dispatcher.py

import numpy as np
import joblib
from astropy import table
from csst_dfs_client import plan, level0, level1
from tqdm import trange

from .._csst import csst

# from csst_dag._csst import csst

# THESE ARE GENERAL PARAMETERS!
PLAN_PARAMS = {
    "dataset": None,
    "instrument": None,
    "obs_type": None,
    "obs_group": None,
    "obs_id": None,
    "proposal_id": None,
}

LEVEL0_PARAMS = {
    "dataset": None,
    "instrument": None,
    "obs_type": None,
    "obs_group": None,
    "obs_id": None,
    "detector": None,
    "prc_status": -1024,
}

LEVEL1_PARAMS = {
    "dataset": None,
    "instrument": None,
    "obs_type": None,
    "obs_group": None,
    "obs_id": None,
    "detector": None,
    "prc_status": -1024,
    "data_model": None,
    "batch_id": "default_batch",
}

PROC_PARAMS = {
    "priority": 1,
    "batch_id": "default_batch",
    "pmapname": "pmapname",
    "final_prc_status": -2,
    "demo": False,
    # should be capable to extend
}

# plan basis keys
PLAN_BASIS_KEYS = (
    "dataset",
    "instrument",
    "obs_type",
    "obs_group",
    "obs_id",
    "n_frame",
    "_id",
)

# data basis keys
DATA_BASIS_KEYS = (
    "dataset",
    "instrument",
    "obs_type",
    "obs_group",
    "obs_id",
    "detector",
    "file_name",
    "_id",
)

# join_type for data x plan
PLAN_JOIN_TYPE = "inner"
"""
References:
    - https://docs.astropy.org/en/stable/api/astropy.table.join.html
    - https://docs.astropy.org/en/stable/table/operations.html#join

Typical types:
    - inner join: Only matching rows from both tables
    - left join: All rows from left table, matching rows from right table
    - right join: All rows from right table, matching rows from left table
    - outer join: All rows from both tables
    - cartesian join: Every combination of rows from both tables
"""


def override_common_keys(d1: dict, d2: dict) -> dict:
    """
    Construct a new dictionary by updating the values of basis_keys that exists in the first dictionary
    with the values of the second dictionary.

    Parameters
    ----------
    d1 : dict
        The first dictionary.
    d2 : dict
        The second dictionary.

    Returns
    -------
    dict:
        The updated dictionary.
    """
    return {k: d2[k] if k in d2.keys() else d1[k] for k in d1.keys()}


def extract_basis_table(dlist: list[dict], basis_keys: tuple) -> table.Table:
    """Extract basis key-value pairs from a list of dictionaries."""
    return table.Table([{k: d.get(k) for k in basis_keys} for d in dlist])


def split_data_basis(data_basis: table.Table, n_split: int = 1) -> list[table.Table]:
    """Split data basis into n_split parts."""
    assert (
        np.unique(data_basis["dataset"]).size == 1
    ), "Only one dataset is allowed for splitting."
    # sort
    data_basis.sort(keys=["dataset", "obs_id"])
    # get unique obsid
    u_obsid, i_obsid, c_obsid = np.unique(
        data_basis["obs_id"].data, return_index=True, return_counts=True
    )
    # set chunk size
    chunk_size = int(np.fix(len(u_obsid) / n_split))
    # initialize chunks
    chunks = []
    for i_split in range(n_split):
        if i_split < n_split - 1:
            chunks.append(
                data_basis[
                    i_obsid[i_split * chunk_size] : i_obsid[(i_split + 1) * chunk_size]
                ]
            )
        else:
            chunks.append(data_basis[i_obsid[i_split * chunk_size] :])
    # np.unique(table.vstack(chunks)["_id"])
    # np.unique(table.vstack(chunks)["obs_id"])
    return chunks


class Dispatcher:
    """
    A class to dispatch tasks based on the observation type.
    """

    @staticmethod
    def find_plan_basis(**kwargs) -> table.Table:
        """
        Find plan records.
        """
        # query
        qr = plan.find(**override_common_keys(PLAN_PARAMS, kwargs))
        assert qr.success, qr
        # plan basis / obsid basis
        for _ in qr.data:
            _["n_frame"] = (
                _["params"]["n_epec_frame"] if _["instrument"] == "HSTDM" else 1
            )
        plan_basis = extract_basis_table(
            qr.data,
            PLAN_BASIS_KEYS,
        )
        return plan_basis

    @staticmethod
    def find_level0_basis(**kwargs) -> table.Table:
        """
        Find level0 records.
        """
        # query
        qr = level0.find(**override_common_keys(LEVEL0_PARAMS, kwargs))
        assert qr.success, qr
        # data basis
        data_basis = extract_basis_table(
            qr.data,
            DATA_BASIS_KEYS,
        )
        return data_basis

    @staticmethod
    def find_level1_basis(**kwargs) -> table.Table:
        """
        Find level1 records.
        """
        # query
        qr = level1.find(**override_common_keys(LEVEL1_PARAMS, kwargs))
        assert qr.success, qr
        # data basis
        data_basis = extract_basis_table(
            qr.data,
            DATA_BASIS_KEYS,
        )
        return data_basis

    @staticmethod
    def dispatch_file(
        plan_basis: table.Table,
        data_basis: table.Table,
    ) -> list[dict]:
        # unique obsid --> useless
        # u_obsid = table.unique(data_basis["dataset", "obs_id"])

        # initialize task list
        task_list = []

        # loop over plan
        for i_data_basis in trange(
            len(data_basis),
            unit="task",
            dynamic_ncols=True,
        ):
            # i_data_basis = 1
            this_task = dict(data_basis[i_data_basis])
            this_data_basis = data_basis[i_data_basis : i_data_basis + 1]
            this_relevant_plan = table.join(
                this_data_basis,
                plan_basis,
                keys=["dataset", "obs_id"],
                join_type="inner",
            )
            # append this task
            task_list.append(
                dict(
                    task=this_task,
                    success=True,
                    relevant_plan=this_relevant_plan,
                    relevant_data=data_basis[i_data_basis : i_data_basis + 1],
                    n_relevant_plan=len(this_relevant_plan),
                    n_relevant_data=1,
                )
            )

        return task_list

    @staticmethod
    def dispatch_detector(
        plan_basis: table.Table,
        data_basis: table.Table,
        n_jobs: int = 1,
    ) -> list[dict]:
        """

        Parameters
        ----------
        plan_basis
        data_basis
        n_jobs

        Returns
        -------

        """
        if n_jobs != 1:
            task_list = joblib.Parallel(n_jobs=n_jobs)(
                joblib.delayed(Dispatcher.dispatch_detector)(plan_basis, _)
                for _ in split_data_basis(data_basis, n_split=n_jobs)
            )
            return sum(task_list, [])

        # unique obsid
        u_obsid = table.unique(data_basis["dataset", "obs_id"])
        relevant_plan = table.join(
            u_obsid,
            plan_basis,
            keys=["dataset", "obs_id"],
            join_type=PLAN_JOIN_TYPE,
        )
        print(f"{len(relevant_plan)} relevant plan records")

        u_data_detector = table.unique(
            data_basis[
                "dataset",
                "instrument",
                "obs_type",
                "obs_group",
                "obs_id",
                "detector",
            ]
        )

        # initialize task list
        task_list = []

        # loop over plan
        for i_data_detector in trange(
            len(u_data_detector),
            unit="task",
            dynamic_ncols=True,
        ):
            # i_data_detector = 1
            this_task = dict(u_data_detector[i_data_detector])
            this_data_detector = u_data_detector[i_data_detector : i_data_detector + 1]

            # join data and plan
            this_data_detector_files = table.join(
                this_data_detector,
                data_basis,
                keys=[
                    "dataset",
                    "instrument",
                    "obs_type",
                    "obs_group",
                    "obs_id",
                    "detector",
                ],
                join_type="inner",
            )
            this_data_detector_plan = table.join(
                this_data_detector,
                relevant_plan,
                keys=[
                    "dataset",
                    "instrument",
                    "obs_type",
                    "obs_group",
                    "obs_id",
                ],
                join_type=PLAN_JOIN_TYPE,
            )

            # whether detector effective
            this_detector = this_data_detector["detector"][0]
            this_instrument = this_data_detector["instrument"][0]
            this_detector_effective = (
                this_detector in csst[this_instrument].effective_detector_names
            )

            n_files_expected = (
                this_data_detector_plan["n_frame"][0]
                if len(this_data_detector_plan) > 0
                else 0
            )
            n_files_found = len(this_data_detector_files)
            # append this task
            task_list.append(
                dict(
                    task=this_task,
                    success=(
                        len(this_data_detector_plan) == 1
                        and len(this_data_detector_files) == 1
                        and this_detector_effective
                        and n_files_found == n_files_expected
                    ),
                    relevant_plan=this_data_detector_plan,
                    relevant_data=this_data_detector_files,
                    n_relevant_plan=len(this_data_detector_plan),
                    n_relevant_data=len(this_data_detector_files),
                )
            )
        return task_list

    @staticmethod
    def dispatch_obsid(
        plan_basis: table.Table,
        data_basis: table.Table,
        n_jobs: int = 1,
    ) -> list[dict]:

        if n_jobs != 1:
            task_list = joblib.Parallel(n_jobs=n_jobs)(
                joblib.delayed(Dispatcher.dispatch_obsid)(plan_basis, _)
                for _ in split_data_basis(data_basis, n_split=n_jobs)
            )
            return sum(task_list, [])

        # unique obsid
        u_obsid = table.unique(data_basis["dataset", "obs_id"])
        relevant_plan = table.join(
            u_obsid,
            plan_basis,
            keys=["dataset", "obs_id"],
            join_type=PLAN_JOIN_TYPE,
        )
        print(f"{len(relevant_plan)} relevant plan records")

        u_data_obsid = table.unique(
            data_basis[
                "dataset",
                "instrument",
                "obs_type",
                "obs_group",
                "obs_id",
            ]
        )

        # initialize task list
        task_list = []

        # loop over plan
        for i_data_obsid in trange(
            len(u_data_obsid),
            unit="task",
            dynamic_ncols=True,
        ):
            # i_data_obsid = 2
            this_task = dict(u_data_obsid[i_data_obsid])
            this_data_obsid = u_data_obsid[i_data_obsid : i_data_obsid + 1]

            # join data and plan
            this_data_obsid_file = table.join(
                this_data_obsid,
                data_basis,
                keys=[
                    "dataset",
                    "instrument",
                    "obs_type",
                    "obs_group",
                    "obs_id",
                ],
                join_type="inner",
            )
            this_data_obsid_plan = table.join(
                this_data_obsid,
                relevant_plan,
                keys=[
                    "dataset",
                    "instrument",
                    "obs_type",
                    "obs_group",
                    "obs_id",
                ],
                join_type=PLAN_JOIN_TYPE,
            )

            # whether effective detectors all there
            this_instrument = this_data_obsid["instrument"][0]
            this_n_frame = (
                this_data_obsid_plan["n_frame"] if len(this_data_obsid_plan) > 0 else 0
            )
            this_effective_detector_names = csst[
                this_instrument
            ].effective_detector_names

            if this_instrument == "HSTDM":
                # 不确定以后是1个探测器还是2个探测器
                this_n_file_found = len(this_data_obsid_file)
                this_n_file_expected = (this_n_frame, this_n_frame * 2)
                this_success = this_n_file_found in this_n_file_expected
            else:
                # for other instruments, e.g., MSC
                # n_file_found = len(this_obsgroup_obsid_file)
                # n_file_expected = len(effective_detector_names)
                # this_success &= n_file_found == n_file_expected

                # or more strictly, expected files are a subset of files found
                this_success = set(this_effective_detector_names) <= set(
                    this_data_obsid_file["detector"]
                )

            # append this task
            task_list.append(
                dict(
                    task=this_task,
                    success=this_success,
                    relevant_plan=this_data_obsid_plan,
                    relevant_data=this_data_obsid_file,
                    n_relevant_plan=len(this_data_obsid_plan),
                    n_relevant_data=len(this_data_obsid_file),
                )
            )

        return task_list

    @staticmethod
    def dispatch_obsgroup(
        plan_basis: table.Table,
        data_basis: table.Table,
        # n_jobs: int = 1,
    ) -> list[dict]:

        # unique obsgroup basis
        obsgroup_basis = table.unique(
            plan_basis[
                "dataset",
                "instrument",
                "obs_type",
                "obs_group",
            ]
        )

        # initialize task list
        task_list = []

        # loop over obsgroup
        for i_obsgroup in trange(
            len(obsgroup_basis),
            unit="task",
            dynamic_ncols=True,
        ):

            # i_obsgroup = 1
            this_task = dict(obsgroup_basis[i_obsgroup])
            this_success = True

            this_obsgroup_plan = table.join(
                obsgroup_basis[i_obsgroup : i_obsgroup + 1],  # this obsgroup
                plan_basis,
                keys=["dataset", "instrument", "obs_type", "obs_group"],
                join_type=PLAN_JOIN_TYPE,
            )
            this_obsgroup_file = table.join(
                this_obsgroup_plan,
                data_basis,
                keys=["dataset", "instrument", "obs_type", "obs_group", "obs_id"],
                join_type="inner",
                table_names=["plan", "data"],
            )

            # loop over obsid
            for i_obsid in range(len(this_obsgroup_plan)):
                # i_obsid = 1
                # print(i_obsid)
                this_instrument = this_obsgroup_plan[i_obsid]["instrument"]
                this_n_frame = this_obsgroup_plan[i_obsid]["n_frame"]
                this_effective_detector_names = csst[
                    this_instrument
                ].effective_detector_names

                this_obsgroup_obsid_file = table.join(
                    this_obsgroup_plan[i_obsid : i_obsid + 1],  # this obsid
                    data_basis,
                    keys=["dataset", "instrument", "obs_type", "obs_group", "obs_id"],
                    join_type="inner",
                    table_names=["plan", "data"],
                )

                if this_instrument == "HSTDM":
                    # 不确定以后是1个探测器还是2个探测器
                    this_n_file_found = len(this_obsgroup_obsid_file)
                    this_n_file_expected = (this_n_frame, this_n_frame * 2)
                    this_success &= this_n_file_found in this_n_file_expected
                else:
                    # for other instruments, e.g., MSC
                    # n_file_found = len(this_obsgroup_obsid_file)
                    # n_file_expected = len(effective_detector_names)
                    # this_success &= n_file_found == n_file_expected

                    # or more strictly, expected files are a subset of files found
                    this_success &= set(this_effective_detector_names) <= set(
                        this_obsgroup_obsid_file["detector"]
                    )

            # append this task
            task_list.append(
                dict(
                    task=this_task,
                    success=this_success,
                    relevant_plan=this_obsgroup_plan,
                    relevant_data=this_obsgroup_file,
                    n_relevant_plan=len(this_obsgroup_plan),
                    n_relevant_data=this_obsgroup_file,
                )
            )
        return task_list

    @staticmethod
    def load_test_data() -> tuple:
        import joblib

        plan_recs = joblib.load("dagtest/csst-msc-c9-25sqdeg-v3.plan.dump")
        data_recs = joblib.load("dagtest/csst-msc-c9-25sqdeg-v3.level0.dump")
        print(f"{len(plan_recs.data)} plan records")
        print(f"{len(data_recs.data)} data records")
        for _ in plan_recs.data:
            _["n_frame"] = (
                _["params"]["n_epec_frame"] if _["instrument"] == "HSTDM" else 1
            )
        # 未来如果HSTDM的设定简化一些，这里n_frame可以改成n_file，更直观
        plan_basis = extract_basis_table(
            plan_recs.data,
            PLAN_BASIS_KEYS,
        )
        data_basis = extract_basis_table(
            data_recs.data,
            DATA_BASIS_KEYS,
        )
        return plan_basis, data_basis