware_config)

Check if performance storage is sufficient for the observation.