Use u32 instead of usize; return Vec instead of impl Iterator for low…

…er MSRV

crates/augurs-js/src/seasons.rs

@@ -6,6 +6,6 @@ use augurs_seasons::{Detector, PeriodogramDetector};
 
 /// Detect the seasonal periods in a time series.
 #[wasm_bindgen]
-pub fn seasonalities(y: &[f64]) -> Vec<usize> {
-    PeriodogramDetector::builder().build(y).detect().collect()
+pub fn seasonalities(y: &[f64]) -> Vec<u32> {
+    PeriodogramDetector::builder().build(y).detect()
 }

crates/augurs-seasons/README.md

@@ -20,7 +20,7 @@ let y = &[
     0.09, 0.29, 0.81, 0.49,
     0.11, 0.28, 0.78, 0.53,
 ];
-let periods: Vec<_> = PeriodogramDetector::builder().build(y).detect().collect();
+let periods = PeriodogramDetector::builder().build(y).detect();
 assert_eq!(periods[0], 4);
 # }
 ```

crates/augurs-seasons/benches/periodogram.rs

@@ -7,12 +7,7 @@ use augurs_testing::data::SEASON_EIGHT;
 fn season_eight(c: &mut Criterion) {
     let y = SEASON_EIGHT;
     c.bench_function("season_eight", |b| {
-        b.iter(|| {
-            PeriodogramDetector::builder()
-                .build(y)
-                .detect()
-                .collect::<Vec<_>>()
-        });
+        b.iter(|| PeriodogramDetector::builder().build(y).detect());
     });
 }
 

crates/augurs-seasons/src/lib.rs

@@ -17,5 +17,5 @@ pub use periodogram::{
 /// A detector of periodic signals in a time series.
 pub trait Detector {
     /// Detects the periods of the time series.
-    fn detect(&self) -> impl Iterator<Item = usize>;
+    fn detect(&self) -> Vec<u32>;
 }

crates/augurs-seasons/src/periodogram.rs

@@ -12,8 +12,8 @@ const DEFAULT_MAX_FFT_PERIOD: f64 = 512.0;
 /// A builder for a periodogram detector.
 #[derive(Debug, Clone)]
 pub struct Builder {
-    min_period: usize,
-    max_period: Option<usize>,
+    min_period: u32,
+    max_period: Option<u32>,
     threshold: f64,
 }
 
@@ -32,7 +32,7 @@ impl Builder {
     ///
     /// The default is 4.
     #[must_use]
-    pub fn min_period(mut self, min_period: usize) -> Self {
+    pub fn min_period(mut self, min_period: u32) -> Self {
         self.min_period = min_period;
         self
     }
@@ -41,7 +41,7 @@ impl Builder {
     ///
     /// The default is the length of the data divided by 3, or 512, whichever is smaller.
     #[must_use]
-    pub fn max_period(mut self, max_period: usize) -> Self {
+    pub fn max_period(mut self, max_period: u32) -> Self {
         self.max_period = Some(max_period);
         self
     }
@@ -71,15 +71,15 @@ impl Builder {
     }
 }
 
-fn default_max_period(data: &[f64]) -> usize {
-    (data.len() as f64 / DEFAULT_MIN_FFT_CYCLES).min(DEFAULT_MAX_FFT_PERIOD) as usize
+fn default_max_period(data: &[f64]) -> u32 {
+    (data.len() as f64 / DEFAULT_MIN_FFT_CYCLES).min(DEFAULT_MAX_FFT_PERIOD) as u32
 }
 
 /// A periodogram of a time series.
 #[derive(Debug, Clone, PartialEq)]
 pub struct Periodogram {
     /// The periods of the periodogram.
-    pub periods: Vec<usize>,
+    pub periods: Vec<u32>,
     /// The powers of the periodogram.
     pub powers: Vec<f64>,
 }
@@ -125,11 +125,11 @@ pub struct Period {
     /// The power of the peak.
     pub power: f64,
     /// The period of the peak.
-    pub period: usize,
+    pub period: u32,
     /// The previous period in the periodogram.
-    pub prev_period: usize,
+    pub prev_period: u32,
     /// The next period in the periodogram.
-    pub next_period: usize,
+    pub next_period: u32,
 }
 
 /// A season detector which uses a periodogram to identify seasonal periods.
@@ -140,8 +140,8 @@ pub struct Period {
 #[derive(Debug)]
 pub struct Detector<'a> {
     data: &'a [f64],
-    min_period: usize,
-    max_period: usize,
+    min_period: u32,
+    max_period: u32,
     threshold: f64,
 }
 
@@ -163,7 +163,7 @@ impl<'a> Detector<'a> {
     pub fn periodogram(&self) -> Periodogram {
         let frequency = 1.0;
         let data_len = self.data.len();
-        let n_per_segment = (self.max_period * 2).min(data_len / 2);
+        let n_per_segment = (self.max_period * 2).min(data_len as u32 / 2);
         let max_fft_size = (n_per_segment as f64).log2().floor() as usize;
         let n_segments = (data_len as f64 / n_per_segment as f64).ceil() as usize;
 
@@ -177,7 +177,7 @@ impl<'a> Detector<'a> {
         // Periods are the reciprocal of the frequency, since we've used a frequency of 1.
         // Make sure we skip the first one, which is 0, and the first power, which corresponds to
         // that.
-        let periods = freqs.iter().skip(1).map(|x| x.recip().round() as usize);
+        let periods = freqs.iter().skip(1).map(|x| x.recip().round() as u32);
         let power = sd.iter().skip(1).copied();
 
         let (periods, powers) = periods
@@ -202,8 +202,11 @@ impl<'a> Detector<'a> {
 }
 
 impl<'a> crate::Detector for Detector<'a> {
-    fn detect(&self) -> impl Iterator<Item = usize> {
-        self.periodogram().peaks(self.threshold).map(|x| x.period)
+    fn detect(&self) -> Vec<u32> {
+        self.periodogram()
+            .peaks(self.threshold)
+            .map(|x| x.period)
+            .collect()
     }
 }
 
@@ -225,7 +228,7 @@ mod test {
             0.09, 0.29, 0.81, 0.49,
             0.11, 0.28, 0.78, 0.53,
         ];
-        let periods = Detector::builder().build(y).detect().collect::<Vec<_>>();
+        let periods = Detector::builder().build(y).detect();
         assert_eq!(periods[0], 4);
     }
 

crates/augurs-seasons/src/test_data.rs

@@ -1,7 +1,7 @@
 use augurs_testing::data::{SEASON_EIGHT, SEASON_SEVEN};
 
 pub(crate) struct TestCase {
-    pub(crate) season_lengths: &'static [usize],
+    pub(crate) season_lengths: &'static [u32],
     pub(crate) data: &'static [f64],
 }
 

crates/pyaugurs/src/seasons.rs

@@ -7,14 +7,10 @@ use augurs_seasons::{Detector, PeriodogramDetector};
 
 /// Detect the seasonal periods in a time series.
 #[pyfunction]
-pub fn seasonalities(
-    py: Python<'_>,
-    y: PyReadonlyArray1<'_, f64>,
-) -> PyResult<Py<PyArray1<usize>>> {
+pub fn seasonalities(py: Python<'_>, y: PyReadonlyArray1<'_, f64>) -> PyResult<Py<PyArray1<u32>>> {
     Ok(PeriodogramDetector::builder()
         .build(y.as_slice()?)
         .detect()
-        .collect::<Vec<_>>()
         .to_pyarray(py)
         .into())
 }