Abstracting netCDF4 to support interpolator

src/interpolator/linear_fit.rs

@@ -0,0 +1,98 @@
+//! Module containing interpolators
+//!
+//! Contains the bilinear_interpolator function
+
+use tracing::trace;
+
+use crate::error::{Error, Result};
+
+#[derive(Debug)]
+/// Handles a linear relationship
+///
+/// This was originally created to handle the dimensions of regular girds,
+/// such latitude and longitude in a regularly spaced dataset, providing a
+/// cheap conversion between the dimension, such as latitute, to the
+/// correspondent index position.
+pub(super) struct LinearFit<T> {
+    /// Scale between the physical dimension and the index position.
+    slope: T,
+    /// Offset where the grid starts.
+    intercept: T,
+}
+
+impl<T> LinearFit<T>
+where
+    T: Copy,
+    T: std::ops::Sub<Output = T>,
+    T: std::ops::Mul<Output = T>,
+{
+    #[allow(dead_code)]
+    /// Convert to 'index' scale position
+    ///
+    /// Predict the index position of a given value. For instance, a result of
+    /// `1.5` meants that the given value is between the second and third
+    /// positions in the grid (first gridpoint is 0).
+    pub(super) fn predict(&self, x: T) -> T {
+        (x - self.intercept) * self.slope
+    }
+}
+
+/*
+#[cfg(test)]
+mod test_linearfit {
+    use super::*;
+
+    #[test]
+    fn test_fit_u64() {
+        let lf = LinearFit::<u64> {
+            slope: 2,
+            intercept: 1,
+        };
+        assert_eq!(lf.predict(3), 7);
+    }
+
+    #[test]
+    fn test_fit_f64() {
+        let lf = LinearFit::<f64> {
+            slope: 2.0,
+            intercept: 1.0,
+        };
+        assert_eq!(lf.predict(3.0), 7.0);
+    }
+}
+*/
+
+/// Tolerance for linear relationship
+///
+/// If the ratio between the two dimensions deviate more than that, it will
+/// not be considered a linear relationship.
+const LINEAR_RELATION_TOLERANCE: f64 = 0.005;
+
+impl LinearFit<f64> {
+    /// Create a new LinearFit from a vector of values
+    ///
+    /// For a given vector of values, calculates the best linear relationship
+    /// between the values and its index position with the purpose to estimate
+    /// the index position of the closest value to a given target.
+    ///
+    /// This procedure also validates if a linear relationship is a good
+    /// approximation with a threshold of 0.5% of tolerance.
+    pub(super) fn from_fit(x: ndarray::ArrayD<f64>) -> Result<LinearFit<f64>> {
+        let dx = &x.slice(ndarray::s![1..]) - &x.slice(ndarray::s![..-1]);
+        let slope = dx.mean().expect("Failed to calculate mean");
+        let criteria = ((dx - slope) / slope)
+            .abs()
+            .into_iter()
+            .map(|v| v > LINEAR_RELATION_TOLERANCE)
+            .any(|v| v);
+        if criteria {
+            return Err(Error::Undefined(
+                "Linear is a bad approximation".to_string(),
+            ));
+        }
+        Ok(LinearFit {
+            slope,
+            intercept: x[0],
+        })
+    }
+}

src/interpolator.rs → src/interpolator/mod.rs

@@ -2,13 +2,16 @@
 //!
 //! Contains the bilinear_interpolator function
 
+mod linear_fit;
+
 use std::collections::HashMap;
 
 use tracing::trace;
 
 use crate::datatype::Point;
 use crate::error::{Error, Result};
 use crate::io::Dataset;
+use linear_fit::LinearFit;
 
 #[allow(dead_code)]
 /// Bilinear interpolation
@@ -160,97 +163,6 @@ fn test_edges() {
     }
 }
 
-#[derive(Debug)]
-/// Handles a linear relationship
-///
-/// This was originally created to handle the dimensions of regular girds,
-/// such latitude and longitude in a regularly spaced dataset, providing a
-/// cheap conversion between the dimension, such as latitute, to the
-/// correspondent index position.
-struct LinearFit<T> {
-    /// Scale between the physical dimension and the index position.
-    slope: T,
-    /// Offset where the grid starts.
-    intercept: T,
-}
-
-impl<T> LinearFit<T>
-where
-    T: Copy,
-    T: std::ops::Sub<Output = T>,
-    T: std::ops::Mul<Output = T>,
-{
-    #[allow(dead_code)]
-    /// Convert to 'index' scale position
-    ///
-    /// Predict the index position of a given value. For instance, a result of
-    /// `1.5` meants that the given value is between the second and third
-    /// positions in the grid (first gridpoint is 0).
-    fn predict(&self, x: T) -> T {
-        (x - self.intercept) * self.slope
-    }
-}
-
-/*
-#[cfg(test)]
-mod test_linearfit {
-    use super::*;
-
-    #[test]
-    fn test_fit_u64() {
-        let lf = LinearFit::<u64> {
-            slope: 2,
-            intercept: 1,
-        };
-        assert_eq!(lf.predict(3), 7);
-    }
-
-    #[test]
-    fn test_fit_f64() {
-        let lf = LinearFit::<f64> {
-            slope: 2.0,
-            intercept: 1.0,
-        };
-        assert_eq!(lf.predict(3.0), 7.0);
-    }
-}
-*/
-
-/// Tolerance for linear relationship
-///
-/// If the ratio between the two dimensions deviate more than that, it will
-/// not be considered a linear relationship.
-const LINEAR_RELATION_TOLERANCE: f64 = 0.005;
-
-impl LinearFit<f64> {
-    /// Create a new LinearFit from a vector of values
-    ///
-    /// For a given vector of values, calculates the best linear relationship
-    /// between the values and its index position with the purpose to estimate
-    /// the index position of the closest value to a given target.
-    ///
-    /// This procedure also validates if a linear relationship is a good
-    /// approximation with a threshold of 0.5% of tolerance.
-    fn from_fit(x: ndarray::ArrayD<f64>) -> Result<LinearFit<f64>> {
-        let dx = &x.slice(ndarray::s![1..]) - &x.slice(ndarray::s![..-1]);
-        let slope = dx.mean().expect("Failed to calculate mean");
-        let criteria = ((dx - slope) / slope)
-            .abs()
-            .into_iter()
-            .map(|v| v > LINEAR_RELATION_TOLERANCE)
-            .any(|v| v);
-        if criteria {
-            return Err(Error::Undefined(
-                "Linear is a bad approximation".to_string(),
-            ));
-        }
-        Ok(LinearFit {
-            slope,
-            intercept: x[0],
-        })
-    }
-}
-
 struct RegularGrid<'a> {
     // dataset: &'a dyn Dataset,
     dataset: Box<dyn Dataset + 'a>,