clippy

src/blur.rs

@@ -37,7 +37,7 @@ impl Blur {
     }
 
     // Approximate 1D Gaussian filter of standard deviation sigma with N box filter passes. Each element in the output array contains the radius of the box filter for the corresponding pass.
-    fn boxes_for_gaussian<const N: usize>(sigma: f32) -> ([usize; N]) {
+    fn boxes_for_gaussian<const N: usize>(sigma: f32) -> [usize; N] {
         let w_ideal = (12.0 * sigma * sigma / N as f32 + 1.0).sqrt();
         let mut w = w_ideal as usize;
         w -= 1 - (w & 1);

src/imgdata.rs

@@ -32,9 +32,9 @@ impl ThinGridData {
 
     #[allow(dead_code)]
     pub fn new_from_grid_vec(in_grids: Vec<Grid>) -> Vec<Self> {
-        return in_grids.iter().map(|grid|{
+        in_grids.iter().map(|grid|{
             Self::new_from_grid(grid)
-        }).collect();
+        }).collect()
     }
 
     // from grid.rs (needed in image gen)
@@ -60,12 +60,12 @@ impl ThinGridData {
 
     pub fn size_of(&self) -> usize {
         let mut output: usize = 0;
-        output = output + std::mem::size_of_val(&self.width);
-        output = output + std::mem::size_of_val(&self.height);
+        output += std::mem::size_of_val(&self.width);
+        output += std::mem::size_of_val(&self.height);
         for i in self.data.iter() {
-            output = output + std::mem::size_of_val(&i);
+            output += std::mem::size_of_val(&i);
         }
-        return output;
+        output
     }
 }
 
@@ -97,12 +97,12 @@ impl ImgData {
 
     pub fn size_of(&self) -> usize {
         let mut output: usize = 0;
-        output = output + std::mem::size_of_val(&self.iteration);
-        output = output + std::mem::size_of_val(&self.palette);
+        output += std::mem::size_of_val(&self.iteration);
+        output += std::mem::size_of_val(&self.palette);
         for grid in self.grids.iter() {
-            output = output + grid.size_of();
+            output += grid.size_of();
         }
-        return output;
+        output
     }
 
     #[inline]

src/model.rs

@@ -132,7 +132,7 @@ impl Model {
             self.save_image_data();
 
             let agents_tick_elapsed: f64 = agents_tick_time.elapsed().as_millis() as f64;
-            let ms_per_agent: f64 = (agents_tick_elapsed as f64) / (agents_num as f64);
+            let ms_per_agent: f64 = agents_tick_elapsed / (agents_num as f64);
             time_per_agent_list.push(ms_per_agent);
             time_per_step_list.push(agents_tick_elapsed);
 
@@ -149,9 +149,9 @@ impl Model {
         pb.finish();
 
         let avg_per_step: f64 =
-            time_per_step_list.iter().sum::<f64>() as f64 / time_per_step_list.len() as f64;
+            time_per_step_list.iter().sum::<f64>() / time_per_step_list.len() as f64;
         let avg_per_agent: f64 =
-            time_per_agent_list.iter().sum::<f64>() as f64 / time_per_agent_list.len() as f64;
+            time_per_agent_list.iter().sum::<f64>() / time_per_agent_list.len() as f64;
         println!(
             "Average time per step: {}ms\nAverage time per agent: {}ms",
             avg_per_step, avg_per_agent
@@ -159,7 +159,7 @@ impl Model {
     }
 
     fn size_of_imgdata_vec(&self) -> usize {
-        return (self.img_data_vec[0].size_of() as usize) * (self.img_data_vec.len() as usize);
+        self.img_data_vec[0].size_of() * self.img_data_vec.len()
     }
 
     fn save_image_data(&mut self) {
@@ -200,7 +200,7 @@ impl Model {
         pb.finish();
         */
 
-        (&self.img_data_vec)
+        self.img_data_vec
             .par_iter()
             .progress_with(pb)
             .for_each(|img| {

src/palette.rs

@@ -15,7 +15,7 @@ pub fn random_palette() -> Palette {
 const fn hex_to_color(c: usize) -> image::Rgb<u8> {
     let r = (c >> 16) & 0xff;
     let g = (c >> 8) & 0xff;
-    let b = (c >> 0) & 0xff;
+    let b = c & 0xff;
     image::Rgb::<u8>([r as u8, g as u8, b as u8])
 }