Source code for

#  Copyright (c) 2023 Mira Geoscience Ltd.
#  This file is part of geoh5py.
#  geoh5py is free software: you can redistribute it and/or modify
#  it under the terms of the GNU Lesser General Public License as published by
#  the Free Software Foundation, either version 3 of the License, or
#  (at your option) any later version.
#  geoh5py is distributed in the hope that it will be useful,
#  but WITHOUT ANY WARRANTY; without even the implied warranty of
#  GNU Lesser General Public License for more details.
#  You should have received a copy of the GNU Lesser General Public License
#  along with geoh5py.  If not, see <>.

from __future__ import annotations

from abc import ABC, abstractmethod

import numpy as np

from .data import Data, PrimitiveTypeEnum

[docs]class NumericData(Data, ABC): """ Data container for floats values """
[docs] @classmethod def primitive_type(cls) -> PrimitiveTypeEnum: return PrimitiveTypeEnum.INVALID
@property @abstractmethod def ndv(self): """No-data-value""" @property def values(self) -> np.ndarray | None: """ :return: values: An array of float values """ if getattr(self, "_values", None) is None: values = self.workspace.fetch_values(self) if isinstance(values, (np.ndarray, type(None))): self._values = self.check_vector_length(values) return self._values @values.setter def values(self, values: np.ndarray | None): if isinstance(values, (np.ndarray, type(None))): values = self.check_vector_length(values) else: raise ValueError( f"Input 'values' for {self} must be of type {np.ndarray} or None." ) self._values = values self.workspace.update_attribute(self, "values")
[docs] def check_vector_length(self, values) -> np.ndarray: """ Check for possible mismatch between the length of values stored and the expected number of cells or vertices. """ if self.n_values is not None: if values is None or len(values) < self.n_values: full_vector = np.ones(self.n_values, dtype=type(self.ndv)) if isinstance(self.ndv, float): full_vector *= np.nan else: full_vector *= self.ndv full_vector[: len(np.ravel(values))] = np.ravel(values) return full_vector if len(values) > self.n_values: raise ValueError( f"Input 'values' of shape({self.n_values},) expected. " f"Array of shape{values.shape} provided.)" ) return values