spectrum-analyzer 0.2.2

/*
MIT License

Copyright (c) 2021 Philipp Schuster

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
//! Module for the struct [`FrequencySpectrum`].

use crate::frequency::{Frequency, FrequencyValue};
use alloc::boxed::Box;
use alloc::collections::BTreeMap;
use alloc::vec::Vec;
use core::cell::{Cell, Ref, RefCell};

/// Describes the type for a function factory that generates a function that can scale/normalize
/// the data inside [`FrequencySpectrum`].
/// This can be used to subtract `min` value from all values for example , if `min`
/// is `> 0`. The signature is the following:
/// `(min: f32, max: f32, average: f32, median: f32) -> fn(f32) -> f32`
/// i.e. you provide a function which generates a function that gets
/// applied to each element.
pub type SpectrumTotalScaleFunctionFactory =
    Box<dyn Fn(f32, f32, f32, f32) -> Box<dyn Fn(f32) -> f32>>;

/// Convenient wrapper around the processed FFT result which describes each frequency and
/// its value/amplitude in the analyzed slice of samples. It only consists of the frequencies
/// which were desired, e.g. specified via
/// [`crate::limit::FrequencyLimit`] when [`crate::samples_fft_to_spectrum`] was called.
#[derive(Debug)]
pub struct FrequencySpectrum {
    /// Raw data. Vector is sorted from lowest
    /// frequency to highest.
    data: RefCell<Vec<(Frequency, FrequencyValue)>>,
    /// Average value of frequency value/magnitude/amplitude.
    average: Cell<FrequencyValue>,
    /// Median value of frequency value/magnitude/amplitude.
    median: Cell<FrequencyValue>,
    /// Minimum value of frequency value/magnitude/amplitude.
    min: Cell<FrequencyValue>,
    /// Maximum value of frequency value/magnitude/amplitude.
    max: Cell<FrequencyValue>,
}

impl FrequencySpectrum {
    /// Creates a new object. Calculates several metrics on top of
    /// the passed vector.
    #[inline(always)]
    pub fn new(data: Vec<(Frequency, FrequencyValue)>) -> Self {
        let obj = Self {
            data: RefCell::new(data),
            average: Cell::new(FrequencyValue::from(-1.0)),
            median: Cell::new(FrequencyValue::from(-1.0)),
            min: Cell::new(FrequencyValue::from(-1.0)),
            max: Cell::new(FrequencyValue::from(-1.0)),
        };
        // IMPORTANT!!
        obj.calc_statistics();
        obj
    }

    /// Applies the function generated by `total_scaling_fn` to each element and updates
    /// `min`, `max`, etc. afterwards accordingly.
    ///
    /// ## Parameters
    /// * `total_scaling_fn` See [`crate::spectrum::SpectrumTotalScaleFunctionFactory`].
    #[inline(always)]
    pub fn apply_total_scaling_fn(&self, total_scaling_fn: SpectrumTotalScaleFunctionFactory) {
        let scale_fn = (total_scaling_fn)(
            // into() => FrequencyValue => f32
            self.min.get().val(),
            self.max.get().val(),
            self.average.get().val(),
            self.median.get().val(),
        );

        {
            // drop RefMut<> from borrow_mut() before calc_statistics
            let mut data = self.data.borrow_mut();
            for (_fr, fr_val) in data.iter_mut() {
                *fr_val = (scale_fn)(fr_val.val()).into()
            }
            // drop RefMut<> from borrow_mut() before calc_statistics
        }
        self.calc_statistics();
    }

    /// Getter for lazily evaluated `average`.
    #[inline(always)]
    pub fn average(&self) -> FrequencyValue {
        self.average.get()
    }

    /// Getter for lazily evaluated `median`.
    #[inline(always)]
    pub fn median(&self) -> FrequencyValue {
        self.median.get()
    }

    /// Getter for lazily evaluated `max`.
    #[inline(always)]
    pub fn max(&self) -> FrequencyValue {
        self.max.get()
    }

    /// Getter for lazily evaluated `min`.
    #[inline(always)]
    pub fn min(&self) -> FrequencyValue {
        self.min.get()
    }

    /// [`max()`] - [`min()`]
    #[inline(always)]
    pub fn range(&self) -> FrequencyValue {
        self.max() - self.min()
    }

    /// Getter for `data`.
    #[inline(always)]
    pub fn data(&self) -> Ref<Vec<(Frequency, FrequencyValue)>> {
        self.data.borrow()
    }

    /// Returns a `BTreeMap`. The key is of type u32.
    /// (`f32` is not `Ord`, hence we can't use it as key.) You can optionally specify a
    /// scale function, e.g. multiply all frequencies with 1000 for better
    /// accuracy when represented as unsigned integer.
    ///
    /// ## Parameters
    /// * `scale_fn` optional scale function, e.g. multiply all frequencies with 1000 for better
    ///              accuracy when represented as unsigned integer.
    ///
    /// ## Return
    /// New `BTreeMap` from frequency to frequency value.
    #[inline(always)]
    pub fn to_map(&self, scale_fn: Option<&dyn Fn(f32) -> u32>) -> BTreeMap<u32, f32> {
        self.data
            .borrow()
            .iter()
            .map(|(fr, fr_val)| (fr.val(), fr_val.val()))
            .map(|(fr, fr_val)| {
                (
                    if let Some(fnc) = scale_fn {
                        (fnc)(fr)
                    } else {
                        fr as u32
                    },
                    fr_val,
                )
            })
            .collect()
    }

    /*/// Returns an iterator over the underlying vector [`data`].
    #[inline(always)]
    pub fn iter(&self) -> Iter<(Frequency, FrequencyValue)> {
        self.data.borrow().iter()
    }*/

    /// Calculates min, max, median and average of the frequency values/magnitudes/amplitudes.
    #[inline(always)]
    fn calc_statistics(&self) {
        let data = self.data.borrow();
        // first: order all by frequency value in ascending order
        let mut vals = data
            .iter()
            // map to only value
            .map(|(_fr, val)| val)
            // f64 to prevent overflow
            // .map(|v| v as f64)
            .collect::<Vec<&FrequencyValue>>();
        vals.sort();

        // sum
        let sum: f32 = vals
            .iter()
            .map(|fr_val| fr_val.val())
            .fold(0.0, |a, b| a + b);

        let avg = sum / vals.len() as f32;
        let average: FrequencyValue = avg.into();

        let median = {
            // we assume that vals.length() is always even, because
            // it must be a power of 2 (for FFT)
            let a = *vals[vals.len() / 2 - 1];
            let b = *vals[vals.len() / 2];
            (a + b) / 2.0.into()
        };
        let min = *vals[0];
        let max = *vals[vals.len() - 1];

        self.min.replace(min);
        self.max.replace(max);
        self.average.replace(average);
        self.median.replace(median);
    }
}

/*impl FromIterator<(Frequency, FrequencyValue)> for FrequencySpectrum {

    #[inline(always)]
    fn from_iter<T: IntoIterator<Item=(Frequency, FrequencyValue)>>(iter: T) -> Self {
        // 1024 is just a guess: most likely 2048 is a common FFT length,
        // i.e. 1024 results for the frequency spectrum.
        let mut vec = Vec::with_capacity(1024);
        for (fr, val) in iter {
            vec.push((fr, val))
        }

        FrequencySpectrum::new(vec)
    }
}*/

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_spectrum() {
        let spectrum = vec![
            (0.0_f32, 5.0_f32),
            (50.0, 50.0),
            (100.0, 100.0),
            (150.0, 150.0),
            (200.0, 100.0),
            (250.0, 20.0),
            (300.0, 0.0),
            (450.0, 200.0),
        ];

        let spectrum = spectrum
            .into_iter()
            .map(|(fr, val)| (fr.into(), val.into()))
            .collect::<Vec<(Frequency, FrequencyValue)>>();
        let spectrum = FrequencySpectrum::new(spectrum);

        assert_eq!(
            (0.0.into(), 5.0.into()),
            spectrum.data()[0],
            "Vector must be ordered"
        );
        assert_eq!(
            (50.0.into(), 50.0.into()),
            spectrum.data()[1],
            "Vector must be ordered"
        );
        assert_eq!(
            (100.0.into(), 100.0.into()),
            spectrum.data()[2],
            "Vector must be ordered"
        );
        assert_eq!(
            (150.0.into(), 150.0.into()),
            spectrum.data()[3],
            "Vector must be ordered"
        );
        assert_eq!(
            (200.0.into(), 100.0.into()),
            spectrum.data()[4],
            "Vector must be ordered"
        );
        assert_eq!(
            (250.0.into(), 20.0.into()),
            spectrum.data()[5],
            "Vector must be ordered"
        );
        assert_eq!(
            (300.0.into(), 0.0.into()),
            spectrum.data()[6],
            "Vector must be ordered"
        );
        assert_eq!(
            (450.0.into(), 200.0.into()),
            spectrum.data()[7],
            "Vector must be ordered"
        );

        assert_eq!(0.0, spectrum.min().val(), "min() must work");
        assert_eq!(200.0, spectrum.max().val(), "max() must work");
        assert_eq!(200.0 - 0.0, spectrum.range().val(), "range() must work");
        assert_eq!(78.125, spectrum.average().val(), "average() must work");
        assert_eq!(
            (50 + 100) as f32 / 2.0,
            spectrum.median().val(),
            "median() must work"
        );
    }
}