Merge pull request #61 from LAPKB/net

Net

.gitignore

@@ -27,3 +27,4 @@ settings.json
 /benches/*.json
 log.txt
 op.csv
+*results.txt

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "pmcore"
-version = "0.6.0"
+version = "0.7.0"
 edition = "2021"
 authors = [
     "Julián D. Otálvaro <juliandavid347@gmail.com>",
@@ -23,21 +23,23 @@ serde_json = "1.0.66"
 sobol_burley = "0.5.0"
 ndarray-stats = "0.5.1"
 linfa-linalg = "0.1.0"
-ratatui = { version = "0.28.0", features = ["crossterm"] }
 tokio = { version = "1.32.0", features = ["sync", "rt"] }
 argmin = { version = "0.10.0", features = [] }
 argmin-math = { version = "0.4.0", features = ["ndarray_v0_15-nolinalg"] }
 tracing = "0.1.40"
 tracing-subscriber = { version = "0.3.17", features = ["env-filter", "fmt", "time"] }
 chrono = "0.4"
 config = {version = "0.14", features=["preserve_order"]}
-faer = "0.19.0"
+faer = "0.19.3"
 faer-ext = {version = "0.2.0", features = ["nalgebra", "ndarray"]}
-pharmsol = "0.6.0"
+pharmsol = "0.7.0"
 # pharmsol = {path = "../pharmsol"}
+# pharmsol = {git ="https://github.com/LAPKB/pharmsol", branch="net"}
+# pharmsol = {git ="https://github.com/LAPKB/pharmsol", branch="main"}
 toml = "0.8.14" #REMOVE
 rand = "0.8.5"
 anyhow = "1.0.86"
+nalgebra = "0.33.0"
 
 
 [profile.release]

README.md

@@ -16,7 +16,7 @@ Rust library with the building blocks to create and implement new non-parametric
 * Powerful simulation engine 
 * Informative Terminal User Interface (TUI)
 
-## Available algoritms
+## Available algorithms
 
 This project aims to implement several algorithms for non-parametric population pharmacokinetic modelling.
 

benches/compare.rs

@@ -1,7 +1,7 @@
 use pmcore::prelude::*;
 
 use diol::prelude::*;
-use settings::*;
+use settings::{Log, *};
 use toml::Table;
 
 fn main() -> std::io::Result<()> {
@@ -26,11 +26,12 @@ pub fn analytical_bke(bencher: Bencher, len: usize) {
     );
     let settings = bke_settings();
     let data = data::read_pmetrics("examples/bimodal_ke/bimodal_ke.csv").unwrap();
+    let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
     bencher.bench(|| {
         for _ in 0..len {
-            let result = black_box(fit(eq.clone(), data.clone(), settings.clone()).unwrap());
-            assert!(result.cycles == 96);
-            assert!(result.objf == -344.64028277953844);
+            let result = black_box(algorithm.fit().unwrap());
+            assert!(result.cycles() == 96);
+            assert!(result.objf() == -344.64028277953844);
         }
     });
 }
@@ -53,11 +54,12 @@ pub fn ode_bke(bencher: Bencher, len: usize) {
     );
     let settings = bke_settings();
     let data = data::read_pmetrics("examples/bimodal_ke/bimodal_ke.csv").unwrap();
+    let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
     bencher.bench(|| {
         for _ in 0..len {
-            let result = black_box(fit(eq.clone(), data.clone(), settings.clone()).unwrap());
-            assert!(result.cycles == 104);
-            assert!(result.objf == -348.69505647385495);
+            let result = black_box(algorithm.fit().unwrap());
+            assert!(result.cycles() == 104);
+            assert!(result.objf() == -348.69505647385495);
         }
     });
 }
@@ -80,11 +82,12 @@ fn analytical_tel(bencher: Bencher, len: usize) {
     );
     let settings = tel_settings();
     let data = data::read_pmetrics("examples/two_eq_lag/two_eq_lag.csv").unwrap();
+    let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
     bencher.bench(|| {
         for _ in 0..len {
-            let result = black_box(fit(eq.clone(), data.clone(), settings.clone()).unwrap());
-            assert!(result.cycles == 686);
-            assert!(result.objf == 432.95499351489167);
+            let result = black_box(algorithm.fit().unwrap());
+            assert!(result.cycles() == 686);
+            assert!(result.objf() == 432.95499351489167);
         }
     });
 }
@@ -111,11 +114,12 @@ fn ode_tel(bencher: Bencher, len: usize) {
     );
     let settings = tel_settings();
     let data = data::read_pmetrics("examples/two_eq_lag/two_eq_lag.csv").unwrap();
+    let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
     bencher.bench(|| {
         for _ in 0..len {
-            let result = black_box(fit(eq.clone(), data.clone(), settings.clone()).unwrap());
-            assert!(result.cycles == 707);
-            assert!(result.objf == 432.9542531584738);
+            let result = black_box(algorithm.fit().unwrap());
+            assert!(result.cycles() == 707);
+            assert!(result.objf() == 432.9542531584738);
         }
     });
 }
@@ -125,7 +129,6 @@ fn tel_settings() -> Settings {
         config: Config {
             cycles: 1000,
             algorithm: "NPAG".to_string(),
-            tui: false,
             cache: true,
             ..Default::default()
         },
@@ -195,7 +198,6 @@ fn bke_settings() -> Settings {
         config: Config {
             cycles: 1024,
             algorithm: "NPAG".to_string(),
-            tui: false,
             cache: true,
             include: None,
             exclude: None,

compare.sh

@@ -0,0 +1,82 @@
+#!/bin/bash
+
+# Check if enough arguments are supplied
+if [ "$#" -ne 4 ]; then
+    echo "Usage: ./time_compare.sh <example_name> <N> <commit-hash-1> <commit-hash-2>"
+    exit 1
+fi
+
+# Input arguments
+EXAMPLE_NAME=$1
+N=$2
+COMMIT1=$3
+COMMIT2=$4
+
+# Check if bc is installed
+if ! command -v bc &> /dev/null
+then
+    echo "'bc' is required but it's not installed. Please install it using your package manager."
+    exit 1
+fi
+
+# Output file based on example name
+OUTPUT_FILE="${EXAMPLE_NAME}_time_comparison_results.txt"
+echo "Time comparison results" > $OUTPUT_FILE
+echo "Example: $EXAMPLE_NAME" >> $OUTPUT_FILE
+echo "===================================" >> $OUTPUT_FILE
+
+# Function to run the example N times and log the average time
+function time_commit() {
+    COMMIT=$1
+    EXAMPLE_NAME=$2
+    N=$3
+    TOTAL_TIME=0
+
+    echo "Checking out commit $COMMIT"
+    git checkout $COMMIT 
+
+    # Running the example N times
+    echo "Running $EXAMPLE_NAME in release mode for $N times"
+
+    # # Makes sure the target directory is clean
+    # cargo clean -q
+
+     #compile the example
+    echo "Compilating $COMMIT..."
+    cargo build --release -q --example $EXAMPLE_NAME    
+    echo "Done compilating $COMMIT..."
+    for i in $(seq 1 $N); do
+
+
+        echo "Run $i: executing 'cargo run' for commit $COMMIT..."
+
+        # Capture both stdout and stderr to handle errors from cargo run
+        RUN_TIME=$( { /usr/bin/time -f "%e" cargo run --release --example $EXAMPLE_NAME 2>&1 | grep -Eo '^[0-9]+\.[0-9]+$'; } 2>&1 )
+
+        # Validate that RUN_TIME is a valid float
+        if [[ $RUN_TIME =~ ^[0-9]+(\.[0-9]+)?$ ]]; then
+            echo "Run $i: ${RUN_TIME}s"
+            TOTAL_TIME=$(echo "$TOTAL_TIME + $RUN_TIME" | bc)
+        else
+            echo "Error: Unable to capture timing for run $i. Got: $RUN_TIME"
+            exit 1
+        fi
+    done
+
+    # Calculate average time
+    AVERAGE_TIME=$(echo "scale=3; $TOTAL_TIME / $N" | bc)
+    echo "Average time for commit $COMMIT: ${AVERAGE_TIME}s" >> $OUTPUT_FILE
+    echo "-----------------------------------" >> $OUTPUT_FILE
+}
+
+# Time the first commit
+time_commit $COMMIT1 $EXAMPLE_NAME $N
+
+# Time the second commit
+time_commit $COMMIT2 $EXAMPLE_NAME $N
+
+# Switch back to the current branch
+git checkout - 
+
+# Output completion message
+echo "Timing complete. Results saved to $OUTPUT_FILE."

examples/bimodal_ke/config.toml

@@ -1,7 +1,6 @@
 [config]
 cycles = 1024
 algorithm = "NPAG"
-tui = false
 cache = true
 
 [random]
@@ -18,8 +17,8 @@ level = "info"
 
 [prior]
 sampler = "sobol"
-file = "examples/bimodal_ke/prior.csv"
+# file = "examples/bimodal_ke/prior.csv"
 
 [output]
 write = true
-path = "examples/bimodal_ke/output"
+path = "examples/bimodal_ke/output"

examples/bimodal_ke/main.rs

@@ -1,5 +1,5 @@
+use logger::setup_log;
 use pmcore::prelude::*;
-
 fn main() {
     let eq = equation::ODE::new(
         |x, p, _t, dx, rateiv, _cov| {
@@ -28,8 +28,26 @@ fn main() {
     //     },
     //     (1, 1),
     // );
+    // let eq = equation::ODENet::new(
+    //     vec![dmatrix![-1.0], dmatrix![0.0]],
+    //     vec![],
+    //     vec![],
+    //     vec![],
+    //     vec![],
+    //     vec![],
+    //     vec![OutEq::new(0, Div(X(0), P(1)))],
+    //     (1, 1),
+    // );
 
     let settings = settings::read("examples/bimodal_ke/config.toml").unwrap();
+    setup_log(&settings).unwrap();
     let data = data::read_pmetrics("examples/bimodal_ke/bimodal_ke.csv").unwrap();
-    let _result = fit(eq, data, settings);
+    let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
+    let result = algorithm.fit().unwrap();
+    // algorithm.initialize().unwrap();
+    // while !algorithm.next_cycle().unwrap() {}
+    // let result = algorithm.into_npresult();
+    result.write_outputs().unwrap();
+    // println!("{:?}", result);
+    // let _result = fit(eq, data, settings);
 }