russell_tensor 3.2.1

use russell_lab::{AsArray1D, Vector, format_scientific};
use serde::{Deserialize, Serialize};
use std::cmp;
use std::fmt::{self, Write};

/// Defines a first-order tensor (vector) in R³
///
/// The "standard" components are recorded here where "standard" means with respect to a Cartesian system.
#[derive(Deserialize, Serialize)]
#[serde(transparent)]
pub struct Tensor1 {
    /// Holds the 3 standard components (stack)
    ///
    /// Stack version => fixed size memory
    pub(crate) vec: [f64; 3],
}

impl Tensor1 {
    /// Allocates a new instance
    pub fn new() -> Self {
        Tensor1 { vec: [0.0, 0.0, 0.0] }
    }

    /// Allocates a new instance from a standard (dense) array
    ///
    /// # Input
    ///
    /// * `inp` -- the standard components; a 1D array (fixed-size array, slice, or vector)
    ///   with exactly 3 components
    ///
    /// # Panics
    ///
    /// A panic will occur if `inp` does not have exactly 3 components
    ///
    /// # Examples
    ///
    /// ```
    /// use russell_tensor::Tensor1;
    ///
    /// let u = Tensor1::from(&[1.0, 2.0, 3.0]);
    /// assert_eq!(u.get(0), 1.0);
    /// assert_eq!(u.get(1), 2.0);
    /// assert_eq!(u.get(2), 3.0);
    /// ```
    pub fn from<'a, S>(inp: &'a S) -> Self
    where
        S: AsArray1D<'a, f64>,
    {
        assert_eq!(inp.size(), 3, "the input array must have exactly 3 components");
        let mut tensor = Tensor1::new();
        tensor.vec[0] = inp.at(0);
        tensor.vec[1] = inp.at(1);
        tensor.vec[2] = inp.at(2);
        tensor
    }

    /// Sets the i-th standard component
    ///
    /// # Input
    ///
    /// * `i` -- The index must be 0, 1, or 2
    /// * `value` -- The standard component value
    ///
    /// # Panics
    ///
    /// A panic may occur if the index is out of range
    #[inline]
    pub fn set(&mut self, i: usize, value: f64) {
        self.vec[i] = value;
    }

    /// Adds a value to the i-th standard component
    ///
    /// # Input
    ///
    /// * `i` -- The index must be 0, 1, or 2
    /// * `value` -- The standard component value to be added
    ///
    /// # Panics
    ///
    /// A panic may occur if the index is out of range
    #[inline]
    pub fn add(&mut self, i: usize, value: f64) {
        self.vec[i] += value;
    }

    /// Scales this tensor in-place
    ///
    /// ```text
    /// self := α self
    /// ```
    ///
    /// # Examples
    ///
    /// ```
    /// use russell_lab::vec_approx_eq;
    /// use russell_tensor::Tensor1;
    ///
    /// let mut u = Tensor1::from(&[1.0, 2.0, 3.0]);
    /// u.scale(2.0);
    /// vec_approx_eq(&u.as_vector(), &[2.0, 4.0, 6.0], 1e-15);
    /// ```
    #[inline]
    pub fn scale(&mut self, alpha: f64) {
        self.vec[0] *= alpha;
        self.vec[1] *= alpha;
        self.vec[2] *= alpha;
    }

    /// Gets the i-th standard component
    ///
    /// # Input
    ///
    /// * `i` -- The index must be 0, 1, or 2
    ///
    /// # Panics
    ///
    /// A panic may occur if the index is out of range
    #[inline]
    pub fn get(&self, i: usize) -> f64 {
        self.vec[i]
    }

    /// Performs the cross product between this tensor and another
    ///
    /// ```text
    /// result = this × other
    /// ```
    pub fn cross(&self, result: &mut Tensor1, other: &Tensor1) {
        result.vec[0] = self.vec[1] * other.vec[2] - self.vec[2] * other.vec[1];
        result.vec[1] = self.vec[2] * other.vec[0] - self.vec[0] * other.vec[2];
        result.vec[2] = self.vec[0] * other.vec[1] - self.vec[1] * other.vec[0];
    }

    /// Calculates the dot (inner) product between this tensor and another
    ///
    /// ```text
    /// result = this . other
    /// ```
    pub fn dot(&self, other: &Tensor1) -> f64 {
        self.vec[0] * other.vec[0] + self.vec[1] * other.vec[1] + self.vec[2] * other.vec[2]
    }

    /// Calculates the Euclidean norm
    ///
    /// ```text
    /// norm(u) = √(u·u) = √(u₀² + u₁² + u₂²)
    /// ```
    ///
    /// # Examples
    ///
    /// ```
    /// use russell_lab::approx_eq;
    /// use russell_tensor::Tensor1;
    ///
    /// let u = Tensor1::from(&[3.0, 4.0, 12.0]);
    /// approx_eq(u.norm(), 13.0, 1e-13);
    /// ```
    #[inline]
    pub fn norm(&self) -> f64 {
        f64::sqrt(self.vec[0] * self.vec[0] + self.vec[1] * self.vec[1] + self.vec[2] * self.vec[2])
    }

    /// Returns this Tensor1 as a Vector object from russell_lab
    ///
    /// This function is useful for integration with `russell_lab` and for unit testing
    pub fn as_vector(&self) -> Vector {
        Vector::from(&self.vec)
    }

    /// Returns the components in scientific notation
    ///
    /// The returned [String] can be printed (e.g., `println!("{}", ...)`) or
    /// saved to a log file.
    ///
    /// # Input
    ///
    /// * `label` -- a label (e.g., a description of the tensor)
    /// * `factor` -- a factor to multiply the components before printing (e.g., a unit conversion factor)
    /// * `width` -- the field width used to print each component
    /// * `precision` -- the number of digits after the decimal point
    pub fn scientific(&self, label: &str, factor: f64, width: usize, precision: usize) -> String {
        let mut buf = String::new();
        writeln!(&mut buf, "{} =", label).unwrap();
        writeln!(&mut buf, "┌{:1$}┐", " ", width + 1).unwrap();
        for m in 0..3 {
            if m > 0 {
                writeln!(&mut buf, " │").unwrap();
            }
            write!(&mut buf, "│").unwrap();
            let val = self.vec[m] * factor;
            write!(&mut buf, "{:>1$}", format_scientific(val, width, precision), width).unwrap();
        }
        writeln!(&mut buf, " │").unwrap();
        writeln!(&mut buf, "└{:1$}┘", " ", width + 1).unwrap();
        buf
    }
}

impl fmt::Display for Tensor1 {
    /// Generates a string representation of the standard components associated with this Tensor1
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        // find largest width
        let mut width = 0;
        let mut buf = String::new();
        for m in 0..3 {
            let val = self.get(m);
            match f.precision() {
                Some(v) => write!(&mut buf, "{:.1$}", val, v).unwrap(),
                None => write!(&mut buf, "{}", val).unwrap(),
            }
            width = cmp::max(buf.chars().count(), width);
            buf.clear();
        }
        // draw vector
        width += 1;
        writeln!(f, "┌{:1$}┐", " ", width + 1).unwrap();
        for m in 0..3 {
            if m > 0 {
                writeln!(f, " │").unwrap();
            }
            write!(f, "│").unwrap();
            let val = self.get(m);
            match f.precision() {
                Some(v) => write!(f, "{:>1$.2$}", val, width, v).unwrap(),
                None => write!(f, "{:>1$}", val, width).unwrap(),
            }
        }
        writeln!(f, " │").unwrap();
        write!(f, "└{:1$}┘", " ", width + 1).unwrap();
        Ok(())
    }
}

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

#[cfg(test)]
mod tests {
    use super::Tensor1;
    use russell_lab::{approx_eq, vec_approx_eq};

    #[test]
    fn norm_works() {
        let u = Tensor1::from(&[3.0, 4.0, 12.0]);
        approx_eq(u.norm(), 13.0, 1e-13);
    }

    #[test]
    fn scale_works() {
        let mut u = Tensor1::from(&[1.0, -2.0, 3.0]);
        u.scale(2.0);
        vec_approx_eq(&u.as_vector(), &[2.0, -4.0, 6.0], 1e-15);
    }

    #[test]
    fn serialize_deserialize_works() {
        // transparent: the tensor is serialized as a bare [f64; 3] array
        let u = Tensor1::from(&[1.0, -2.0, 3.0]);
        let json = serde_json::to_string(&u).unwrap();
        assert_eq!(json, "[1.0,-2.0,3.0]");
        let back: Tensor1 = serde_json::from_str(&json).unwrap();
        assert_eq!(back.get(0), 1.0);
        assert_eq!(back.get(1), -2.0);
        assert_eq!(back.get(2), 3.0);
    }

    #[test]
    fn scientific_works() {
        let u = Tensor1::from(&[1.0, -2.0, 3.0]);
        assert_eq!(
            u.scientific("u", 1.0, 10, 2),
            "u =\n\
             ┌           ┐\n\
             │  1.00E+00 │\n\
             │ -2.00E+00 │\n\
             │  3.00E+00 │\n\
             └           ┘\n"
        );
        // factor
        assert_eq!(
            u.scientific("u", 2.0, 10, 2),
            "u =\n\
             ┌           ┐\n\
             │  2.00E+00 │\n\
             │ -4.00E+00 │\n\
             │  6.00E+00 │\n\
             └           ┘\n"
        );
    }

    #[test]
    fn new_set_get_work() {
        let mut u = Tensor1::new();
        u.set(0, 123.0);
        u.set(1, 456.0);
        u.set(2, 789.0);
        assert_eq!(u.get(0), 123.0);
        vec_approx_eq(&u.as_vector(), &[123.0, 456.0, 789.0], 1e-15);
        assert_eq!(
            format!("{}", u),
            "┌     ┐\n\
             │ 123 │\n\
             │ 456 │\n\
             │ 789 │\n\
             └     ┘"
        );
        assert_eq!(
            format!("{:.1}", u),
            "┌       ┐\n\
             │ 123.0 │\n\
             │ 456.0 │\n\
             │ 789.0 │\n\
             └       ┘"
        );
    }

    #[test]
    fn cross_and_dot_work() {
        let u = Tensor1::from(&[1.0, -2.0, 3.0]);
        let v = Tensor1::from(&[-1.0, 0.0, 1.0]);
        let mut w = Tensor1::new();
        u.cross(&mut w, &v);
        assert_eq!(w.get(0), -2.0);
        assert_eq!(w.get(1), -4.0);
        assert_eq!(w.get(2), -2.0);
        assert_eq!(u.dot(&w), 0.0);
        assert_eq!(v.dot(&w), 0.0);
    }
}