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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,137 @@ | ||
| from datetime import datetime, timedelta | ||
| from typing import List, Tuple | ||
|
|
||
| import numpy as np | ||
| import numpy.typing as npt | ||
|
|
||
| from ert.config.parsing import ConfigWarning | ||
| from ert.config.parsing.observations_parser import ( | ||
| DateValues, | ||
| ErrorValues, | ||
| ObservationConfigError, | ||
| SummaryValues, | ||
| ) | ||
|
|
||
| DEFAULT_TIME_DELTA = timedelta(seconds=30) | ||
|
|
||
|
|
||
| def get_time(date_dict: DateValues, start_time: datetime) -> Tuple[datetime, str]: | ||
| if date_dict.date is not None: | ||
| date_str = date_dict.date | ||
| try: | ||
| return datetime.fromisoformat(date_str), f"DATE={date_str}" | ||
| except ValueError: | ||
| try: | ||
| date = datetime.strptime(date_str, "%d/%m/%Y") | ||
| ConfigWarning.ert_context_warn( | ||
| f"Deprecated time format {date_str}." | ||
| " Please use ISO date format YYYY-MM-DD", | ||
| date_str, | ||
| ) | ||
| return date, f"DATE={date_str}" | ||
| except ValueError as err: | ||
| raise ObservationConfigError.with_context( | ||
| f"Unsupported date format {date_str}." | ||
| " Please use ISO date format", | ||
| date_str, | ||
| ) from err | ||
|
|
||
| if date_dict.days is not None: | ||
| days = date_dict.days | ||
| return start_time + timedelta(days=days), f"DAYS={days}" | ||
| if date_dict.hours is not None: | ||
| hours = date_dict.hours | ||
| return start_time + timedelta(hours=hours), f"HOURS={hours}" | ||
| raise ValueError("Missing time specifier") | ||
|
|
||
|
|
||
| def _find_nearest( | ||
| time_map: List[datetime], | ||
| time: datetime, | ||
| threshold: timedelta = DEFAULT_TIME_DELTA, | ||
| ) -> int: | ||
| nearest_index = -1 | ||
| nearest_diff = None | ||
| for i, t in enumerate(time_map): | ||
| diff = abs(time - t) | ||
| if diff < threshold and (nearest_diff is None or nearest_diff > diff): | ||
| nearest_diff = diff | ||
| nearest_index = i | ||
| if nearest_diff is None: | ||
| raise IndexError(f"{time} is not in the time map") | ||
| return nearest_index | ||
|
|
||
|
|
||
| def get_restart( | ||
| date_dict: DateValues, | ||
| obs_name: str, | ||
| time_map: List[datetime], | ||
| has_refcase: bool, | ||
| ) -> int: | ||
| if date_dict.restart is not None: | ||
| return date_dict.restart | ||
| if not time_map: | ||
| raise ObservationConfigError.with_context( | ||
| f"Missing REFCASE or TIME_MAP for observations: {obs_name}", | ||
| obs_name, | ||
| ) | ||
|
|
||
| try: | ||
| time, date_str = get_time(date_dict, time_map[0]) | ||
| except ObservationConfigError: | ||
| raise | ||
| except ValueError as err: | ||
| raise ObservationConfigError.with_context( | ||
| f"Failed to parse date of {obs_name}", obs_name | ||
| ) from err | ||
|
|
||
| try: | ||
| return _find_nearest(time_map, time) | ||
| except IndexError as err: | ||
| raise ObservationConfigError.with_context( | ||
| f"Could not find {time} ({date_str}) in " | ||
| f"the time map for observations {obs_name}" | ||
| + ( | ||
| "The time map is set from the REFCASE keyword. Either " | ||
| "the REFCASE has an incorrect/missing date, or the observation " | ||
| "is given an incorrect date.)" | ||
| if has_refcase | ||
| else " (The time map is set from the TIME_MAP " | ||
| "keyword. Either the time map file has an " | ||
| "incorrect/missing date, or the observation is given an " | ||
| "incorrect date." | ||
| ), | ||
| obs_name, | ||
| ) from err | ||
|
|
||
|
|
||
| def make_value_and_std_dev( | ||
| observation_dict: SummaryValues, | ||
| ) -> Tuple[float, float]: | ||
| value = observation_dict.value | ||
| return ( | ||
| value, | ||
| float( | ||
| handle_error_mode( | ||
| np.array(value), | ||
| observation_dict, | ||
| ) | ||
| ), | ||
| ) | ||
|
|
||
|
|
||
| def handle_error_mode( | ||
| values: "npt.ArrayLike", | ||
| error_dict: ErrorValues, | ||
| ) -> "npt.NDArray[np.double]": | ||
| values = np.asarray(values) | ||
| error_mode = error_dict.error_mode | ||
| error_min = error_dict.error_min | ||
| error = error_dict.error | ||
| if error_mode == "ABS": | ||
| return np.full(values.shape, error) | ||
| elif error_mode == "REL": | ||
| return np.abs(values) * error | ||
| elif error_mode == "RELMIN": | ||
| return np.maximum(np.abs(values) * error, np.full(values.shape, error_min)) | ||
| raise ObservationConfigError(f"Unknown error mode {error_mode}", error_mode) |
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