# Copyright (c) 2022 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# 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 <https://www.gnu.org/licenses/>.
from __future__ import annotations
from abc import ABC
import numpy as np
from .data import Data, DataType, PrimitiveTypeEnum
[docs]class NumericData(Data, ABC):
"""
Data container for floats values
"""
def __init__(self, data_type: DataType, **kwargs):
super().__init__(data_type, **kwargs)
[docs] @classmethod
def primitive_type(cls) -> PrimitiveTypeEnum:
return PrimitiveTypeEnum.INVALID
@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) * np.nan
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
def __call__(self):
return self.values