<reponame>perillaroc/nwpc-data
from typing import Union, Dict, List, Optional
from pathlib import Path

import numpy as np
import pandas as pd
import xarray as xr


from .grads_ctl import GradsCtlParser
from .grads_data_handler import GradsDataHandler, GradsRecordHandler


def load_field_from_file(
        file_path: Union[str, Path],
        parameter: str,
        level_type: str = None,
        level: Union[int, float, List] = None,
        level_dim: Optional[str] = None,
        latitude_direction: str = "degree_north",
        forecast_time: Union[str, pd.Timedelta] = None,
        **kwargs
) -> Optional[xr.DataArray]:
    """
    Load one field or fields of one parameter from GrADS binary file.

    Parameters
    ----------
    file_path
    parameter
    level_type
        * pl / ml
        * index
        * single
        * None
    level
    level_dim
    latitude_direction
        * degree_north
        * degree_south
    forecast_time
    kwargs

    Returns
    -------
    xr.DataArray
        Xarray DataArray if found, or None if not.

    Examples
    --------
    Load GRAPES GFS GMF postvar data:

    >>> from reki.data_finder import find_local_file
    >>> from reki.format.grads import load_field_from_file
    >>> postvar_file_path = find_local_file(
    ...     "grapes_gfs_gmf/bin/postvar_ctl",
    ...     start_time="2021080200",
    ...     forecast_time="36h"
    ... )
    >>> field = load_field_from_file(
    ...     postvar_file_path,
    ...     parameter="t",
    ...     level_type="pl",
    ...     level=850
    ... )

    Load GRAPES TYM postvar data. Set ``forecast_time`` option because all data files of one cycle use one CTL file.

    >>> postvar_file_path = find_local_file(
    ...     "grapes_tym/bin/postvar_ctl",
    ...     start_time="2021080200",
    ... )
    >>> field = load_field_from_file(
    ...     postvar_file_path,
    ...     parameter="t",
    ...     level_type="pl",
    ...     forecast_time="24h",
    ...     level=850
    ... )
    """
    if isinstance(forecast_time, str):
        forecast_time = pd.to_timedelta(forecast_time)

    ctl_parser = GradsCtlParser()
    ctl_parser.parse(file_path)
    grads_ctl = ctl_parser.grads_ctl

    # level_type: pl, index, single
    grads_level_type = "multi"
    level_dim_name = "level"

    if not isinstance(level, List) and level is not None:
        level = [level]

    if level_type == "single":
        level = [0]
        grads_level_type = "single"
    elif level_type == "index":
        level = [grads_ctl.zdef["values"][cur_level] for cur_level in level]
    elif level_type in ("pl", "ml"):
        level_dim_name = level_type
        if level is None:
            level = grads_ctl.zdef["values"]
    elif level_type is None:
        grads_level_type = None
        # level = None

    if level_dim is not None:
        level_dim_name = level_dim

    data_handler = GradsDataHandler(grads_ctl)

    xarray_records = []
    for index, record in enumerate(grads_ctl.record):
        if not check_record(
            record,
            parameter=parameter,
            level=level,
            level_type=grads_level_type,
            forecast_time=forecast_time,
        ):
            continue

        offset = data_handler.get_offset_by_record_index(record["record_index"])
        record_handler = GradsRecordHandler(grads_ctl, index, offset)

        xarray_record = create_data_array_from_record(
            record=record_handler,
            parameter=parameter,
            level=record["level"],
            level_dim_name=level_dim_name,
            latitude_direction=latitude_direction,
        )
        xarray_records.append(xarray_record)

    record_count = len(xarray_records)
    if record_count == 0:
        return None
    elif record_count == 1:
        return xarray_records[0]
    else:
        data = xr.concat(xarray_records, level_dim_name)

    return data


def check_record(
        record: Dict,
        parameter: str,
        level_type: str = None,
        level: Union[int, float, List] = None,
        valid_time: pd.Timestamp = None,
        forecast_time: pd.Timedelta = None
) -> bool:
    if parameter != record["name"]:
        return False

    if level_type is not None and level_type != record["level_type"]:
        return False

    if level is not None:
        if isinstance(level, List):
            if record["level"] not in level:
                return False
        else:
            if level != record["level"]:
                return False

    if valid_time is not None and valid_time != record["valid_time"]:
        return False

    if forecast_time is not None and forecast_time != record["forecast_time"]:
        return False

    return True


def create_data_array_from_record(
        record: GradsRecordHandler,
        parameter,
        level,
        level_dim_name=None,
        latitude_direction="degree_north",
) -> Optional[xr.DataArray]:
    grads_ctl = record.grads_ctl

    # values
    file_path = grads_ctl.get_data_file_path(record.record_info)
    with open(file_path, "rb") as f:
        values = record.load_data(f)

    # coords
    lons = grads_ctl.xdef["values"]
    lats = grads_ctl.ydef["values"]

    if latitude_direction == "degree_north":
        values = np.flip(values, 0)
        lats = lats[::-1]

    coords = {}
    coords["latitude"] = xr.Variable(
        "latitude",
        lats,
        attrs={
            "units": latitude_direction,
            "standard_name": "latitude",
            "long_name": "latitude"
        },
    )
    coords["longitude"] = xr.Variable(
        "longitude",
        lons,
        attrs={
            "units": "degrees_east",
            "standard_name": "longitude",
            "long_name": "longitude"
        }
    )

    coords[level_dim_name] = level
    coords["valid_time"] = record.record_info["valid_time"]

    if grads_ctl.start_time is not None and grads_ctl.forecast_time is not None:
        coords["start_time"] = grads_ctl.start_time
        coords["forecast_time"] = grads_ctl.forecast_time

    # dims
    dims = ("latitude", "longitude")

    # attrs
    data_attrs = {
        "description": record.record_info["description"]
    }

    data = xr.DataArray(
        values,
        dims=dims,
        coords=coords,
        attrs=data_attrs,
        name=parameter,
    )

    return data

from typing import Union, Dict, List, Optional
from pathlib import Path

import numpy as np
import pandas as pd
import xarray as xr


from .grads_ctl import GradsCtlParser
from .grads_data_handler import GradsDataHandler, GradsRecordHandler


def load_field_from_file(
        file_path: Union[str, Path],
        parameter: str,
        level_type: str = None,
        level: Union[int, float, List] = None,
        level_dim: Optional[str] = None,
        latitude_direction: str = "degree_north",
        forecast_time: Union[str, pd.Timedelta] = None,
        **kwargs
) -> Optional[xr.DataArray]:
    """
    Load one field or fields of one parameter from GrADS binary file.

    Parameters
    ----------
    file_path
    parameter
    level_type
        * pl / ml
        * index
        * single
        * None
    level
    level_dim
    latitude_direction
        * degree_north
        * degree_south
    forecast_time
    kwargs

    Returns
    -------
    xr.DataArray
        Xarray DataArray if found, or None if not.

    Examples
    --------
    Load GRAPES GFS GMF postvar data:

    >>> from reki.data_finder import find_local_file
    >>> from reki.format.grads import load_field_from_file
    >>> postvar_file_path = find_local_file(
    ...     "grapes_gfs_gmf/bin/postvar_ctl",
    ...     start_time="2021080200",
    ...     forecast_time="36h"
    ... )
    >>> field = load_field_from_file(
    ...     postvar_file_path,
    ...     parameter="t",
    ...     level_type="pl",
    ...     level=850
    ... )

    Load GRAPES TYM postvar data. Set ``forecast_time`` option because all data files of one cycle use one CTL file.

    >>> postvar_file_path = find_local_file(
    ...     "grapes_tym/bin/postvar_ctl",
    ...     start_time="2021080200",
    ... )
    >>> field = load_field_from_file(
    ...     postvar_file_path,
    ...     parameter="t",
    ...     level_type="pl",
    ...     forecast_time="24h",
    ...     level=850
    ... )
    """
    if isinstance(forecast_time, str):
        forecast_time = pd.to_timedelta(forecast_time)

    ctl_parser = GradsCtlParser()
    ctl_parser.parse(file_path)
    grads_ctl = ctl_parser.grads_ctl

    # level_type: pl, index, single
    grads_level_type = "multi"
    level_dim_name = "level"

    if not isinstance(level, List) and level is not None:
        level = [level]

    if level_type == "single":
        level = [0]
        grads_level_type = "single"
    elif level_type == "index":
        level = [grads_ctl.zdef["values"][cur_level] for cur_level in level]
    elif level_type in ("pl", "ml"):
        level_dim_name = level_type
        if level is None:
            level = grads_ctl.zdef["values"]
    elif level_type is None:
        grads_level_type = None
        # level = None

    if level_dim is not None:
        level_dim_name = level_dim

    data_handler = GradsDataHandler(grads_ctl)

    xarray_records = []
    for index, record in enumerate(grads_ctl.record):
        if not check_record(
            record,
            parameter=parameter,
            level=level,
            level_type=grads_level_type,
            forecast_time=forecast_time,
        ):
            continue

        offset = data_handler.get_offset_by_record_index(record["record_index"])
        record_handler = GradsRecordHandler(grads_ctl, index, offset)

        xarray_record = create_data_array_from_record(
            record=record_handler,
            parameter=parameter,
            level=record["level"],
            level_dim_name=level_dim_name,
            latitude_direction=latitude_direction,
        )
        xarray_records.append(xarray_record)

    record_count = len(xarray_records)
    if record_count == 0:
        return None
    elif record_count == 1:
        return xarray_records[0]
    else:
        data = xr.concat(xarray_records, level_dim_name)

    return data


def check_record(
        record: Dict,
        parameter: str,
        level_type: str = None,
        level: Union[int, float, List] = None,
        valid_time: pd.Timestamp = None,
        forecast_time: pd.Timedelta = None
) -> bool:
    if parameter != record["name"]:
        return False

    if level_type is not None and level_type != record["level_type"]:
        return False

    if level is not None:
        if isinstance(level, List):
            if record["level"] not in level:
                return False
        else:
            if level != record["level"]:
                return False

    if valid_time is not None and valid_time != record["valid_time"]:
        return False

    if forecast_time is not None and forecast_time != record["forecast_time"]:
        return False

    return True


def create_data_array_from_record(
        record: GradsRecordHandler,
        parameter,
        level,
        level_dim_name=None,
        latitude_direction="degree_north",
) -> Optional[xr.DataArray]:
    grads_ctl = record.grads_ctl

    # values
    file_path = grads_ctl.get_data_file_path(record.record_info)
    with open(file_path, "rb") as f:
        values = record.load_data(f)

    # coords
    lons = grads_ctl.xdef["values"]
    lats = grads_ctl.ydef["values"]

    if latitude_direction == "degree_north":
        values = np.flip(values, 0)
        lats = lats[::-1]

    coords = {}
    coords["latitude"] = xr.Variable(
        "latitude",
        lats,
        attrs={
            "units": latitude_direction,
            "standard_name": "latitude",
            "long_name": "latitude"
        },
    )
    coords["longitude"] = xr.Variable(
        "longitude",
        lons,
        attrs={
            "units": "degrees_east",
            "standard_name": "longitude",
            "long_name": "longitude"
        }
    )

    coords[level_dim_name] = level
    coords["valid_time"] = record.record_info["valid_time"]

    if grads_ctl.start_time is not None and grads_ctl.forecast_time is not None:
        coords["start_time"] = grads_ctl.start_time
        coords["forecast_time"] = grads_ctl.forecast_time

    # dims
    dims = ("latitude", "longitude")

    # attrs
    data_attrs = {
        "description": record.record_info["description"]
    }

    data = xr.DataArray(
        values,
        dims=dims,
        coords=coords,
        attrs=data_attrs,
        name=parameter,
    )

    return data
Load one field or fields of one parameter from GrADS binary file.      Parameters     ----------     file_path     parameter     level_type         * pl / ml         * index         * single         * None     level     level_dim     latitude_direction         * degree_north         * degree_south     forecast_time     kwargs      Returns     -------     xr.DataArray         Xarray DataArray if found, or None if not.      Examples     --------     Load GRAPES GFS GMF postvar data:      >>> from reki.data_finder import find_local_file     >>> from reki.format.grads import load_field_from_file     >>> postvar_file_path = find_local_file(     ...     "grapes_gfs_gmf/bin/postvar_ctl",     ...     start_time="2021080200",     ...     forecast_time="36h"     ... )     >>> field = load_field_from_file(     ...     postvar_file_path,     ...     parameter="t",     ...     level_type="pl",     ...     level=850     ... )      Load GRAPES TYM postvar data. Set ``forecast_time`` option because all data files of one cycle use one CTL file.      >>> postvar_file_path = find_local_file(     ...     "grapes_tym/bin/postvar_ctl",     ...     start_time="2021080200",     ... )     >>> field = load_field_from_file(     ...     postvar_file_path,     ...     parameter="t",     ...     level_type="pl",     ...     forecast_time="24h",     ...     level=850     ... ) # level_type: pl, index, single # level = None # values # coords # dims # attrs