https://github.com/rmaestre/variableStars
Optimized package for extract patterns on variable stars
https://github.com/rmaestre/variableStars
astrophysics corot cpp neural-networks r tensorflow
Last synced: over 1 year ago
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Optimized package for extract patterns on variable stars
- Host: GitHub
- URL: https://github.com/rmaestre/variableStars
- Owner: rmaestre
- License: mit
- Created: 2018-11-06T07:14:05.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-04-08T05:11:22.000Z (over 6 years ago)
- Last Synced: 2023-03-23T03:02:33.404Z (over 3 years ago)
- Topics: astrophysics, corot, cpp, neural-networks, r, tensorflow
- Language: R
- Homepage:
- Size: 70.9 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
[](https://travis-ci.org/rmaestre/variableStars)
Introduction
------------
Variable Star package provides the main funtions to analized patterns on the [oscilation modes of variable stars](https://en.wikipedia.org/wiki/Asteroseismology).
All the code is based on these two papers:
- [Asteroseismic analysis of the CoRoT *δ* Scuti star HD 174936](https://www.aanda.org/articles/aa/full_html/2009/40/aa11932-09/aa11932-09.html)
- [An in-depth study of HD 174966 with CoRoT photometry and HARPS spectroscopy](https://www.aanda.org/articles/aa/full_html/2013/11/aa20256-12/aa20256-12.html)
Installation
------------
``` r
install.packages("devtools")
library(devtools)
install_github("rmaestre/variableStars")
```
#### ** Note for Windows users **
We strongly recommend to use [The Microsoft R Open & MKL R distribution](https://mran.microsoft.com/open) as R distribution.
Also, **please** do not forgive to include the file ```Makevars.win``` into the **src** project folder with the next content:
```
CXX_STD = CXX11
PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
```
An UI for experimentation with synthetic data is provided
---------------------------------------------------------
``` r
library(variableStars)
runUISynthetic()
```
Example of use on a pulsar data
-------------------------------
Please, find [here](docs/Experiment_-_HD174936.md) or [here](docs/Experiment_-_HD174966.md) the main execution of the complete package procedure.
Main Workflow
-------------
(The pulsar in the Crab Nebula is composed by images taken by Hubble (red) and Chandra X-Ray(blue))
Implementation
-------------
All core funcionalities are programmed [in C++ using RcppArmadillo integrated through Rcpp](https://github.com/rmaestre/variableStars/blob/master/src/tools.cpp). An example of function to calculate all differences between pair of element using Armadillo C++ library, iterators and std operattions:
```c
// Calculate all frequences differences
int n = frequences.n_elem;
int diagSupElements = n * (n - 1) / 2;
arma::vec diff(diagSupElements); // Number of elements in the sup. diag.
NumericVector::iterator it_first, it_second, it_diff;
it_diff = diff.begin(); // output iterator
int countElements = 0;
// Double loop (n^2 complexity)
for (it_first = frequences.begin(); it_first < frequences.end(); it_first++) {
for (it_second = it_first; it_second < frequences.end() & it_diff < diff.end(); it_second++) {
if (it_first != it_second) { // Jump same elements
* it_diff =
std::abs( * it_second - * it_first); // Save absolute difference
if ( * it_diff != 0) {
it_diff++; // Increase pointer
countElements++; // Increase elements
}
}
}
}
// Remove unused memory
diff.resize(diagSupElements - (diagSupElements - countElements));
// Return results
return diff;
}
```
However, all code can be call from R easily with the next function
```r
result <- process(
data$frequency,
data$amplitude,
filter = "uniform",
gRegimen = 0,
minDnu = 15,
maxDnu = 95,
dnuValue = -1,
dnuGuessError = 10,
dnuEstimation = TRUE,
numFrequencies = 30,
debug = TRUE
)
```
Deep Neural Networks
------------
This package is also used as feature engineering in [Deep Neural Network application to *Dnu* and *dr* estimation](https://github.com/rmaestre/astroseismologyNN
).
