Add __repr__ for pyaugurs structs

crates/augurs-ets/src/auto.rs

@@ -329,6 +329,16 @@ impl AutoETS {
  }
  }
 
+ /// Get the season length of the model.
+ pub fn season_length(&self) -> usize {
+ self.season_length
+ }
+
+ /// Get the search specification.
+ pub fn spec(&self) -> AutoSpec {
+ self.spec
+ }
+
  /// Create a new `AutoETS` model search without any seasonal components.
  ///
  /// Equivalent to `AutoETS::new(1, "ZZN")`.

crates/pyaugurs/src/ets.rs

@@ -26,6 +26,14 @@ impl AutoETS {
  Ok(Self { inner })
  }
 
+ fn __repr__(&self) -> String {
+ format!(
+ "AutoETS(spec=\"{}\", season_length={})",
+ self.inner.spec(),
+ self.inner.season_length()
+ )
+ }
+
  /// Search for the best model, fitting it to the data.
  ///
  /// The model will be stored on the inner `AutoETS` instance, after which

crates/pyaugurs/src/lib.rs

@@ -37,7 +37,6 @@ impl From<Forecast> for augurs_core::Forecast {
  }
 }
 
-// TODO: __repr__, etc.
 #[pymethods]
 impl Forecast {
  #[new]
@@ -66,6 +65,17 @@ impl Forecast {
  })
  }
 
+ fn __repr__(&self) -> String {
+ let intervals = self.inner.intervals.as_ref();
+ format!(
+ "Forecast(point={:?}, level={:?}, lower={:?}, upper={:?})",
+ self.inner.point,
+ intervals.map(|x| x.level),
+ intervals.map(|x| &x.lower),
+ intervals.map(|x| &x.upper)
+ )
+ }
+
  /// Get the point forecast.
  fn point(&self, py: Python<'_>) -> Py<PyArray1<f64>> {
  // Use `to_pyarray` to allocate a new array on the Python heap.

crates/pyaugurs/src/trend.rs

@@ -35,6 +35,10 @@ pub struct PyTrendModel {
 
 #[pymethods]
 impl PyTrendModel {
+ fn __repr__(&self) -> String {
+ format!("PyTrendModel(model=\"{}\")", self.name())
+ }
+
  /// Wrap a trend model implemented in Python into a PyTrendModel.
  ///
  /// The returned PyTrendModel can be used in MSTL models using the