Source code for

#  Copyright (c) 2024 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
from warnings import warn

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"""
[docs] @abstractmethod def format_type(self, values: np.ndarray) -> np.ndarray: """ Check if the type of values is valid and convert it to right dtype. :param values: numpy array to modify. :return: the formatted values. """
@property def values(self) -> np.ndarray | None: """ :return: values: An array of values """ if getattr(self, "_values", None) is None: values = self.workspace.fetch_values(self) if isinstance(values, (np.ndarray, type(None))): self._values = self.format_values(values) return self._values @values.setter def values(self, values: np.ndarray | None): if not isinstance(values, (np.ndarray, type(None))): raise TypeError( f"Input 'values' for {self} must be of type {np.ndarray} or None." ) self._values = self.format_values(values) self.workspace.update_attribute(self, "values")
[docs] def format_length(self, values: np.ndarray) -> np.ndarray: """ Check for possible mismatch between the length of values :param values: the values to check. :return: the values with the right length. """ if self.n_values is None: return values if len(values) < self.n_values: full_vector = np.ones(self.n_values, dtype=values.dtype) * self.nan_value 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
[docs] def format_values(self, values: np.ndarray | None) -> np.ndarray: """ Check for possible mismatch between the length of values stored and the expected number of cells or vertices. :param values: Array of values to check :returns: values: An array of float values of length n_values or None """ if values is None: return values if not isinstance(values, np.ndarray): raise TypeError("Input 'values' must be a numpy array.") if values.ndim > 1: values = np.ravel(values) warn("Input 'values' converted to a 1D array.") # change nan values to nan_value values[np.isnan(values)] = self.nan_value # check the length of the values values = self.format_length(values) # check the value type values = self.format_type(values) return values