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Merged
g-bauer merged 13 commits into from
Oct 18, 2022
Merged

Add Sync + Send to EquationOfState trait bounds #57

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e44f4b8
add sync + send to EquationOfState trait bounds, change Rc to Arc
g-bauer Sep 27, 2022
47d1a7f
Made State sync + send, added parallel execution to pure PhaseDiagram…
g-bauer Sep 28, 2022
0f67fe6
Added parallelization for estimator
g-bauer Sep 30, 2022
60356ab
Removed splitting in chunks from estimator. Added impls for all datas…
g-bauer Oct 3, 2022
118eef4
Updated models
g-bauer Oct 3, 2022
10d07c2
make all DFT struct Sync + Send and remove unsendable attribute
prehner Oct 4, 2022
498a6e9
more changes, added rayon feature
g-bauer Oct 10, 2022
5ccff9c
Removed parallel impls in estimator
g-bauer Oct 17, 2022
51715e2
Remove unused impls; updated Python interface
g-bauer Oct 17, 2022
8541909
Remove surface tension test from estimator
g-bauer Oct 17, 2022
9c4b65e
fixed feature cfg
g-bauer Oct 17, 2022
4b317d0
remove function for global thread pool
g-bauer Oct 17, 2022
752ab45
added rayon error as variant of EoSError, changed Cargo.toml feature …
g-bauer Oct 18, 2022
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Removed parallel impls in estimator
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@g-bauer
g-bauer committed Oct 17, 2022
commit 5ccff9ca8d0087b56fff8bd69446b9271338735b
2 changes: 1 addition & 1 deletion feos-core/src/lib.rs
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Expand Up @@ -74,7 +74,7 @@ pub fn initialize_global_thread_pool_py(num_threads: usize) -> pyo3::PyResult<()
}

/// Consistent conversions between quantities and reduced properties.
pub trait EosUnit: Unit + Send {
pub trait EosUnit: Unit + Send + Sync {
fn reference_temperature() -> QuantityScalar<Self>;
fn reference_length() -> QuantityScalar<Self>;
fn reference_density() -> QuantityScalar<Self>;
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2 changes: 1 addition & 1 deletion feos-dft/src/convolver/mod.rs
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Expand Up @@ -19,7 +19,7 @@ use transform::*;
/// Helmholtz energy functional.
///
/// Parametrized over data types `T` and dimension of the problem `D`.
pub trait Convolver<T, D: Dimension>: Send {
pub trait Convolver<T, D: Dimension>: Send + Sync {
/// Convolve the profile with the given weight function.
fn convolve(&self, profile: Array<T, D>, weight_function: &WeightFunction<T>) -> Array<T, D>;

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2 changes: 1 addition & 1 deletion feos-dft/src/convolver/transform.rs
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Expand Up @@ -25,7 +25,7 @@ impl SinCosTransform {
}
}

pub(super) trait FourierTransform<T: DualNum<f64>>: Send {
pub(super) trait FourierTransform<T: DualNum<f64>>: Send + Sync {
fn forward_transform(&self, f_r: ArrayView1<T>, f_k: ArrayViewMut1<T>, scalar: bool);

fn back_transform(&self, f_k: ArrayViewMut1<T>, f_r: ArrayViewMut1<T>, scalar: bool);
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2 changes: 1 addition & 1 deletion feos-dft/src/functional.rs
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Expand Up @@ -132,7 +132,7 @@ pub enum MoleculeShape<'a> {
}

/// A general Helmholtz energy functional.
pub trait HelmholtzEnergyFunctional: Sized + Send {
pub trait HelmholtzEnergyFunctional: Sized + Send + Sync {
/// Return a slice of [FunctionalContribution]s.
fn contributions(&self) -> &[Box<dyn FunctionalContribution>];

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2 changes: 1 addition & 1 deletion feos-dft/src/profile.rs
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Expand Up @@ -24,7 +24,7 @@ pub(crate) const CUTOFF_RADIUS: f64 = 14.0;
/// In the most basic case, the chemical potential is specified in a DFT calculation,
/// for more general systems, this trait provides the possibility to declare additional
/// equations for the calculation of the chemical potential during the iteration.
pub trait DFTSpecification<U, D: Dimension, F>: Send {
pub trait DFTSpecification<U, D: Dimension, F>: Send + Sync {
fn calculate_chemical_potential(
&self,
profile: &DFTProfile<U, D, F>,
Expand Down
31 changes: 1 addition & 30 deletions src/estimator/dataset.rs
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Expand Up @@ -15,7 +15,7 @@ use std::sync::Arc;
///
/// Functionalities in the context of optimizations of
/// parameters of equations of state.
pub trait DataSet<U: EosUnit, E: EquationOfState>: Send {
pub trait DataSet<U: EosUnit, E: EquationOfState>: Send + Sync {
/// Return target quantity.
fn target(&self) -> &QuantityArray1<U>;

Expand Down Expand Up @@ -71,35 +71,6 @@ pub trait DataSet<U: EosUnit, E: EquationOfState>: Send {
.enumerate()
.fold(0.0, |mean, (i, x)| mean + (x.abs() - mean) / (i + 1) as f64))
}

// /// Parallel evaluation of the equation of state for the target quantity for each data point.
// fn par_predict(&self, eos: &Arc<E>) -> Result<QuantityArray1<U>, EstimatorError>
// where
// QuantityScalar<U>: std::fmt::Display + std::fmt::LowerExp,
// {
// self.predict(eos)
// }

// /// Parallel evaluation the cost function.
// fn par_cost(&self, eos: &Arc<E>, loss: Loss) -> Result<Array1<f64>, EstimatorError>
// where
// QuantityScalar<U>: std::fmt::Display + std::fmt::LowerExp,
// {
// let mut cost = self.par_relative_difference(eos)?;
// loss.apply(&mut cost);
// let datapoints = cost.len();
// Ok(cost / datapoints as f64)
// }

// /// Returns the relative difference between the equation of state and the experimental values.
// fn par_relative_difference(&self, eos: &Arc<E>) -> Result<Array1<f64>, EstimatorError>
// where
// QuantityScalar<U>: std::fmt::Display + std::fmt::LowerExp,
// {
// let prediction = &self.par_predict(eos)?;
// let target = self.target();
// Ok(((prediction - target) / target).into_value()?)
// }
}

impl<U: EosUnit, E: EquationOfState> fmt::Display for dyn DataSet<U, E> {
Expand Down
10 changes: 3 additions & 7 deletions src/estimator/diffusion.rs
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Expand Up @@ -64,13 +64,9 @@ impl<U: EosUnit, E: EquationOfState + EntropyScaling<U>> DataSet<U, E> for Diffu
.unwrap();
let ps = self.pressure.to_reduced(U::reference_pressure()).unwrap();

#[cfg(not(feature = "rayon"))]
let tp_iter = ts.iter().zip(ps.iter());
#[cfg(feature = "rayon")]
let tp_iter = (ts.as_slice().unwrap(), ps.as_slice().unwrap())
.into_par_iter();

let res = tp_iter
let res = ts
.iter()
.zip(ps.iter())
.map(|(&t, &p)| {
State::new_npt(
eos,
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27 changes: 0 additions & 27 deletions src/estimator/estimator.rs
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Expand Up @@ -57,27 +57,11 @@ where
Ok(concatenate(Axis(0), &aview)?)
}

// pub fn par_cost(&self, eos: &Arc<E>) -> Result<Array1<f64>, EstimatorError> {
// let w = arr1(&self.weights) / self.weights.iter().sum::<f64>();
// let predictions = self
// .data
// .iter()
// .enumerate()
// .flat_map(|(i, d)| d.par_cost(eos, self.losses[i]).unwrap() * w[i])
// .collect::<Array1<f64>>();
// Ok(predictions)
// }

/// Returns the properties as computed by the equation of state for each `DataSet`.
pub fn predict(&self, eos: &Arc<E>) -> Result<Vec<QuantityArray1<U>>, EstimatorError> {
self.data.iter().map(|d| d.predict(eos)).collect()
}

// /// Returns the properties as computed by the equation of state for each `DataSet`.
// pub fn par_predict(&self, eos: &Arc<E>) -> Result<Vec<QuantityArray1<U>>, EstimatorError> {
// self.data.iter().map(|d| d.par_predict(eos)).collect()
// }

/// Returns the relative difference for each `DataSet`.
pub fn relative_difference(&self, eos: &Arc<E>) -> Result<Vec<Array1<f64>>, EstimatorError> {
self.data
Expand All @@ -86,17 +70,6 @@ where
.collect()
}

// /// Returns the relative difference for each `DataSet`.
// pub fn par_relative_difference(
// &self,
// eos: &Arc<E>,
// ) -> Result<Vec<Array1<f64>>, EstimatorError> {
// self.data
// .iter()
// .map(|d| d.par_relative_difference(eos))
// .collect()
// }

/// Returns the mean absolute relative difference for each `DataSet`.
pub fn mean_absolute_relative_difference(
&self,
Expand Down
63 changes: 1 addition & 62 deletions src/estimator/liquid_density.rs
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Expand Up @@ -3,7 +3,7 @@ use feos_core::{
DensityInitialization, EosUnit, EquationOfState, MolarWeight, PhaseEquilibrium, SolverOptions,
State,
};
use ndarray::{arr1, Array1};
use ndarray::arr1;
use quantity::{QuantityArray1, QuantityScalar};
#[cfg(feature = "rayon")]
use rayon_::prelude::*;
Expand Down Expand Up @@ -76,36 +76,6 @@ impl<U: EosUnit, E: EquationOfState + MolarWeight<U>> DataSet<U, E> for LiquidDe
.collect())
}

// fn par_predict(&self, eos: &Arc<E>) -> Result<QuantityArray1<U>, EstimatorError> {
// let moles = arr1(&[1.0]) * U::reference_moles();
// let ts = self
// .temperature
// .to_reduced(U::reference_temperature())
// .unwrap();
// let ps = self.pressure.to_reduced(U::reference_pressure()).unwrap();

// let res = (ts.as_slice().unwrap(), ps.as_slice().unwrap())
// .into_par_iter()
// .map(|(&t, &p)| {
// let state = State::new_npt(
// eos,
// t * U::reference_temperature(),
// p * U::reference_pressure(),
// &moles,
// DensityInitialization::Liquid,
// );
// if let Ok(s) = state {
// s.mass_density()
// .to_reduced(U::reference_mass() / U::reference_volume())
// .unwrap()
// } else {
// f64::NAN
// }
// })
// .collect::<Vec<f64>>();
// Ok(Array1::from_vec(res) * U::reference_mass() / U::reference_volume())
// }

fn get_input(&self) -> HashMap<String, QuantityArray1<U>> {
let mut m = HashMap::with_capacity(2);
m.insert("temperature".to_owned(), self.temperature());
Expand Down Expand Up @@ -174,37 +144,6 @@ impl<U: EosUnit, E: EquationOfState + MolarWeight<U>> DataSet<U, E>
.collect())
}

// fn par_predict(&self, eos: &Arc<E>) -> Result<QuantityArray1<U>, EstimatorError>
// where
// QuantityScalar<U>: std::fmt::Display + std::fmt::LowerExp,
// {
// let ts = self
// .temperature
// .to_reduced(U::reference_temperature())
// .unwrap();

// let res = ts
// .into_par_iter()
// .map(|&t| {
// if let Ok(state) = PhaseEquilibrium::pure(
// eos,
// t * U::reference_temperature(),
// None,
// self.solver_options,
// ) {
// state
// .liquid()
// .mass_density()
// .to_reduced(U::reference_mass() / U::reference_volume())
// .unwrap()
// } else {
// f64::NAN
// }
// })
// .collect::<Vec<f64>>();
// Ok(Array1::from_vec(res) * U::reference_mass() / U::reference_volume())
// }

fn get_input(&self) -> HashMap<String, QuantityArray1<U>> {
let mut m = HashMap::with_capacity(2);
m.insert("temperature".to_owned(), self.temperature());
Expand Down
73 changes: 0 additions & 73 deletions src/estimator/python.rs
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Expand Up @@ -504,29 +504,6 @@ macro_rules! impl_estimator {
Ok(self.0.cost(&eos.0)?.view().to_pyarray(py))
}

// /// Compute the cost function for each ``DataSet`` in parallel.
// ///
// /// The cost function is:
// /// - The relative difference between prediction and target value,
// /// - to which a loss function is applied,
// /// - and which is weighted according to the number of datapoints,
// /// - and the relative weights as defined in the Estimator object.
// ///
// /// Parameters
// /// ----------
// /// eos : PyEos
// /// The equation of state that is used.
// ///
// /// Returns
// /// -------
// /// numpy.ndarray[Float]
// /// The cost function evaluated for each experimental data point
// /// of each ``DataSet``.
// #[pyo3(text_signature = "($self, eos)")]
// fn par_cost<'py>(&self, eos: &$py_eos, py: Python<'py>) -> PyResult<&'py PyArray1<f64>> {
// Ok(self.0.par_cost(&eos.0)?.view().to_pyarray(py))
// }

/// Return the properties as computed by the
/// equation of state for each `DataSet`.
///
Expand All @@ -548,27 +525,6 @@ macro_rules! impl_estimator {
.collect())
}

// /// Return the properties as computed by the
// /// equation of state for each `DataSet` in parallel.
// ///
// /// Parameters
// /// ----------
// /// eos : PyEos
// /// The equation of state that is used.
// ///
// /// Returns
// /// -------
// /// List[SIArray1]
// #[pyo3(text_signature = "($self, eos)")]
// fn par_predict(&self, eos: &$py_eos) -> PyResult<Vec<PySIArray1>> {
// Ok(self
// .0
// .par_predict(&eos.0)?
// .iter()
// .map(|d| PySIArray1::from(d.clone()))
// .collect())
// }

/// Return the relative difference between experimental data
/// and prediction of the equation of state for each ``DataSet``.
///
Expand Down Expand Up @@ -598,35 +554,6 @@ macro_rules! impl_estimator {
.collect())
}

// /// Return the relative difference between experimental data
// /// and prediction of the equation of state for each ``DataSet``.
// ///
// /// The relative difference is computed as:
// ///
// /// .. math:: \text{Relative Difference} = \frac{x_i^\text{prediction} - x_i^\text{experiment}}{x_i^\text{experiment}}
// ///
// /// Parameters
// /// ----------
// /// eos : PyEos
// /// The equation of state that is used.
// ///
// /// Returns
// /// -------
// /// List[numpy.ndarray[Float]]
// #[pyo3(text_signature = "($self, eos)")]
// fn par_relative_difference<'py>(
// &self,
// eos: &$py_eos,
// py: Python<'py>,
// ) -> PyResult<Vec<&'py PyArray1<f64>>> {
// Ok(self
// .0
// .par_relative_difference(&eos.0)?
// .iter()
// .map(|d| d.view().to_pyarray(py))
// .collect())
// }

/// Return the mean absolute relative difference for each ``DataSet``.
///
/// The mean absolute relative difference is computed as:
Expand Down
9 changes: 3 additions & 6 deletions src/estimator/thermal_conductivity.rs
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Expand Up @@ -68,12 +68,9 @@ impl<U: EosUnit, E: EquationOfState + EntropyScaling<U>> DataSet<U, E> for Therm
/ U::reference_temperature()
/ U::reference_length();

#[cfg(not(feature = "rayon"))]
let tp_iter = ts.iter().zip(ps.iter());
#[cfg(feature = "rayon")]
let tp_iter = (ts.as_slice().unwrap(), ps.as_slice().unwrap()).into_par_iter();

let res = tp_iter
let res = ts
.iter()
.zip(ps.iter())
.map(|(&t, &p)| {
State::new_npt(
eos,
Expand Down
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