Spectral analysis code created for the delivery of the DFisher_2022A ADACS MAP project.
Project description
Dfisher_2022A
This project is being developed in the course of delivering the DFisher_2022A ADACS Merit Allocation Program project.
Installation
Pre-requirement:
- python >=3.8 <3.10
- HDF5
- C compiler
Installing with pip
$python -m pip git+https://github.com/ADACS-Australia/dfisher_2022a.git#egg=dfisher_2022a
Getting Started
Import the package
>>> import dfisher_2022a
Read in data cube
>>> cube = dfisher_2022a.ReadCubeFile("single_gaussian_muse_size.fits").cube
If a separate variance file is provide:
>>> cube = dfisher_2022a.ReadCubeFile("single_gaussian_muse_size.fits", "muse_var.fits").cube
Prepare data for fitting
>>> p = dfisher_2022a.ProcessedCube(cube, z=0.009, snr_threshold=5.)
1. De-redshift the cube
>>> p.de_redshift()
2. Select fitting region for a given line
>>> p.select_region("Halpha", left=20, right=20)
Keywords left
and right
set the wavelength cuts around the given line on both sides, e.g. the selected region is [line-left, line+right]. If this region exceeds the cube wavelength range, a nearest value within the cube will be used instead.
3. Filter the cube by SNR threshold
>>> p.get_snrmap()
Select fitting model
>>> model = dfisher_2022a.Lm_Const_1GaussModel
A single Gaussian model is available within this package. Users can customize their own models following this note.
Fit the cube
>>> cfl = dfisher_2022a.CubeFitterLM(data=p.data, weight=p.weight, x=p.x, model=model, method='leastsq') # accept lmfit.Model.fit kwargs
>>> cfl.fit_cube()
Additional keyword arguments from lmfit.Model.fit can be passed to the class object as well.
Save output
>>> out = dfisher_2022a.ResultLM()
>>> out.get_output(p) # get attributes from ProcessedCube object
>>> out.get_output(cfl)
>>> out.save()
An out
directory will be generated in the current directory.
Read output
In the .out
folder:
result.h5
fitdata/
where result.h5
stores the fitting result, and fitdata/
contains processed data used for fitting.
To read result.h5
file:
>>> import pandas as pd
>>> store = pd.HDFStore("result.h5")
>>> store.keys()
['/Halpha_Const_1GaussModel']
>>> df = store.get("Halpha_Const_1GaussModel")
Check available lines
>>> dfisher_2022a.EmissionLines
{'Halpha': 6562.819, 'Hb4861': 4861.333, 'Hdelta': 4101.742, ...
The line information is included in emission_lines.py
. Users can customize this file (e.g. adding more lines or updating the wavelength) before importing this package.
A wrapped approach
A wrapper function is available, which encapsulates steps 1-6.
>>> from dfisher_2022a import fit_lm
>>> model = dfisher_2022a.Lm_Const_1GaussModel
>>> fit_lm(cubefile="single_gaussian_muse_size.fits", line="Halpha", model=model, z=0.009, left=20, right=20, snr_threshold=5.)
Project details
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