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csromer-0.0.2
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مشاهده بیشترتوضیحات
Compressed Sensing Rotation Measure Reconstructor
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | csromer-0.0.2 |
| نام | csromer |
| نسخه کتابخانه | 0.0.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Miguel Carcamo |
| ایمیل نویسنده | miguel.carcamo@manchester.ac.uk |
| آدرس صفحه اصلی | https://github.com/miguelcarcamov/csromer |
| آدرس اینترنتی | https://pypi.org/project/csromer/ |
| مجوز | GNU GPL |
# CS-ROMER
*Compressed Sensing ROtation MEasure Reconstruction*
Compressed sensing reconstruction framework for Faraday depth spectra.
Please feel free to open an issue if you spot a bug. This is an open source project, and therefore you can fork, make changes and submit a [pull request](https://github.com/miguelcarcamov/csromer/pulls) of any of your additions and modifications.
- This paper explains what is [Faraday rotation measure synthesis](https://www.aanda.org/articles/aa/abs/2005/39/aa2990-05/aa2990-05.html)
- Wikipedia information about [Faraday effect](https://en.wikipedia.org/wiki/Faraday_effect)
## Features
- Simulation of Faraday depth sources
- Subtraction of Galactic RM
- Reconstruction of Faraday depth sources from linearly polarized data
- Reconstruction of Faraday depth sources using Compressed Sensing
- More than 100 wavelet filters provided by `Pywavelets`
This code will run in a Python >= 3.9.7 environment with all the packages installed (see `requirements.txt` file).
## Citing
The paper of this software is under submission but if you use it you can cite it as:
```
@misc{https://doi.org/10.48550/arxiv.2205.01413,
doi = {10.48550/ARXIV.2205.01413},
url = {https://arxiv.org/abs/2205.01413},
author = {Cárcamo, Miguel and Scaife, Anna M. M. and Alexander, Emma L. and Leahy, J. Patrick},
keywords = {Instrumentation and Methods for Astrophysics (astro-ph.IM), Astrophysics of Galaxies (astro-ph.GA), FOS: Physical sciences, FOS: Physical sciences},
title = {CS-ROMER: A novel compressed sensing framework for Faraday depth reconstruction},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
## Installation
The software can be installed as a python package locally or using Pypi
### Locally
`pip install -e .`
### From Pypi
`pip install csromer`
### From Github
`pip install -U git+https://github.com/miguelcarcamov/csromer.git`
## Simulate Faraday sources directly in frequency space
CS-ROMER is able to simulate Faraday depth spectra directly in wavelength-squared space. The classes `FaradayThinSource` and `FaradayThickSource` inherit directly from `Dataset`, and therefore you can directly use them as an input to your reconstruction.
### Thin sources
```python
import numpy as np
from csromer.simulation import FaradayThinSource
# Let's create an evenly spaced frequency vector from 1.008 to 2.031 GHz (JVLA setup)
nu = np.linspace(start=1.008e9, stop=2.031e9, num=1000)
# Let's say that the peak polarized intensity will be 0.0035 mJy/beam with a spectral index = 1.0
peak_thinsource = 0.0035
# The Faraday source will be positioned at phi_0 = -200 rad/m^2
thinsource = FaradayThinSource(nu=nu, s_nu=peak_thinsource, phi_gal=-200, spectral_idx=1.0)
```
### Thick sources
```python
import numpy as np
from csromer.simulation import FaradayThickSource
# Let's create an evenly spaced frequency vector from 1.008 to 2.031 GHz (JVLA setup)
nu = np.linspace(start=1.008e9, stop=2.031e9, num=1000)
# Let's say that the peak polarized intensity will be 0.0035 mJy/beam with a spectral index = 1.0
peak_thicksource = 0.0035
# The Faraday source will be positioned at phi_0 = 200 rad/m^2 and will have a width of 140 rad/m^2
thicksource = FaradayThickSource(nu=nu, s_nu=peak_thicksource, phi_fg=140, phi_center=200, spectral_idx=1.0)
```
### Simulate
Once you have set your source parameters, you can call the `simulate()` function as
```python
thinsource.simulate()
thicksource.simulate()
```
This call will simulate the linealy polarized emission and it will assign the data to the `data` attribute.
### Mixed sources
A thin+thick or mixed source is simply a superposition/sum of a thin source and thick source. Therefore we have overriden the `+` operator in order to sum these two objects.
```python
mixedsource = thinsource + thicksource
```
The result will be a `FaradaySource` object.
### Remove frequency channels randomly as if you were doing RFI flagging
The framework also allows you to randomly remove data with the function `remove_channels` to simulate RFI flagging
```python
# Let's say that we want to randomly remove 20% of the data
mixedsource.remove_channels(0.2)
```
### Adding noise to your simulations
If we want to add random Gaussian noise to our simulation we can simply call the function `apply_noise`
```python
# Let's add Gaussian random noise with mean 0 and standard deviation equal
# to 20% the peak of the signal.
sigma = 0.2*mixedsource.s_nu
mixedsource.apply_noise(sigma)
```
## Reconstruct 1D Faraday sources
To illustrate how to reconstruct Faraday depth signals with CS-ROMER first we will reconstruct the mixed source that we have just constructed
### Dirty Faraday depth spectra
```python
from csromer.reconstruction import Parameter
from csromer.transformers import DFT1D
# We first need to initialize the parameter object that will contain our Faraday depth
# data either in Faraday-depth space or in wavelet space
parameter = Parameter()
# We calculate the cellsize in Faraday depth space using an oversampling factor of 8
# Here parameter.data is set as a complex array of zeros
parameter.calculate_cellsize(dataset=mixedsource, oversampling=8)
# We instantiate our discrete Fourier transform
dft = DFT1D(dataset=mixedsource, parameter=parameter)
# We calculate the dirty Faraday depth spectra
F_dirty = dft.backward(mixedsource.data)
```
### Reconstruct simulated data
```python
from csromer.transformers import NUFFT1D
# We instantiate our non-uniform FFT
nufft = NUFFT1D(dataset=mixedsource, parameter=parameter, solve=True)
# At this point we can use either the parameter data set with zeros or we can
# use the dirty Faraday depth spectra
parameter.data = F_dirty
parameter.complex_data_to_real() # We convert the complex data to real
# You can set the L1 lambda regularization manually or estimate it as
lambda_l1 = np.sqrt(mixedsource.m + 2*np.sqrt(mixedsource.m)) * np.sqrt(2) * np.mean(mixedsource.sigma)
```
### Objective function
```python
from csromer.objectivefunction import L1, Chi2
from csromer.objectivefunction import OFunction
# We instantiate each part of our objective function
chi2 = Chi2(dft_obj=nufft, wavelet=None) # chi-squared
l1 = L1(reg=lambda_l1) # L1-norm regularization
F_obj = OFunction([chi2, l1]) # Whole objective function
f_obj = OFunction([chi2]) # Only chi-squared
g_obj = OFunction([l1]) # Just regularizations
```
### Optimization algorithm
One of the ways to optimize the objective function is to use the FISTA algorithm.
```python
from csromer.optimization import FISTA
# We instantiate our FISTA object as
opt = FISTA(guess_param=parameter, F_obj=F_obj, fx=chi2, gx=g_obj, noise=mixedsource.theo_noise, verbose=False)
# We run the optimization algorithm
obj, X = opt.run()
X.real_data_to_complex() # We convert the data back to complex when the optimization finishes
```
This returns the objective function value `obj` and `X`a `Parameter` instance object. Therefore in this case `X.data` will hold the reconstructed Faraday depth spectra.
At this point you can also access to the model and residual data in wavelength-squared as `mixedsource.model_data` and `mixedsource.residual`, respectively. You can calculate the residuals in Faraday depth space by using the DFT object as
```python
F_residual = dft.backward(mixedsource.residual)
```
### Using discrete or undecimated wavelets
CS-ROMER has about 100 filters to user with discrete wavelet transforms or undecimated wavelet transforms. We use the `Pywavelets` package, for more information please refer to [PyWavelets](https://pywavelets.readthedocs.io/en/latest/index.html). To use the wavelets in cs-romer you can do:
```python
from csromer.dictionaries import DiscreteWavelet, UndecimatedWavelet
# This line instantiates a discrete wavelet
wav = DiscreteWavelet(wavelet_name="coif3", mode="periodization", append_signal=False)
# This line instantiates an undecimated wavelet
wav = UndecimatedWavelet(wavelet_name="sym2", mode="periodization", append_signal=True)
```
The `append_signal` parameter plugs the Faraday depth spectrum to your coefficients resulting in redundancy in your coefficients. If you just want the wavelet coefficients then set `append_signal=False`.
At this point our parameter object data needs to be our coefficients and not our Faraday depth spectra, therefore, we do
```python
parameter.data = F_dirty # Suppose that you set your parameter data with your dirty Faraday depth spectrum
parameter.complex_data_to_real() # We convert the data to real
# Here we do a wavelet decomposition of our Faraday depth space
# We set the coefficients of the decomposition as our parameter data
parameter.data = wav.decompose(parameter.data)
# Don't forget to change your chi-squared
chi2 = Chi2(dft_obj=nufft, wavelet=wav)
```
You might have noticed that at the end of the optimization we will end up with fitted coefficients instead of a Faraday depth spectrum.
Therefore, we need to reconstruct the Faraday depth spectrum from our coefficients doing
```python
X.data = wav.reconstruct(X.data) # We reconstruct the Faraday depth spectrum from coefficients
X.real_data_to_complex() # We convert the real Faraday depth spectrum into complex
```
### Reconstruct a real line of sight data
To reconstruct real data your main script should follow the same workflow. The only difference is that you need to instantiate a `Dataset` object.
```python
from csromer.base import Dataset
# nu is the irregular spaced frequency
# data is the polarized emission
# sigma is the error per channel (this can be an array of ones or rms calculation per image channel)
# alpha is the spectral index at this line of sight
dataset = Dataset(nu=nu, data=data, sigma=sigma, spectral_idx=alpha)
```
### Subtracting the Milky Way RM contribution
We use [S. Hutschenreuter et al.](https://www.aanda.org/articles/aa/full_html/2022/01/aa40486-21/aa40486-21.html) Faraday sky HealPIX image to subtract the galactic RM contribution at a certain position of the sky using the object `FaradaySky`.
Note that you can omit this step, and subtract any RM value that you might find appropiate.
```python
from csromer.faraday_sky import FaradaySky
from astropy.coordinates import SkyCoord
import astropy.units as un
f_sky = FaradaySky()
coord = SkyCoord(ra=173.694*un.deg, dec=48.957*un.deg, frame="fk5")
gal_mean, gal_std = f_sky.galactic_rm(coord.ra, coord.dec, frame="fk5")
dataset.subtract_galacticrm(gal_mean.value)
```
## Reconstruct a cube
We warn the users that not all framework functions are yet implemented to work with data cubes. Therefore, we need to use `numpy` broadcasting and the package `joblib`.
```
```
## Contact
Please if you have any problem, issue or you catch a bug using this software please use the [issues tab](https://github.com/miguelcarcamov/csromer/issues) if you have a common question or you look for any help please use the [discussions tab](https://github.com/miguelcarcamov/csromer/discussions).
نیازمندی
| مقدار | نام |
|---|---|
| >=0.6.11 | proxmin |
| >=1.1.1 | PyWavelets |
| >=3.4.2 | matplotlib |
| >=3.2.1 | prox-tv |
| >=1.20.3 | numpy |
| >=2021.2.0 | pynufft |
| >=1.6.3 | scipy |
| >=4.2.1 | astropy |
| >=1.0.1 | joblib |
| >=0.4.4 | astroquery |
| >=3.1.0 | h5py |
| >=0.6 | astropy-healpix |
نحوه نصب
نصب پکیج whl csromer-0.0.2:
pip install csromer-0.0.2.whl
نصب پکیج tar.gz csromer-0.0.2:
pip install csromer-0.0.2.tar.gz