Fix new clippy lints

README.md

@@ -128,10 +128,10 @@ let mut coder = DefaultAnsCoder::new();
 
 // Define some data and a sequence of entropy models. We use quantized Gaussians here,
 // but `constriction` also provides other models and allows you to implement your own.
-let symbols = vec![23i32, -15, 78, 43, -69];
+let symbols = [23i32, -15, 78, 43, -69];
 let quantizer = DefaultLeakyQuantizer::new(-100..=100);
-let means = vec![35.2f64, -1.7, 30.1, 71.2, -75.1];
-let stds = vec![10.1f64, 25.3, 23.8, 35.4, 3.9];
+let means = [35.2f64, -1.7, 30.1, 71.2, -75.1];
+let stds = [10.1f64, 25.3, 23.8, 35.4, 3.9];
 let models = means.iter().zip(&stds).map(
     |(&mean, &std)| quantizer.quantize(probability::distribution::Gaussian::new(mean, std))
 );

src/pybindings/stream/model/internals.rs

@@ -44,7 +44,7 @@ where
 {
     #[inline]
     fn clone(&self) -> Self {
-        Self(self.0)
+        *self
     }
 }
 

src/stream/chain.rs

@@ -1297,7 +1297,7 @@ mod tests {
         let coder2 = ChainCoder::from_remainders(remainders).unwrap();
         let coder3 = ChainCoder::from_remainders(remainders_suffix).unwrap();
 
-        for (mut coder, prefix) in vec![
+        for (mut coder, prefix) in [
             (coder, vec![]),
             (coder2, vec![]),
             (coder3, remainders_prefix),

src/stream/model.rs

@@ -4246,7 +4246,7 @@ mod tests {
     #[test]
     fn lookup_noncontiguous() {
         let symbols = "axcy";
-        let probabilities = vec![3u8, 18, 1, 42];
+        let probabilities = [3u8, 18, 1, 42];
         let encoder_model = NonContiguousCategoricalEncoderModel::<_, u8, 6>::from_symbols_and_nonzero_fixed_point_probabilities(
             symbols.chars(),probabilities.iter(),false
         )

tests/readme.rs

@@ -10,10 +10,10 @@ fn example() {
 
     // Define some data and a sequence of entropy models. We use quantized Gaussians
     // here, but you could also use other models or even implement your own.
-    let symbols = vec![23i32, -15, 78, 43, -69];
+    let symbols = [23i32, -15, 78, 43, -69];
     let quantizer = DefaultLeakyQuantizer::new(-100..=100);
-    let means = vec![35.2f64, -1.7, 30.1, 71.2, -75.1];
-    let stds = vec![10.1f64, 25.3, 23.8, 35.4, 3.9];
+    let means = [35.2f64, -1.7, 30.1, 71.2, -75.1];
+    let stds = [10.1f64, 25.3, 23.8, 35.4, 3.9];
     let models = means.iter().zip(&stds).map(|(&mean, &std)| {
         quantizer.quantize(probability::distribution::Gaussian::new(mean, std))
     });