feat!: Update NPResult to contain the posterior and predictions (#224)

* Add method to write Theta with names and probs

* Add Posterior to NPResult

* Add predictions to NPResult

* Update predictions.rs

* Implement suggestions

* chore: Clean up some comments

* feat: some methods needed in hermes (#226)

* some methods needed in hermes


---------

Co-authored-by: Julian Otalvaro <juliandavid347@gmail.com>

Author: mhovd

Cargo.toml

@@ -29,7 +29,7 @@ tracing-subscriber = { version = "0.3.19", features = [
 ] }
 faer = "0.23.1"
 faer-ext = { version = "0.7.1", features = ["nalgebra", "ndarray"] }
-pharmsol = "=0.20.0"
+pharmsol = "=0.21.0"
 rand = "0.9.0"
 anyhow = "1.0.100"
 rayon = "1.10.0"

examples/bimodal_ke/main.rs

@@ -48,7 +48,7 @@ fn main() -> Result<()> {
     settings.initialize_logs()?;
     let data = data::read_pmetrics("examples/bimodal_ke/bimodal_ke.csv")?;
     let mut algorithm = dispatch_algorithm(settings, eq, data)?;
-    let result = algorithm.fit()?;
+    let mut result = algorithm.fit()?;
     result.write_outputs()?;
 
     Ok(())

examples/drusano/main.rs

@@ -137,7 +137,7 @@ fn main() -> Result<()> {
     algorithm.initialize().unwrap();
     algorithm.fit().unwrap();
     // while !algorithm.next_cycle().unwrap() {}
-    let result = algorithm.into_npresult();
+    let mut result = algorithm.into_npresult()?;
     result.write_outputs().unwrap();
     Ok(())
 }

examples/iov/main.rs

@@ -52,7 +52,7 @@ fn main() -> Result<()> {
     let data = data::read_pmetrics("examples/iov/test.csv").unwrap();
     let mut algorithm = dispatch_algorithm(settings, sde, data).unwrap();
     algorithm.initialize().unwrap();
-    let result = algorithm.fit().unwrap();
+    let mut result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 
     Ok(())

examples/meta/main.rs

@@ -64,6 +64,6 @@ fn main() {
     let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
     // let result = algorithm.fit().unwrap();
     algorithm.initialize().unwrap();
-    let result = algorithm.fit().unwrap();
+    let mut result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 }

examples/neely/main.rs

@@ -96,6 +96,6 @@ fn main() {
     settings.initialize_logs().unwrap();
     let data = data::read_pmetrics("examples/neely/data.csv").unwrap();
     let mut algorithm = dispatch_algorithm(settings, ode, data).unwrap();
-    let result = algorithm.fit().unwrap();
+    let mut result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 }

examples/new_iov/main.rs

@@ -55,6 +55,6 @@ fn main() {
     let data = data::read_pmetrics("examples/new_iov/data.csv").unwrap();
     let mut algorithm = dispatch_algorithm(settings, sde, data).unwrap();
     algorithm.initialize().unwrap();
-    let result = algorithm.fit().unwrap();
+    let mut result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 }

examples/theophylline/main.rs

@@ -53,6 +53,6 @@ fn main() {
     let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
     // let result = algorithm.fit().unwrap();
     algorithm.initialize().unwrap();
-    let result = algorithm.fit().unwrap();
+    let mut result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 }

examples/two_eq_lag/main.rs

@@ -89,7 +89,7 @@ fn main() {
     settings.initialize_logs().unwrap();
     let data = data::read_pmetrics("examples/two_eq_lag/two_eq_lag.csv").unwrap();
     let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
-    let result = algorithm.fit().unwrap();
+    let mut result = algorithm.fit().unwrap();
     // algorithm.initialize().unwrap();
     // while !algorithm.next_cycle().unwrap() {}
     // let result = algorithm.into_npresult();

examples/vanco_sde/main.rs

@@ -78,6 +78,6 @@ fn main() {
 
     let mut algorithm = dispatch_algorithm(settings, sde, data).unwrap();
     algorithm.initialize().unwrap();
-    let result = algorithm.fit().unwrap();
+    let mut result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 }