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https://github.com/gwbres/allan-tools
:chart_with_upwards_trend: Allantools portage to Rust
https://github.com/gwbres/allan-tools
frequency frequency-analysis mathematics maths rust statistics system-analysis time time-frequency time-frequency-analysis time-series-analysis
Last synced: about 4 hours ago
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:chart_with_upwards_trend: Allantools portage to Rust
- Host: GitHub
- URL: https://github.com/gwbres/allan-tools
- Owner: gwbres
- License: apache-2.0
- Created: 2021-12-23T07:39:02.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-03-13T11:24:34.000Z (over 2 years ago)
- Last Synced: 2024-10-29T23:43:36.659Z (8 days ago)
- Topics: frequency, frequency-analysis, mathematics, maths, rust, statistics, system-analysis, time, time-frequency, time-frequency-analysis, time-series-analysis
- Language: Rust
- Homepage:
- Size: 486 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE-APACHE
Awesome Lists containing this project
README
# allan-tools
[](https://crates.io/crates/allan-tools)
[](https://github.com/gwbres/allan-tools/blob/main/LICENSE-APACHE)
[](https://github.com/gwbres/allan-tools/blob/main/LICENSE-MIT)
[](https://crates.io/crates/allan-tools)
[](https://github.com/gwbres/allan-tools/actions/workflows/rust.yml)
[](https://docs.rs/allan-tools/badge.svg)
Allantools (python lib) portage to Rust
This library allows easy computations of Allan deviation & similar statistics.
These statistical methods are mostly used in system stability studies.
## Allan and other deviations
Compute Allan deviation over raw data:
```rust
use allantools::*;
let taus = tau::generator(tau::TauAxis::Octave, 2, 128); // [2, 4, 8, ... 128]
let sampling_rate = 1.0_f64; // [Hz]
let (dev, errs) = deviation(&data, &taus, Deviation::Allan, sampling_rate, false, false).unwrap();
```
This lib against `Time Lab`.
### Known calculations
* Deviation::Allan `adev`
* Deviation::Modified `mdev`
* Deviation::Time `tdev` (not fully tested yet)
* Deviation::Hadamard `hdev` (not fully tested yet)
* Deviation::Gcov `gcov` allan covariances (not tested yet)
### Error bars
Only basic (biased) error bars following the 1/√N decay are currently produced
### Overlapping
Improve statiscal confidence by using _overlapped_ formulae
```rust
let data: Vec = some_data();
let taus = tau::generator(tau::TauAxis::Octave, 128);
let overlapping = true;
let sampling_rate = 1.0_f64; // [Hz]
let (var, errs) = deviation(&data, &taus, Deviation::Allan, sampling_rate, false, overlapping).unwrap();
```
### Fractionnal data
`is fractional` can be used to compute statistics over fractional
(n.a) data:
```rust
let data: Vec = some_data();
let taus = tau::generator(tau::TauAxis::Octave, 10000);
let is_fractional = true;
let sampling_rate = 1.0_f64; // [Hz]
let ( adev, errs) = deviation(&data, &taus, Deviation::Allan, sampling_rate, is_fractional, false).unwrap();
let (oadev, errs) = deviation(&data, &taus, Deviation::Allan, sampling_rate, is_fractional, true).unwrap();
```
### Tau axis generator
The user can pass any τ serie to all computation methods.
This lib integrates a τ axis generator too, which is a convenient
method to quickly pass a standard axis to a computation method.
Several axis are known:
* TauAxis::Octave is the most efficient
* TauAxis::Decade is the standard and is efficient
* TauAxis::All requires more computation
```rust
let taus = tau::generator(tau::TauAxis::Decade, 1.0, 10000.0); // [1.0, 10.0, 100.0, ..., 10000.0]
```
Using TauAxis::All requires more computation but gives a total
time granularity
```rust
let taus = tau::generator(tau::TauAxis::All, 1.0, 100.0); // [1.0, 2.0, 3.0, ..., 99.0, 100.0]
```
### Tau offset and error management
This library computes the requested statistics for all τ values, as long as
$#964;(n) can be evaluated.
If τ (n) cannot be evaluated, computation stops and returns all
previously evaluated offsets.
If not a single τ value is feasible, the lib returns Error::NotEnoughSamplesError
The user must pass a valid τ serie, otherwise:
* TauAxis::NullTauValue: is returned when τ = 0 (non sense) is requested
* TauAxis::NegativeTauValue: is return when τ < 0 (non physical) is requested
* TauAxis::InvalidTauShape: shape is not an increasing (not necessarily steady) shape
### Data & Noise generators
Some data generators were integrated or develpped for testing purposes:
* White noise generator
```rust
let x = allantools::noise::white_noise(
-140.0_f64, // dBc/Hz
1.0_f64, // (Hz)
10000); // 10k samples
```
* Pink noise generator
```rust
let x = allantools::noise::pink_noise(
-140.0_f64, // dBc @ 1Hz
1.0_f64, // (Hz)
1024); // 1k samples
```
| Noise | White PM | Flicker PM | White FM | Flicker FM |
|:------:|:-------------------------:|:------------------------:|:------------:|:-----------:|
| adev | -1 | -1 | -1/2 | 0 |
| mdev | -3/2 | -1 | -1/2 | 0 |
| method | utils::diff(noise::white) | utils::diff(noise::pink) | noise::white | noise::pink |
### Power Law Identification
#### NIST LAG1D autocorrelation
[NIST Power Law identification method[[46]]](https://www.nist.gov/publications/handbook-frequency-stability-analysis)
This macro works well on data serie where one noise process is very dominant.
```rust
let r = allantools::nist_lag1d_autocorr(&some_data);
```
#### Bias1 + R(n) identification method
TODO
### Three Cornered Hat
Three cornered hat fashion statistics, to estimate
a/b/c from a against b, b against c and c against a measurements.
```rust
let a_against_b = some_measurements("a", "b");
let b_against_c = some_measurements("b", "c");
let c_against_a = some_measurements("c", "a");
let taus = tau::tau_generator(tau::TauAxis::Octave, 10000.0);
let sampling_rate = 1.0;
let is_fractionnal = false;
let overlapping = true;
let ((dev_a, err_a),(dev_b,err_b),(dev_c,err_c)) =
three_cornered_hat(&a_against_b, &b_against_c, &c_against_a,
&taus, sampling_rate, is_fractionnal, overlapping, Deviation::Allan).unwrap();
```
### Tools & utilities
__cumsum__ : (python::numpy like) returns cummulative sum of a serie
```rust
let data: Vec = some_data();
allantools::utilities::cumsum(data, None);
allantools::utilities::cumsum(data, Some(10E6_f64)); // opt. normalization
```
__diff__ : (python::numpy like) returns 1st order derivative of a serie
```rust
let data: Vec = some_data();
allantools::utilities::diff(data, None);
allantools::utilities::diff(data, Some(10E6_f64)); // opt. normalization
```
__random__ : generates a pseudo random sequence 0 < x <= 1.0
```rust
let data = allantools::utilities::random(1024); // 1k symbols
println!("{:#?}", data);
```
__normalize__ : normalizes a sequence to 1/norm :
```rust
let data: Vec = somedata();
let normalized = allantools::utilities::normalize(
data,
2.0_f64 * std::f64::consts::PI); // 1/(2pi)
```
__to\_fractional\_frequency__ : converts a raw data serie
to fractional data.
```rust
let data: Vec = somedata(); // sampled @ 10kHz
let fract = allantools::utilities::to_fractional_frequency(data, 10E3); // :)
```
__fractional_integral__ : converts a serie of fractional measurements
to integrated measurements (like fractional frequency (n.a) to phase time (s)).
```rust
let data: Vec = somedata(); // (n.a)
let fract = allantools::utilities::fractional_integral(data, 1.0); // sampled @ 1Hz :)
```
__fractional\_freq\_to\_phase\_time__ : macro wrapper of previous function
__phase\_to\_radians__ : converts phase time (s) to phase radians (rad)
```rust
let data: Vec = somedata(); // (s)
let data_rad = allantools::utilities::phase_to_radians(data);
```