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توضیحات

Spectral analysis code created for the delivery of the DFisher_2022A ADACS MAP project.

ویژگی	مقدار
سیستم عامل	-
نام فایل	dfisher-2022a-0.1.9
نام	dfisher-2022a
نسخه کتابخانه	0.1.9
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	J. Hu
ایمیل نویسنده	jitinghu@swin.edu.au
آدرس صفحه اصلی	https://github.com/ADACS-Australia/dfisher_2022a
آدرس اینترنتی	https://pypi.org/project/dfisher-2022a/
مجوز	MIT-expat

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](https://github.com/ADACS-Australia/light-lmfit-py/tree/light)) is used. This version provides a fitting method, "fast_leastsq" in addition to other [fitting methods](https://lmfit.github.io/lmfit-py/fitting.html#choosing-different-fitting-methods) available in `lmfit(1.0.3)`. This method can be 2x faster than `leastsq`. Check [dev notes](https://github.com/ADACS-Australia/light-lmfit-py/tree/light) 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](https://lmfit.github.io/lmfit-py/model.html#model-class-methods) 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](https://lmfit.github.io/lmfit-py/model.html). 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](https://github.com/ADACS-Australia/light-lmfit-py/tree/light) for more details.

نیازمندی

مقدار	نام
==4.2.0)	Sphinx
==1.0.0)	sphinx-rtd-theme
>=1.19,<2.0	numpy
>=1.4.2,<2.0.0	pandas
>=3.5,<4.0	mpdaf
>=3.7.0,<4.0.0	tables
>=1.0.3,<2.0.0	lmfit
>=4.64.0,<5.0.0	tqdm

زبان مورد نیاز

مقدار	نام
>=3.8,<3.10	Python

نحوه نصب

نصب پکیج whl dfisher-2022a-0.1.9:

pip install dfisher-2022a-0.1.9.whl

نصب پکیج tar.gz dfisher-2022a-0.1.9:

pip install dfisher-2022a-0.1.9.tar.gz