Dfisher_2022A Documentation

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 >= 1.8.4 (>=1.8.15 is strongly recommended)

Latest PyPI release

$ pip install dfisher_2022a

Common troubleshooting: If installation fails, try to upgrade pip by running pip install --upgrade pip first.

Latest dev-version on GitHub

$ pip install git+https://github.com/ADACS-Australia/dfisher_2022a.git#egg=dfisher_2022a

NOTICE: In the dev-version, a faster version of lmfit (light-lmfit-py) is used. This version provides a fitting method, “fast_leastsq” in addition to other fitting methods available in lmfit(1.0.3). This method can be 2x faster than leastsq. Check dev notes for more details.

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.

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 for 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 encapsulating steps 1-6 is available:

>>> 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.)

Use the faster version of lmfit

If dev-version of this package is installed, which uses a faster version of lmfit as dependency, a faster fitting method is also available, by using method="fast_leastsq"and adding an argument fast=True

>>> cfl = dfisher_2022a.CubeFitterLM(data=p.data, weight=p.weight, x=p.x, model=model, method='fast_leastsq', fast=True) # accept lmfit.Model.fit kwargs
>>> cfl.fit_cube()

In the wrapper function:

>>> fit_lm(cubefile="single_gaussian_muse_size.fits", line="Halpha", model=model, z=0.009, left=20, right=20, snr_threshold=5., method="fast_leastsq", fast=True)

Create custom model

Users can create their own models following the descriptions provided by lmfit. To use fast_leastsq method in the dev version, eval_fast needs to be written as a method of the model. See dev notes of light-lmfit-py for more details.

Special notes for using OzSTAR

The default number of worker processes used in the parallel fitting is os.cpu_count(). In OzSTAR, it’s the total number of cores in one node, which is 36. It can result in runtime error if you also setup cpus-per-task, e.g. cpus-per-task=4 in the sinteractive mode or job submission scripts. In this case, please always specify the value of nprocess when calling fit_lm function or instantiating CubeFitterLM class.