conspire/math/tensor/rank_2/vec/
mod.rs

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#[cfg(test)]
use super::super::test::ErrorTensor;

use crate::math::{Tensor, TensorArray, TensorRank0, TensorRank2, TensorVec};
use std::{
    fmt::{Display, Formatter, Result},
    ops::{Add, AddAssign, Div, DivAssign, Index, IndexMut, Mul, MulAssign, Sub, SubAssign},
};

/// A vector of *d*-dimensional tensors of rank 2.
///
/// `D` is the dimension, `I`, `J` are the configurations.
#[derive(Clone, Debug)]
pub struct TensorRank2Vec<const D: usize, const I: usize, const J: usize>(
    Vec<TensorRank2<D, I, J>>,
);

impl<const D: usize, const I: usize, const J: usize> Display for TensorRank2Vec<D, I, J> {
    fn fmt(&self, _f: &mut Formatter) -> Result {
        Ok(())
    }
}

#[cfg(test)]
impl<const D: usize, const I: usize, const J: usize> ErrorTensor for TensorRank2Vec<D, I, J> {
    fn error(
        &self,
        comparator: &Self,
        tol_abs: &TensorRank0,
        tol_rel: &TensorRank0,
    ) -> Option<usize> {
        let error_count = self
            .iter()
            .zip(comparator.iter())
            .map(|(self_a, comparator_a)| {
                self_a
                    .iter()
                    .zip(comparator_a.iter())
                    .map(|(self_a_i, comparator_a_i)| {
                        self_a_i
                            .iter()
                            .zip(comparator_a_i.iter())
                            .filter(|(&self_a_ij, &comparator_a_ij)| {
                                &(self_a_ij - comparator_a_ij).abs() >= tol_abs
                                    && &(self_a_ij / comparator_a_ij - 1.0).abs() >= tol_rel
                            })
                            .count()
                    })
                    .sum::<usize>()
            })
            .sum();
        if error_count > 0 {
            Some(error_count)
        } else {
            None
        }
    }
    fn error_fd(&self, comparator: &Self, epsilon: &TensorRank0) -> Option<(bool, usize)> {
        let error_count = self
            .iter()
            .zip(comparator.iter())
            .map(|(self_a, comparator_a)| {
                self_a
                    .iter()
                    .zip(comparator_a.iter())
                    .map(|(self_a_i, comparator_a_i)| {
                        self_a_i
                            .iter()
                            .zip(comparator_a_i.iter())
                            .filter(|(&self_a_ij, &comparator_a_ij)| {
                                &(self_a_ij / comparator_a_ij - 1.0).abs() >= epsilon
                                    && (&self_a_ij.abs() >= epsilon
                                        || &comparator_a_ij.abs() >= epsilon)
                            })
                            .count()
                    })
                    .sum::<usize>()
            })
            .sum();
        if error_count > 0 {
            Some((true, error_count))
        } else {
            None
        }
    }
}

impl<const D: usize, const I: usize, const J: usize> FromIterator<TensorRank2<D, I, J>>
    for TensorRank2Vec<D, I, J>
{
    fn from_iter<Ii: IntoIterator<Item = TensorRank2<D, I, J>>>(into_iterator: Ii) -> Self {
        Self(Vec::from_iter(into_iterator))
    }
}

impl<const D: usize, const I: usize, const J: usize> Index<usize> for TensorRank2Vec<D, I, J> {
    type Output = TensorRank2<D, I, J>;
    fn index(&self, index: usize) -> &Self::Output {
        &self.0[index]
    }
}

impl<const D: usize, const I: usize, const J: usize> IndexMut<usize> for TensorRank2Vec<D, I, J> {
    fn index_mut(&mut self, index: usize) -> &mut Self::Output {
        &mut self.0[index]
    }
}

impl<const D: usize, const I: usize, const J: usize> TensorVec for TensorRank2Vec<D, I, J> {
    type Item = TensorRank2<D, I, J>;
    type Slice<'a> = &'a [[[TensorRank0; D]; D]];
    fn append(&mut self, other: &mut Self) {
        self.0.append(&mut other.0)
    }
    fn is_empty(&self) -> bool {
        self.0.is_empty()
    }
    fn len(&self) -> usize {
        self.0.len()
    }
    fn new(slice: Self::Slice<'_>) -> Self {
        slice
            .iter()
            .map(|slice_entry| Self::Item::new(*slice_entry))
            .collect()
    }
    fn push(&mut self, item: Self::Item) {
        self.0.push(item)
    }
    fn zero(len: usize) -> Self {
        (0..len).map(|_| Self::Item::zero()).collect()
    }
}

impl<const D: usize, const I: usize, const J: usize> Tensor for TensorRank2Vec<D, I, J> {
    type Item = TensorRank2<D, I, J>;
    fn iter(&self) -> impl Iterator<Item = &Self::Item> {
        self.0.iter()
    }
    fn iter_mut(&mut self) -> impl Iterator<Item = &mut Self::Item> {
        self.0.iter_mut()
    }
}

impl<const D: usize, const I: usize, const J: usize> Add for TensorRank2Vec<D, I, J> {
    type Output = Self;
    fn add(mut self, tensor_rank_2_vec: Self) -> Self::Output {
        self += tensor_rank_2_vec;
        self
    }
}

impl<const D: usize, const I: usize, const J: usize> Add<&Self> for TensorRank2Vec<D, I, J> {
    type Output = Self;
    fn add(mut self, tensor_rank_2_vec: &Self) -> Self::Output {
        self += tensor_rank_2_vec;
        self
    }
}

impl<const D: usize, const I: usize, const J: usize> AddAssign for TensorRank2Vec<D, I, J> {
    fn add_assign(&mut self, tensor_rank_2_vec: Self) {
        self.iter_mut()
            .zip(tensor_rank_2_vec.iter())
            .for_each(|(self_entry, tensor_rank_2)| *self_entry += tensor_rank_2);
    }
}

impl<const D: usize, const I: usize, const J: usize> AddAssign<&Self> for TensorRank2Vec<D, I, J> {
    fn add_assign(&mut self, tensor_rank_2_vec: &Self) {
        self.iter_mut()
            .zip(tensor_rank_2_vec.iter())
            .for_each(|(self_entry, tensor_rank_2)| *self_entry += tensor_rank_2);
    }
}

impl<const D: usize, const I: usize, const J: usize> Div<TensorRank0> for TensorRank2Vec<D, I, J> {
    type Output = Self;
    fn div(mut self, tensor_rank_0: TensorRank0) -> Self::Output {
        self /= &tensor_rank_0;
        self
    }
}

impl<const D: usize, const I: usize, const J: usize> Div<&TensorRank0> for TensorRank2Vec<D, I, J> {
    type Output = Self;
    fn div(mut self, tensor_rank_0: &TensorRank0) -> Self::Output {
        self /= tensor_rank_0;
        self
    }
}

impl<const D: usize, const I: usize, const J: usize> DivAssign<TensorRank0>
    for TensorRank2Vec<D, I, J>
{
    fn div_assign(&mut self, tensor_rank_0: TensorRank0) {
        self.iter_mut().for_each(|entry| *entry /= &tensor_rank_0);
    }
}

impl<const D: usize, const I: usize, const J: usize> DivAssign<&TensorRank0>
    for TensorRank2Vec<D, I, J>
{
    fn div_assign(&mut self, tensor_rank_0: &TensorRank0) {
        self.iter_mut().for_each(|entry| *entry /= tensor_rank_0);
    }
}

impl<const D: usize, const I: usize, const J: usize> Mul<TensorRank0> for TensorRank2Vec<D, I, J> {
    type Output = Self;
    fn mul(mut self, tensor_rank_0: TensorRank0) -> Self::Output {
        self *= &tensor_rank_0;
        self
    }
}

impl<const D: usize, const I: usize, const J: usize> Mul<&TensorRank0> for TensorRank2Vec<D, I, J> {
    type Output = Self;
    fn mul(mut self, tensor_rank_0: &TensorRank0) -> Self::Output {
        self *= tensor_rank_0;
        self
    }
}

impl<const D: usize, const I: usize, const J: usize> MulAssign<TensorRank0>
    for TensorRank2Vec<D, I, J>
{
    fn mul_assign(&mut self, tensor_rank_0: TensorRank0) {
        self.iter_mut().for_each(|entry| *entry *= &tensor_rank_0);
    }
}

impl<const D: usize, const I: usize, const J: usize> MulAssign<&TensorRank0>
    for TensorRank2Vec<D, I, J>
{
    fn mul_assign(&mut self, tensor_rank_0: &TensorRank0) {
        self.iter_mut().for_each(|entry| *entry *= tensor_rank_0);
    }
}

impl<const D: usize, const I: usize, const J: usize> Sub for TensorRank2Vec<D, I, J> {
    type Output = Self;
    fn sub(mut self, tensor_rank_2_vec: Self) -> Self::Output {
        self -= tensor_rank_2_vec;
        self
    }
}

impl<const D: usize, const I: usize, const J: usize> Sub<&Self> for TensorRank2Vec<D, I, J> {
    type Output = Self;
    fn sub(mut self, tensor_rank_2_vec: &Self) -> Self::Output {
        self -= tensor_rank_2_vec;
        self
    }
}

impl<const D: usize, const I: usize, const J: usize> SubAssign for TensorRank2Vec<D, I, J> {
    fn sub_assign(&mut self, tensor_rank_2_vec: Self) {
        self.iter_mut()
            .zip(tensor_rank_2_vec.iter())
            .for_each(|(self_entry, tensor_rank_2)| *self_entry -= tensor_rank_2);
    }
}

impl<const D: usize, const I: usize, const J: usize> SubAssign<&Self> for TensorRank2Vec<D, I, J> {
    fn sub_assign(&mut self, tensor_rank_2_vec: &Self) {
        self.iter_mut()
            .zip(tensor_rank_2_vec.iter())
            .for_each(|(self_entry, tensor_rank_2)| *self_entry -= tensor_rank_2);
    }
}