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use rand::{distributions::Uniform, prelude::Distribution, rngs::StdRng, SeedableRng};
use rustdct::num_traits::{Float, FromPrimitive};
pub mod known_data;
pub mod reference_impls;
#[macro_use]
pub mod macros;
pub fn fuzzy_cmp<T: Float>(a: T, b: T, tolerance: T) -> bool {
a >= b - tolerance && a <= b + tolerance
}
pub fn compare_float_vectors<T: Float + FromPrimitive>(expected: &[T], observed: &[T]) -> bool {
assert_eq!(expected.len(), observed.len());
let tolerance = T::from_f64(0.001).unwrap();
for i in 0..expected.len() {
if !fuzzy_cmp(observed[i], expected[i], tolerance) {
return false;
}
}
true
}
pub fn random_signal<T: Float + FromPrimitive>(length: usize) -> Vec<T> {
let mut sig = Vec::with_capacity(length);
let distribution = Uniform::new(0.0, 10.0);
let seed: [u8; 32] = [
1, 5, 6, 7, 1, 5, 3, 7, 4, 2, 6, 2, 6, 1, 5, 6, 7, 1, 5, 3, 7, 4, 2, 6, 2, 6, 1, 5, 1, 0,
1, 7,
];
let mut rng: StdRng = SeedableRng::from_seed(seed);
for _ in 0..length {
sig.push(T::from_f64(distribution.sample(&mut rng)).unwrap());
}
return sig;
}
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