diff --git a/docs/conf.py b/docs/conf.py index 72e24e5..4738363 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -21,16 +21,11 @@ sys.path.insert(0, os.path.abspath('../')) -#if "TRAVIS" in os.environ: -package_path = os.path.abspath('../..') -os.environ['PYTHONPATH'] = ':'.join((package_path, - os.environ.get('PYTHONPATH', ''))) - # -- Project information ----------------------------------------------------- project = 'ScopeSim_templates' -copyright = '2021, A* Vienna' +copyright = '2024, A* Vienna' author = 'A* Vienna' # The short X.Y version @@ -48,15 +43,6 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -#extensions = [ -# 'sphinx.ext.autodoc', -# 'sphinx.ext.doctest', -# 'sphinx.ext.intersphinx', -# 'sphinx.ext.todo', -# 'sphinx.ext.coverage', -# 'sphinx.ext.mathjax', -# 'sphinx.ext.viewcode', -#] extensions = [ @@ -66,11 +52,10 @@ 'sphinx.ext.inheritance_diagram', 'sphinx.ext.mathjax', 'sphinx.ext.extlinks', -# 'sphinx.ext.linkcode', -# 'jupyter_sphinx', 'sphinx.ext.doctest', + 'sphinx_copybutton', + 'myst_nb', 'numpydoc', - 'nbsphinx', ] # -- Options for intersphinx extension --------------------------------------- @@ -118,10 +103,19 @@ # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' -source_encoding = 'utf-8-sig' +source_suffix = { + ".rst": "restructuredtext", + ".ipynb": "myst-nb", + ".myst": "myst-nb", + ".md": "myst-nb", +} +source_encoding = 'utf-8' + +# MyST NB stuff +nb_execution_timeout = 3600 # [s] +nb_execution_mode = "auto" +# nb_execution_excludepatterns = [] + # The master toctree document. master_doc = 'index' @@ -147,41 +141,38 @@ pygments_style = 'default' #'sphinx' -# -- Options for HTML output --------------------------------------------------- -html_theme = "sphinx_rtd_theme" -html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] - - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -# html_theme = 'alabaster' +# -- Options for HTML output ---------------------------------------------- -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -# html_theme_options = {} +html_theme = "sphinx_book_theme" +html_theme_options = { + "repository_url": "https://github.com/AstarVienna/ScopeSim_Templates", + "use_repository_button": True, + "use_download_button": True, + "home_page_in_toc": True, +} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +html_static_path = ['docs/_static'] +# html_logo = 'docs/_static/logos/T_favicon.png' +html_title = "ScopeSim Templates" -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# The default sidebars (for documents that don't match any pattern) are -# defined by theme itself. Builtin themes are using these templates by -# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', -# 'searchbox.html']``. -# -# html_sidebars = {} +# Add local templates path to modify autosummary templates +# templates_path = ['_templates'] + +# Static files to copy after template files +html_static_path = ['_static'] +html_sidebars = { + "**": [ + "navbar-logo.html", + "search-field.html", + "sbt-sidebar-nav.html", + ] +} +html_sourcelink_suffix = "" # -- Options for HTMLHelp output --------------------------------------------- @@ -235,7 +226,7 @@ # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'ScopeSim_templates', 'ScopeSim Templates Documentation', - author, 'ScopeSim_templates', + author, 'ScopeSim_templates', 'Helper functions for creating ScopeSim Source objects.', 'Miscellaneous'), ] diff --git a/docs/contributions.rst b/docs/contributions.rst deleted file mode 100644 index e69de29..0000000 diff --git a/docs/generated/scopesim_templates.calibration.empty_sky.rst b/docs/generated/scopesim_templates.calibration.empty_sky.rst deleted file mode 100644 index 5c72262..0000000 --- a/docs/generated/scopesim_templates.calibration.empty_sky.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.calibration.empty\_sky -========================================== - -.. currentmodule:: scopesim_templates.calibration - -.. autofunction:: empty_sky \ No newline at end of file diff --git a/docs/generated/scopesim_templates.calibration.flat_field.rst b/docs/generated/scopesim_templates.calibration.flat_field.rst deleted file mode 100644 index 6c01966..0000000 --- a/docs/generated/scopesim_templates.calibration.flat_field.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.calibration.flat\_field -=========================================== - -.. currentmodule:: scopesim_templates.calibration - -.. autofunction:: flat_field \ No newline at end of file diff --git a/docs/generated/scopesim_templates.calibration.lamp.rst b/docs/generated/scopesim_templates.calibration.lamp.rst deleted file mode 100644 index 66db61e..0000000 --- a/docs/generated/scopesim_templates.calibration.lamp.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.calibration.lamp -==================================== - -.. currentmodule:: scopesim_templates.calibration - -.. autofunction:: lamp \ No newline at end of file diff --git a/docs/generated/scopesim_templates.extragalactic.elliptical.rst b/docs/generated/scopesim_templates.extragalactic.elliptical.rst deleted file mode 100644 index b1be247..0000000 --- a/docs/generated/scopesim_templates.extragalactic.elliptical.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.extragalactic.elliptical -============================================ - -.. currentmodule:: scopesim_templates.extragalactic - -.. autofunction:: elliptical \ No newline at end of file diff --git a/docs/generated/scopesim_templates.extragalactic.galaxy.rst b/docs/generated/scopesim_templates.extragalactic.galaxy.rst deleted file mode 100644 index 2a7acb9..0000000 --- a/docs/generated/scopesim_templates.extragalactic.galaxy.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.extragalactic.galaxy -======================================== - -.. currentmodule:: scopesim_templates.extragalactic - -.. autofunction:: galaxy \ No newline at end of file diff --git a/docs/generated/scopesim_templates.extragalactic.galaxy3d.rst b/docs/generated/scopesim_templates.extragalactic.galaxy3d.rst deleted file mode 100644 index 9577efe..0000000 --- a/docs/generated/scopesim_templates.extragalactic.galaxy3d.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.extragalactic.galaxy3d -========================================== - -.. currentmodule:: scopesim_templates.extragalactic - -.. autofunction:: galaxy3d \ No newline at end of file diff --git a/docs/generated/scopesim_templates.extragalactic.spiral_two_component.rst b/docs/generated/scopesim_templates.extragalactic.spiral_two_component.rst deleted file mode 100644 index 5d6e47f..0000000 --- a/docs/generated/scopesim_templates.extragalactic.spiral_two_component.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.extragalactic.spiral\_two\_component -======================================================== - -.. currentmodule:: scopesim_templates.extragalactic - -.. autofunction:: spiral_two_component \ No newline at end of file diff --git a/docs/generated/scopesim_templates.misc.point_source.rst b/docs/generated/scopesim_templates.misc.point_source.rst deleted file mode 100644 index ce22357..0000000 --- a/docs/generated/scopesim_templates.misc.point_source.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.misc.point\_source -====================================== - -.. currentmodule:: scopesim_templates.misc - -.. autofunction:: point_source \ No newline at end of file diff --git a/docs/generated/scopesim_templates.misc.source_from_array.rst b/docs/generated/scopesim_templates.misc.source_from_array.rst deleted file mode 100644 index bcad170..0000000 --- a/docs/generated/scopesim_templates.misc.source_from_array.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.misc.source\_from\_array -============================================ - -.. currentmodule:: scopesim_templates.misc - -.. autofunction:: source_from_array \ No newline at end of file diff --git a/docs/generated/scopesim_templates.misc.source_from_cube.rst b/docs/generated/scopesim_templates.misc.source_from_cube.rst deleted file mode 100644 index e83ebac..0000000 --- a/docs/generated/scopesim_templates.misc.source_from_cube.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.misc.source\_from\_cube -=========================================== - -.. currentmodule:: scopesim_templates.misc - -.. autofunction:: source_from_cube \ No newline at end of file diff --git a/docs/generated/scopesim_templates.misc.source_from_file.rst b/docs/generated/scopesim_templates.misc.source_from_file.rst deleted file mode 100644 index 46d73d4..0000000 --- a/docs/generated/scopesim_templates.misc.source_from_file.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.misc.source\_from\_file -=========================================== - -.. currentmodule:: scopesim_templates.misc - -.. autofunction:: source_from_file \ No newline at end of file diff --git a/docs/generated/scopesim_templates.misc.source_from_imagehdu.rst b/docs/generated/scopesim_templates.misc.source_from_imagehdu.rst deleted file mode 100644 index 050f2fd..0000000 --- a/docs/generated/scopesim_templates.misc.source_from_imagehdu.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.misc.source\_from\_imagehdu -=============================================== - -.. currentmodule:: scopesim_templates.misc - -.. autofunction:: source_from_imagehdu \ No newline at end of file diff --git a/docs/generated/scopesim_templates.misc.source_from_imagehdu_with_flux.rst b/docs/generated/scopesim_templates.misc.source_from_imagehdu_with_flux.rst deleted file mode 100644 index c470c5a..0000000 --- a/docs/generated/scopesim_templates.misc.source_from_imagehdu_with_flux.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.misc.source\_from\_imagehdu\_with\_flux -=========================================================== - -.. currentmodule:: scopesim_templates.misc - -.. autofunction:: source_from_imagehdu_with_flux \ No newline at end of file diff --git a/docs/generated/scopesim_templates.misc.uniform_source.rst b/docs/generated/scopesim_templates.misc.uniform_source.rst deleted file mode 100644 index 394f90a..0000000 --- a/docs/generated/scopesim_templates.misc.uniform_source.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.misc.uniform\_source -======================================== - -.. currentmodule:: scopesim_templates.misc - -.. autofunction:: uniform_source \ No newline at end of file diff --git a/docs/generated/scopesim_templates.stellar.cluster.rst b/docs/generated/scopesim_templates.stellar.cluster.rst deleted file mode 100644 index 93a3426..0000000 --- a/docs/generated/scopesim_templates.stellar.cluster.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.stellar.cluster -=================================== - -.. currentmodule:: scopesim_templates.stellar - -.. autofunction:: cluster \ No newline at end of file diff --git a/docs/generated/scopesim_templates.stellar.star.rst b/docs/generated/scopesim_templates.stellar.star.rst deleted file mode 100644 index 2d35c9f..0000000 --- a/docs/generated/scopesim_templates.stellar.star.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.stellar.star -================================ - -.. currentmodule:: scopesim_templates.stellar - -.. autofunction:: star \ No newline at end of file diff --git a/docs/generated/scopesim_templates.stellar.star_field.rst b/docs/generated/scopesim_templates.stellar.star_field.rst deleted file mode 100644 index 4af116d..0000000 --- a/docs/generated/scopesim_templates.stellar.star_field.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.stellar.star\_field -======================================= - -.. currentmodule:: scopesim_templates.stellar - -.. autofunction:: star_field \ No newline at end of file diff --git a/docs/generated/scopesim_templates.stellar.star_grid.rst b/docs/generated/scopesim_templates.stellar.star_grid.rst deleted file mode 100644 index d913312..0000000 --- a/docs/generated/scopesim_templates.stellar.star_grid.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.stellar.star\_grid -====================================== - -.. currentmodule:: scopesim_templates.stellar - -.. autofunction:: star_grid \ No newline at end of file diff --git a/docs/generated/scopesim_templates.stellar.stars.rst b/docs/generated/scopesim_templates.stellar.stars.rst deleted file mode 100644 index 14bd3bc..0000000 --- a/docs/generated/scopesim_templates.stellar.stars.rst +++ /dev/null @@ -1,6 +0,0 @@ -scopesim\_templates.stellar.stars -================================= - -.. currentmodule:: scopesim_templates.stellar - -.. autofunction:: stars \ No newline at end of file diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..cd8e58f --- /dev/null +++ b/docs/index.md @@ -0,0 +1,110 @@ +--- +substitutions: + logo: |- + ```{image} https://raw.githubusercontent.com/AstarVienna/astarvienna.github.io/main/logos/star_small_t.png + :align: middle + :height: 30px + ``` +--- + +```{raw} html + +``` + +```{image} _static/logos/logo_long_scopesim_templates_t.png +:align: center +:alt: Welcome to the ScopeSim_Templates Documentation! +:width: 600 px +``` + +# ScopeSim Templates + +Another tool from the [A\* Vienna software team](https://astarvienna.github.io/) + +A library of templates and helper functions for creating +{class}`scopesim.source.source.Source` objects that can be used to run `ScopeSim` simulations. + +In short {class}`scopesim.source.source.Source` objects contain a description of the spatial and +spectral information of the source. For more information see [here](./source_object.md). + +## Installation + +This package has been released on PyPi: + +```bash +pip install scopesim_templates +``` + +## From basic to advanced helper functions + +ScopeSim Templates is a python package, and is therefore by nature infinitely extendable. + +As it is impossible for us to know all the details about your specific science case, we provide a +small selection of basic objects (star cluster, elliptical galaxy, etc). +Feel free to start with these to get started with ScopeSim. + +However if your needs outgrow the basic objects, we encourage you to extended the objects to fit your +specific science case. In this case **we strongly encourage you to get in contact with us adding your code +in the form of a subpackage**. You can do this either by opening an issue on Github, or by emailing one of the developers. + +## Available subpackages + +Documentation for all the helper functions contained in each package can be found in the API documentation for each package. + +- `stellar`: + : - {func}`star`: Places a single star on the field + - {func}`stars`: Places a list of stars on the field + - {func}`cluster`: Creates an age=0 cluster with a user selected mass + - {func}`stars_field`: Creates field of stars with random positions + - {func}`star_grid`: Creates a field of stars with regular positions +- `extragalactic` + : - {func}`galaxy`: A simple sersic model with a user selected SED from the `speXtra` database + - {func}`galaxy3D`: A more complex model that includes a velocity field and velocity dispersion field + - {func}`spiral_two_component`: Simple two component model with an outer spiral young SED and an old SED bulge + - {func}`elliptical`: Another sersic model using the Brown SEDs +- `misc` + : - {func}`point_source`: similar to {func}`star` but using any SED from the `speXtra` database + - {func}`uniform_source`: Creates a uniform source with any SED from `speXtra` + - {func}`source_from_image_hdu`: creates a source from an `ImageHDU` with an arbitrary flux and scale + - {func}`source_from_imagehdu_with_flux`: creates a source from an `ImageHDU` where the flux/pixel is known + - {func}`source_from_file`: Load the source from a fits file. Depending on the characteristics other + : functions may be more suitable + - {func}`source_from_array`: General function to create a source from a 2D `numpy` array + - {func}`source_from_cube`: Wrapper to create a source from a 3D datacube +- `calibration`: + : - {func}`empty_sky`: To simulate a sky without no other sources + - {func}`lamp`: Simulates a calibration lamp, i.e. a homogenous source with emissions lines + - {func}`flat_field`: Simulates a flat field + +## Contact + +If you find an issue with ScopeSim Templates, please let us know via the +[Github issues page](https://github.com/AstarVienna/ScopeSim_Templates/issues) + +## Contents + +```{toctree} +:maxdepth: 2 + +notebooks/starting.md +notebooks/stellar.md +notebooks/extragalactic.md +source_object +``` + +## API Reference + +```{eval-rst} +.. autosummary:: + :toctree: _autosummary + :recursive: + :caption: Package Contents + + scopesim_templates.stellar + scopesim_templates.extragalactic + scopesim_templates.calibration.calibration + scopesim_templates.misc.misc +``` diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index b1ec95e..0000000 --- a/docs/index.rst +++ /dev/null @@ -1,104 +0,0 @@ -.. raw:: html - - - -.. image:: _static/logos/logo_long_scopesim_templates_t.png - :width: 600 px - :alt: Welcome to the ScopeSim_Templates Documentation! - :align: center - -|logo| Another tool from the `A* Vienna software team `_ - -.. |logo| image:: https://raw.githubusercontent.com/AstarVienna/astarvienna.github.io/main/logos/star_small_t.png - :height: 30px - :align: middle - - -A library of templates and helper functions for creating -:class:`scopesim.source.source.Source` objects that can be used to run `ScopeSim` simulations. - -In short :class:`scopesim.source.source.Source` objects contain a description of the spatial and -spectral information of the source. For more information see :ref:`here `. - -Installation ------------- - -This package has been released on PyPi:: - - pip install scopesim_templates - - -From basic to advanced helper functions ---------------------------------------- -ScopeSim Templates is a python package, and is therefore by nature infinitely extendable. - -As it is impossible for us to know all the details about your specific science case, we provide a -small selection of basic objects (star cluster, elliptical galaxy, etc). -Feel free to start with these to get started with ScopeSim. - -However if your needs outgrow the basic objects, we encourage you to extended the objects to fit your -specific science case. In this case **we strongly encourage you to get in contact with us adding your code -in the form of a subpackage**. You can do this either by opening an issue on Github, or by emailing one of the developers. - - -Available subpackages ---------------------- - -Documentation for all the helper functions contained in each package can be found in the API documentation for each package. - -* ``stellar``: - * :func:`star`: Places a single star on the field - * :func:`stars`: Places a list of stars on the field - * :func:`cluster`: Creates an age=0 cluster with a user selected mass - * :func:`stars_field`: Creates field of stars with random positions - * :func:`star_grid`: Creates a field of stars with regular positions - -* ``extragalactic`` - * :func:`galaxy`: A simple sersic model with a user selected SED from the ``speXtra`` database - * :func:`galaxy3D`: A more complex model that includes a velocity field and velocity dispersion field - * :func:`spiral_two_component`: Simple two component model with an outer spiral young SED and an old SED bulge - * :func:`elliptical`: Another sersic model using the Brown SEDs - -* ``misc`` - * :func:`point_source`: similar to :func:`star` but using any SED from the ``speXtra`` database - * :func:`uniform_source`: Creates a uniform source with any SED from ``speXtra`` - * :func:`source_from_image_hdu`: creates a source from an ``ImageHDU`` with an arbitrary flux and scale - * :func:`source_from_imagehdu_with_flux`: creates a source from an ``ImageHDU`` where the flux/pixel is known - * :func:`source_from_file`: Load the source from a fits file. Depending on the characteristics other - functions may be more suitable - * :func:`source_from_array`: General function to create a source from a 2D ``numpy`` array - * :func:`source_from_cube`: Wrapper to create a source from a 3D datacube - -* ``calibration``: - * :func:`empty_sky`: To simulate a sky without no other sources - * :func:`lamp`: Simulates a calibration lamp, i.e. a homogenous source with emissions lines - * :func:`flat_field`: Simulates a flat field - - - -Contact -------- -If you find an issue with ScopeSim Templates, please let us know via the -`Github issues page `_ - - -.. toctree:: - :maxdepth: 2 - - notebooks/starting.ipynb - notebooks/stellar.ipynb - notebooks/extragalactic.ipynb - source_object - modules - - - -More... -======= - -.. toctree:: - :maxdepth: 1 - :titlesonly: diff --git a/docs/modules.rst b/docs/modules.rst deleted file mode 100644 index bad02ca..0000000 --- a/docs/modules.rst +++ /dev/null @@ -1,68 +0,0 @@ -.. _scopesim_templates-api: - -************************** -ScopeSim_Templates Package -************************** - - -``scopesim.stellar`` module -=========================== - -Templates to simulate stellar sources - -.. autosummary:: - :nosignatures: - :toctree: generated/ - - scopesim_templates.stellar.star - scopesim_templates.stellar.stars - scopesim_templates.stellar.star_field - scopesim_templates.stellar.star_grid - scopesim_templates.stellar.cluster - -``scopesim_templates.extragalactic`` module -=========================================== - -Templates to simulate extragalactic sources - -.. autosummary:: - :nosignatures: - :toctree: generated/ - - scopesim_templates.extragalactic.galaxy - scopesim_templates.extragalactic.galaxy3d - scopesim_templates.extragalactic.spiral_two_component - scopesim_templates.extragalactic.elliptical - -``scopesim_templates.misc`` module -================================== - -Templates that could be used to simulate more general sources - -.. autosummary:: - :nosignatures: - :toctree: generated/ - - scopesim_templates.misc.point_source - scopesim_templates.misc.uniform_source - scopesim_templates.misc.source_from_file - scopesim_templates.misc.source_from_array - scopesim_templates.misc.source_from_imagehdu - scopesim_templates.misc.source_from_imagehdu_with_flux - scopesim_templates.misc.source_from_cube - - -``scopesim_templates.calibration`` module -========================================== - -Simple templates that could be used to simulate calibration frames. Make sure to turn off the corresponding -effects during the simulation - -.. autosummary:: - :nosignatures: - :toctree: generated/ - - scopesim_templates.calibration.empty_sky - scopesim_templates.calibration.flat_field - scopesim_templates.calibration.lamp - diff --git a/docs/notebooks/extragalactic.ipynb b/docs/notebooks/extragalactic.ipynb deleted file mode 100644 index 5b05768..0000000 --- a/docs/notebooks/extragalactic.ipynb +++ /dev/null @@ -1,173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d0f18d8e-b884-4e56-b80d-8d0d3a91680f", - "metadata": {}, - "source": [ - "# Extragalactic" - ] - }, - { - "cell_type": "markdown", - "id": "c78bb2d1-221c-4ca3-bff7-1fe884c2251f", - "metadata": {}, - "source": [ - "A few functions that can also helpt to create extragalactic objects" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4788a32d-652e-4b17-a3a4-ab9db4818d34", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from scopesim_templates.extragalactic import galaxy\n" - ] - }, - { - "cell_type": "markdown", - "id": "b67de4ed-7163-45ed-befc-39cc809c9ec9", - "metadata": {}, - "source": [ - "The function `galaxy` will generate a sersic profile with user provided parameters. The function can accept any sed in the [speXtra](https://spextra.readthedocs.io/en/latest/) package. \n", - "\n", - "It must be noted that the pixel scale is not related to the pixel scale of the final simulation but to the pixel scale (in arcsec) of the generated image. It is recommended to be fine enough to well sample the profile of the galaxy. \n", - "\n", - "The SED and the selected filter must overlap but an error will be thrown if they don't. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ccb3ae1d-5436-42ac-b1d9-f1faf80a6885", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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l1WsYDdsgAqVuMRF4S4eSMLREYWN+YDGiROK0dWN7dRJz9g4wXB8SqLg0m0O5jy3LjhEC8JOF7T9DppJCk+FDzPo0egXqtdDUp5YwjBABANVom7YXZ2NTZt3ajK9gk2VDiUK3Y55r/ADJUBUc3qagM9q3HhosUNja1iGFrtyZ1VsOKETikIEVInTf9fAgl51HBOM5JwJp0FxPy9IhFNLRZvw9hAUDRFiwxt6BoouqxClxrIto2JJE1NlBKfLyDO5ym/wuDDYrz8ghfmg6MkKIkEHxvbw2NU8QIYJMT5AI+Dl7iq2lw0Ifl2FYfdmwhhqRqE0c8ogQi8C+SGDn8PIBmZxKCKLO+N6V8Vkt11skCwcZecBpZxCCGRJY373wQBIBOURQ9FESQa6fUJDCAQoxaYQhZLicNeNrWMsbCOlef+9AJubWngvOkgrVlluWdNtA8lUPQYtTrf40AhDG4a4SaNCIgXJCKHMBYluwkCln+ula1TppqAuIwA03lFBgaX4gw/M8LNgnCewBTYQQ06UZfa6HinrV+Fnd8rBAJwMu668IDHWU1xXnM4iAnytEkI+J9EThnPxANl6dJHYfFgwIhAX7JoGdvT8QgfoAK7rUmU3U67mCgGegHVpbhwxkTsJLBKp5ApUwGolAmdXzfvN7BiCUKOzkhG4u3/KLQ1uGBbKPuXsHKtg3CXjQ+OEUpKHMQsW5Ap8QSgIYv3ukwOq5TIQo1JBANfYyRBj06SGAIIKizQIiqCwdhhKFvE1LfoDjVGFBIG8Q2jtwXfYJeLH+OZcQZ8FI7GnurllnzH6e0WuohgVQypVxmF4BtHMqdDQRgbh+kwjk2CB1aEYu5KL5gVpYsHZ4YPa74t6BoWrm0K4PzkQg8bCASjnHONXylgOCENjhJgJJeAUoZfNzglw5qBLBcD/UNuwGRFYMluYHGEJhwTgeQS57TBJKdVWJU8PbK6DtGjxz3qBq7K58/lCasEhBznAzDo0Q3O9gBlcYtnZuEAGEEcp3DWq6KonCTmZqN9Ur9/y6hAVLk4QG9kcCa2DmpqMowplh6+FUmtfdcfZdngsiCucGhL668fO6yZhmEYFi8EuJYI38QFY/JyzYClZY4HolMW9gvyRwpr0CixF8GFQvIEIAw6cw3Jqxq8SgHdDJQCMGHh7k5fn5cF8WEQHy67PPNQPHNPuq95u3mcabfXI5b2beyhvwIMMCbyehgv2SwICgwZ81eej8nSLhgirjEQAqRi/7qRi7Sgiibz8ZSEb51LbT004E2fhHncygrUShYuCzwoKxTtFn1c0xZA01IpmbJJSi80a3DU41w88hjNZfE7I7j597BDB+N4y6dtRmfy8/oJ+vSwRTPTNWhQgAqInC4tz7aTKwfrYMC1q9gQgiScKaiuYWp4AS06uGW/vBFkYqFsFs4UEUM4nzd6m6uB4BQAkFFEPWDosQPDIY9NvnVBIEKkQAnQiiS4er5Ad431o9/+Ry5wgL5iQJr+W7A0uw53xBj2K2sGavAAGM34UheYdsoxFCjSDsc258YqwaEYh6fu2hpUN454rh9n1L2ZOEBWtjTpJQwb5JQDPoDX9cxPMKqHXFQfw9at6BmTuIEABywx/LjaMgBcf4LTLwZ/xlRFDsIWDXyO/brPyAt2wI1vfCsEDFOXYSXqt3B5bM4OeY/WtGPqeNMTOZCTr+cIOVST2y3CEF1fihE0Fm1OJcrhxEiWC6TmaYwUQhgLb8APJxhMICBm+3YHjvQBRrJAmt6raRbA81dlfKqklEPnMn43wJ1tLjEUPlvJjJhzqFDKI5A/VdAY0YTKM2YnyvjamHGeaBlfHrGO9JID+A/HxWWOAlAluShLLNGklCoTOaJNwdCXioJfGak3xeaLGCkWvegfZgeB6BFTcX+wyEXLEaYJRrSUBtJldDAPFd9Q6GsXpEwI2xIAdCbuzS8BSvQrmvTWEB72NuWACnbqsk4UxvYL8kEI3BV5vZd/ZDpuK7mvCiXJYbfG3G11x7fVWAcmMHO9eIAXEiyHUphi3lrERhcX+wLCzgfTj1hVwWlhh1J04Srva/CM+KOclBY2lwNzsNjb+L9Ag0QyjOueEN34Vh1w6NELKyQ31noBY6+EavkYq+dFhNFGbkww0xuGy4QliQIRIWnCJJOA7Hl9kXCXhGWjPgRgN3QwdZd4q9BAOUctM9Ngyx1RtIB0dPVkbdw1kzfuTlqjFL+eE6qbJiALiJwu57JSxQzhetFnC5JWHBXFhhwXXOCURn7LVm9tZcQlO/3t/BIQItT2Bvmuk+CuPn51FCCJKBFx6EiABl3XQ9zoqBVmbcNx4WuF6VFhYwmWpYMGfvgIa53kAAnjdQJQEieg0R/TERPUlETxDRz/XlLyOiTxPRl/vPl7I27yeip4joS0T0lvBIJaI7B636rVcIrH4bYHoEgE4Sxcwp9Ajjbw4Jhiz8wZ7tJREUxhwhAj5uKQOlbiyjcXyyLG/HjVQSBZXnMiyQeRempxoWeAnEop1v6NXVglqScKWcwF0Av5BS+pcAfhTAe4jodQDeB+AzKaUHAXym/46+7hEArwfwVgC/QUQX1V4WhQL1izAh8gvSmE+SR3D+TsUM1strxqLO6CjrPA+gzAfkxpgbOhUkMYcIdBlpuCjlW8KCgwwRSD2fFRYwzNo7MActbZfmBFJKz6SU/qI//zaAJwG8CsDDAD7Si30EwNv784cBfDSl9FxK6SsAngLwpvCAo0bXkBxcFa1qg/KuR4Dc4EYIMrDCgpA3cCjlC5I4AIWhH8pNQa1EYM7+tUQhUO4fUO6LSpjingGYFxYAwRlfqRv7NchhbpJQegMVNOUEiOgHAfwIgD8D8MqU0jNARxQAXtGLvQrA11izp/syqetRIrpNRLfv3P37sq85m4bMl4SCKwSyLmrwa24cChC8+iCTqDOMOeQNaCGBIICSOAJEgLLeXDocZRvzA0VfNJ1HwgKINk5YwGWjScLVXzeuIRAWhEmAiF4M4PcB/HxK6VueqFJWmElK6bGU0kMppYduXb7I1jYjLxBFc0Jw418sGqCGAAG5SI7APQxvQEsW6ucVIlCN3GmnyoqbMJCRKJNhQaFrPBcGGn23gOuZmyTUsLY3EECIBIjoPnQE8DsppT/oi79BRA/09Q8AeLYvfxrAa1jzVwP4emg0w0wc9ALyejbeSHJQoCCEIEG4Lx0tJKsifrb+rsJwvBwBn+3HQzH0ehhgGXudCHQj19t5+YFZYcHcTUSFDCn1g0EqZadcMpSohAWR1QEC8CEAT6aUfo1VfRLAu/rzdwH4BCt/hIjuJ6LXAngQwGcbhx3DkjcKW413xu8ZbAHNuLR6YJKzkn1qPuAgymQYcJBhQDmeFiIwvR6FCFQvoDEsAND8boE8b04ScpxiybDRG7gMyLwZwM8A+DwRPd6X/SKAXwHwMSJ6N4C/AfAOAEgpPUFEHwPwRXQrC+9JKV1VezmioCRKqbyRKRUXl8nxenbOZQq9x+TGZOo4HFDq+WFlQregusr8OzE5TwdrO17D1LyTSd33xMuhlREIyajLy6ZP1kamZwhj7dhmcByprzkgD9l4h0BHJEisXaljlD1g9CK53Ni5HBvrAyll7ccyrQ6yHXXP4/B9LTjeQJUEUkp/Cvtx/gmjzQcBfDAyNqVxyWAqQfgPdRSFHtk/f3pZGaGNGE4GZYbVZlATzEAyMuAGgxMRAW/D87/k1PVEkEBTEpgkaQA0GNtwP5iRj20PAI6dMQ4k1D0fXIbrEcaOnlxE2XQddt0IoTMdCMRJoiAYQSIBMtnljkEA+sBr7xEszAssBSWsy95uZzBdbo4ipiURCsjjkOsxcwJKv2uFBuH8AMo2/FqbwgLk7ntTWCDv86hfKQstJ1L+fe5kE2y/DxJo/XnxtQxNqIkkB80VglPlB8h44Bi88EBL/smjyAtwnUNOgCcWWZ4gTASNUPMDBeHROMYMUpb0/QeZ/ICGJOGgO/scxiXKzLpAjmDN7cTAXkhgwGBg6ozvNzX3C0T3CFR2Dna6rL79sa2FiPG4+QHN2BVDBiFLCHIDd2f0gRxqRAClbfGZE0f1WiUhzFwtWMUbqC0ZCm9gzluGBWrlDrnsiwQascT4mjYNefJzxrAyaVihAACVADyPoHijUIYCsIzc/8wMLNyWnPr8Ys2wAIGwYCgz5BcvGaJSJusC7n/hDUgdDd7Abkgg7PZvkBeokonpAdStucsT8M9Uli+B97eWxo5YOCBfIpLuP/cQ1OVCJzSI5gem8TPjUoigGhZIoy1k4e4daPUGVNmNvIFkGbqVWzCwGxIYoe4QjJFBJCQo9Ugdfj2ALi/gGW9Ko5GHMJMM1PCAjHIIo+N7BQ4pP7iBO0nBQaeaEzCIwEsUmtdWmdSaw4KxDTfUSpIw8l7BlhuIPCxMIO6PBAYE8wJLfgugIJcFeYGhbtHMvtArkMavGu543g12NHp+9GQAmggh8wS4ByD68WZ+ANCIQI53SVgg+9Dq8tm+MUloeQajjF9/su3EVnsFuyKBk4YERd/1Pjz3f7Xk4FwikX9j7cFmBFDkBQ7iGGd4RhbW7kHoHkHxZqLxwtEa+QFz5YGgewNCtggLoHgDY1vxr8xIk0cmD2Bbb8DSca3+7wDHgpAgDKdp28pAWjyDb4rCKFI5U8ttxNxg2fsFHRkg9wQyIxN6FQMPJwoXXG+S+hlUD8FIMnKdI9ZcMuQ4xctFBvZJAgNmhASzlgpnJgZDxp8lBYfPQEMtFxFppj2YmnGJGVXzBrKlw/F8CBXKmX/oX3sXwQ8P9PHN8gaK+0EZUeU3tOINCCJZfQPR1t5AEPshgTFrvnFIIDA3L+C9VjzsHKz9mGktebgkxNCThikzwGKWBvMEeL0kA0IeHshPQQRhNx91efd6ZVig3oPSG9ByC0WScCtvwFtFiJCD5Q1I+efL/x1YJSRoWSps3C/QtCIg+7VyASslCy0jQ2/woycgvAJJCImRwhgeCAIY+rMNn/XDjVKOy5vZg+SQEU1tJ+FwndaS4dD/WLeCN6DpsupavIGGlYL9ksCAM4UE6lKh5RWs+b6AGga0ezi5gr6p4QHkuwPFciEPD4RXMIUHKD0CsFDACAmyMjauVcICi0SWLBmewxvQsHSlQGBfJLBGSJDVT6feKsGskAB+SJC1VfICLioewaKVCOYRDB7AYPwQsX5HAAnpoieECzHDj0ZfEoEV86v5AQTDghmQxJHXUT5W3ka7Z7zdWLexN7DmSoGBfZGAxJyQoPWHRuaGBKF431tSTHYIMIyj5VI02WyGSmU5sbqRDJCHA5IQFK9AIwJrxUCGDEVYAKUdG3erN6DeKi1JiKntiOvsDTRgPyTQ+tNi5wgJFJlMl2PULhwPIdOnnVfIKFcmqqTxFG7+4AWwut4rqBGBlShUk4gaSShkEc4DDNdYtAt6A9pKQ0FGO/cGrOXC4DB2AW/Gr/7C8FarBJqR1rYQN0LtpyEUqNWXD3cSxtKHBdlyYZ4f4LkASQRDsrCaKARQGD4UQ5VtsmvxvQH7JlW8AQ5nO3FXb7SteQPyOiRO6A3slgRGhP878RlWCSIGyd4jUPcLWDqKMSnElvLxNiUKlRl4CAuQzeydN4ALADw3cIExPJBeBA8vsqy86NNbLWhKEsKYaZUlQ3cDUYs3AKGr1RsYZ3WlLOINRBBssy8SiIQEWfxfiu0mJGjgJDcUsMIBOb7IuB1kewKoN3y+MjCEAdz4WZ5ACyeKnYXc0EXOIAsLUMq7SUKljZdIbNlABOC0uYGsX69O9wLmvES0LxIQ8PfqrxASzFkl2DAksPYLaHmB5tzDsOCvdgw2e6fpqRhDg35QQxhwkXqvQBDBqEsYtWbsEGEBpNxkaGoOQJHVcwmBDURQDBusrbxX2RgWeAMDNvAGWpYL90MCg8F5ScBagrDl9WKJLUKC0SuohASB8ahEpn0KwpDE5Xop3EjGvEBn8NkxzPQXPHGY2G5CTIRxYN+5sSseQpErgO0dVJcMIxPiUm8gq+d1xjjWzg2s9FuE+yEBiQV7BkKz5BYbhzARQ9NMbYUQQk8kLzCet3oKmQEyD+Ai2UQwEoIIATIvIGXG6517ScK1lgxDuYHsvlzD3IC1ecjAPkhgeGC9Nf6lewbWDAkUrB0SZDDyAlpC0CKsyI+lpFFhfxwS6JBAF/1xeQRxIiAUocHwchF/+7AweuTlRYggDF668WFvIALLG4Cif6vcQNan7Q2oWOG/Fu2DBCTOsGeg+56fl7PrNiGBBteN10hBqVdzC8mo5+e9kVFPAoeLhMPlEYfLY0cEl0dgOIa3ClkiMMsPyOXEwehYP9k5N3yc0RsoZnshuIU3AL8s9MvE1nLhtcgJzPzZ8bX2DLT84tCoa6WQwF1VMEICk0Cs9vJ6lTbEn8zejacDcDgccXExHQfuEcjVA5kfGA035S8oUV5f7CPYkzcgDMglhCXegBb/a+GCxEJvYD8kMGCjBKHfJ2+bV2mG7L5evGJIUN0tKGR5eKATCuV6HSIDAKLUHYeEi4sjLi+vcHnflUIEvYEP7xaIsIAbudxEJPMBYxnW8Qb4pfP2s72B6O8NBLCKN6ChcfPQ/khAYqUE4Wp7BhSZyOvFszcOOSGBukfACQFIazMsHfIlRKHj0JPArcsr3H/fXdy6dReXl1e4GEODNIUFF+IdAghvQMz0q3kDxnOery54hrOyNxAICbT6k7xmLLATEhCz/3VJEGpeQu8NzH292PICQqsEzgxfeBWCGDRySP2TTpRwoIT7Lq/wglvfwwvuu4v77rvCxeUVDhedpzC8gTgYO985KL0BmSQs8gKsLOwNAO7srqEqH/EGANcbCIUEY72ipxYuWDoavIEqCRDRh4noWSL6Aiv7ABH9LRE93h9vY3XvJ6KniOhLRPSWmn4XZ9hB2H3Pz0+1Z8Bso31nbYowgJ8X5DDN+ONjkcRxpPF8aHqghFsXV3jxrTt48f3P4YW3vof77rvC4eIKdHEcw4Jy2bBXxd4tML0BlOezcwOKVyEThBrCvzegjEVbXtTON3uxaCYinsBvA3irUv7rKaU39MenurHQ6wA8AuD1fZvfIKKL0Eg8g+8xGnHNG9gwQbjlngFTXvbBPA1145Ao00KCjCAGYjjSlBw8TtZ5PBKOffmLLu/gld/3bbzk/udw6/IuLi+PnTcw5AcADL9QzI1ec//V3AA/j3gD2Q30QwMTlOvW6gs43kBXz+sMXTvZPFQlgZTSnwD4u5A24GEAH00pPZdS+gqApwC8Kdg2h5cgnAantKvUu33ytnmV5g1smiBUPATzXQLL6IGCADKj14jhOJWnIyEdgeORcHU84Kp/mt/xA7fx0vu/gxfc1+UG6HCcQgKxT2A0dnDjL2+Ou28AKGd/TOWa4S/59SGpI8OpNg+hUibrIjIGluQE3ktEn+vDhZf2Za8C8DUm83RfpoyLHiWi20R0+87Vd7vCgMHvJkFY9KePTS7/zUkQquv7huEXRq/oKFYUxEFH6jyBY0cEx+MBV1cHPPe9S/y/Oy/Ax7/5Rlwejrh1cYXLwxEXfV7Ayg2krKw0TDMnwM6L+F0x4KXLhrt5sYi1PcVy4VwS+E0APwzgDQCeAfCrfbnWs2rZKaXHUkoPpZQeunXxQlnZfe4sQdjNlEnUo7zCFbwBNUEo+lP3F0hPIeXnGlkMhk/HgQAwhgfp6oB0Rbi6e8Cdu5f4+zu38Mx3/hG+decF3Rgo4XA44pC5/QlJhAEeAeSGBNvlV7wCTcdQ3rp5SIMVJsz2BgyZUy0XaphFAimlb6SUrlJKRwC/hcnlfxrAa5joqwF8Pai0XteaIFzqDWiuN9fXslw4tgl6A4XefAymN4B8hs8y/4NsAoaQYLBV1xu4OuB4PODu3QP+4XuX+PZz9+Nbz70Ad65YuqffUzDuGWAGNh4AM+Rkk4F2LgjAzQmsgC02D3V1lQGeYblwFgkQ0QPs608DGFYOPgngESK6n4heC+BBAJ9tUi6NWvEGwglCqVPqm+ENaGMKLRe2egWSHGS5GJvm1g/yWsiQhQLDODVv4IqQrgjHu4S7dy9w584lvnvnPnznzn347p37cDcjAuTnvVdQTRAiN/pCn5Bp3jwUgNxsVEAhnZMvF3LMWS40cOnWAiCi3wXw4wBeTkRPA/hlAD9ORG9A95h8FcDPAkBK6Qki+hiALwK4C+A9KaWrWh8hpNRdzPCp1XEcUVAcpekPQSlNN5C3l7oSpj9Y/xxnf0ylb0oJCcbDxPUggUBjF/yzaJtEHR/LYPA0XeNg4An5+ahk9AYmjyRxUqCOEBJh9AbSIeEKndEfj4TDISGlLmcw3BQidKEACIm66xvGNvSd5K3h19APi7gsvyYmO5ZB/871a/e4u/dp8oQ0ELq/kpwIhmeR9+3qYfLsPNGkWx3/AdOkxJ9/azxaXQVVEkgpvVMp/pAj/0EAH2waxdS4vBiDxUYj5sbey5sGPuCYJvZlRs5JIutDnA86SCSGuDHm14XxcStIxMBAJNmDq5HRoJuoGz8fB3/i+3M6dq7u9HPpnbGOHsZxYph0NRhKf8l0QEqp44meBNCfJ7b/IL+Q/H4k9H+O0TugyegHY+3HWhh+wZSiDf8OUd6ARD09ZPfJ6A/oniXmHQ7txzEyQ84MnV8H16EYMieLqV+o3nCmr2JHg5r9Qy4XRlcI5v7gSEqLlwvHpF3035UNRpicB5fJZN+HMQ7PEPvUcgPFvoEjpkTmERjzBUNYcARwRSw0OOB4dYG7dy9wdfcCx+MBqd9haO6gJkF+8jvyuuJ84Ozxk0o5TR9NsvpqwvkThKa+NROEDnZDAmQZemTzEEfDcuFU74zLSxa2bB5KjBgsmRZ5QQAtuQFOAFmS8Njb0kAKR3QbiIYdhAMR3D3geLcnhGP/eUXZBqNsqI6xhlcJuB4pJ/vybKFi7BJWgrDT5VwnRDsnQVjNW2ycINwNCVTR+HahmjxUEoTSGzAThDvwBorlPYjv0hvgHgCb9Xm9lMFxkKOJDK4INBj53QMjggPS3QPS1aFbTuQJRfNiK9DIoC+Xn5vtGWDeQ2SMAOo7COXYmC5VhzK7L04QGtgHCQwPbsTtH9soRizqrLJQnBjdPLRkK/Ga3oClWyEXdePQYPScDDgRHNERAfMIhvBgOHgoUUO2e9CanZWZf2wrZYbzSEiQEQnKkMAc88ohwdz3CTz3f0ZosA8SqEEuF9bi/x6rLxemXGezN+AZh7NvINen1BnfrY1BmbfA8wGJxu9DHQYi6I18+MRxIgIc+YEpvBhWDCKkK6ERgHGuegEVtHgIm+0Z0HCGnx/bDwkMM/tSb2DLzUP8XDPObGzQjV6Z3WtG4soLAsjGZngAFkGM5cO+AQxeACOCPjQgbvycDHhSUd6DgOWpoQA7d70A5N9bYv8CXkig6F1tz4A6FmfG1/prTBDuhwRq2MtWYs34Mx2VF4sMuLmBpFyHnOEhZDlZCENXzweX/0hT7mDMD2AigsEL6Elh9AwGXcNmo1E35WMxbwBMYzDzAlDcaHPGFiFB0X9DSKC0LctsHZu/Yhypq6g9HyxvwJE9lzfQ1bFzJzegG3A+u4egzeD8WiqGrxIApnCgyA9YRHA1eQUjcQweAieAnlDC18cvtfL8ujNp5NlnnkIofIj+IrFWZoQETQnCTL8dErivGBvYFwlYCGwlHnEdcgMaRiOsv2FIcowKAVhJQu7ya3sHsvyA4xGMuQNGBvlB5nVUoRlHJUew6uoA07/odwYIbkhQw5w9A6E6gf2QgJzZZ3gD1bozegPl+HSjDsMyLs/wNe/gCIUAML1DIElDWzUYy9gh+ssD/BnXyy89mhdg5VEymL1KQMrvDIgxSHnt/Bx7BvZDAjUEvIGTvli0sjeQG3TQG5AEIghgTBJaM7JFCkd+TqOhZ599IjDfWJQfky7mFVj3S4M2u1fgJgNlXqD49DtYFBJwnCMkiA3nnBAGLr0Bt+l5vYG5uwgXvWEoIQ0bgjTYdy5veRLcK8g8giJ3MJDERAaUGT8yAsj64J/yXEJN5LGmRmJuUSgQGYNVHl0l2GgbcYt+YDckUIG1lXgH3kCuv9RteQPlOBZ6A8wrsGZ8NywYjBw5AXBjz4x8aCvCgXHbcCrDAuL3bPxstFQrD9BipEFkqwpB3bWNQyZqIQEv80KC1nDBaHIeGIb+fPAGiplwKOfegIbGJGHXJpfLxmfpYjP8SCQKEXSGjTwsYGQAzRNgew7U/jGNc9VcgSyPegZsqdCWKQ11ycahUEigwN+afB0TgzVcB29AGOUwo+d96d5A6waiUmf+qYYFlvfACeI49V2QBI/vubEPZICJFMYDeR/q0qZyPWG0zry1vEAFSZ1plbKGjUPVPtcICa7FjkHANvQ5bWt1UW+AGbI06pb/X6gujzEDK8e8flhQyHMPQK4MWN5CthKAggyyjUMp/16SAeXXqtyDOVg1FzDqVFYEgOUhwRarBI1WvS8SMFB9zXiuN5DN6KU3MOv3Blq8Aa0s4gVIw4bSxiAGzSAj+YKJADDO/FnSj+cKlNwBFWSgbCSKXDtQn+0HmRXIoPobA0W/G4YE0VWCQB3HfkigYuib5AZkHeozkgwLLG+gc5lz4ihd4TTNsqI/N0lojU8xXEkAPD/gGr6yEpDJyP0Bg0zIGyA9ZwJRFoXzrM/1CiJ5gWpfDa8Xh8Yz9r3uKsF+SCCKJd6AZrCKN8ANePEvE89ZMtQwNyxQCWIigprhZzmCYoMQMjLgKwK6N0C5B6DcBys0qsp4CMT74f0ChHhewKlvfb1YxYyVgKia8yHqDXi5Atmmlgj0yrbeQMQNkiGcJHSIwJbLx+mtFBQ5Aih1ipxcKhyXDB1iWpoXCM2yWRIwTw4u6iPiFVTkXWy8cWgfJNDK7GM7wxtQDRpFXdUbkHJS99Ilw+xamMuNUjYUFpjt8vNI7K8auJIwzHcGYloSNDcPlf1yUijGLjH3WVmKNfMCHJG8gDqe9UKCfZAAYBvyHG9A6PS8gfrbh7x+OvW8AX6+NEmojXt2WCBmYpkfiLbNwgVJBpzMjKPIRcj7aaApT7AiTCNemBdY9F+KRv3auNpcjf2QwFws9AaKOobF3kCmP28zO0mojbGRCPJ2BhFos742FoUMstBAOWQ+gNRxlX2eGu7KQCAvsMbuwWh98y8OtYudCPeKNwAhK+WVJKEVFniQ8beuq0IEijsvk3583JknEFgpWMXAk/H3XYJKcjBU5upn53N3D7b+4pCBfZHAXCzxBtbcQDRnyXD8Hg8L1PyAZtTiGlzX3gkNRoOVZCCNGUJetJPegSTAot8IVrb9Wb8spOppyAs0IKznWm4brhlyzRsIrBissZ24dcmwNUkYCQvKcc4LC+YQgUUOFhm4B7sHasgTGceKmL+noM3gW7cQz1oqjNTFRa4ZaiSiyUa9gS2ThIqBh8OC7JpORwTE3P2MDNiM70F6BJo3cTIE9xG0tgNQ/NqQPYZ6otCrn7t7sEoCRPRhInqWiL7Ayl5GRJ8moi/3ny9lde8noqeI6EtE9Jaa/nykwijnegOOVzB3O/FaScJwWFArG3RVlg23IoJiGVF6A4NsJEHI768kQy67M8xKDlqyG+cFPEQ8gd8G8FZR9j4An0kpPQjgM/13ENHrADwC4PV9m98gogtUsdFf2CCJaiJQ1mk6gVneANfTGhZ4G4JMIy+uaQUikDsLtTrjvQGIPgtC4KQgxy/HshKqYcCp4/k5ehYsFVZJIKX0JwD+ThQ/DOAj/flHALydlX80pfRcSukrAJ4C8KbQSI7HocP8c6k3EPlRUm3WjnoDSdRrScLEzgs9ef/qTCg3ERWGmZw6QR5ZvzOI4Fi2KXICmsErHoEmx8cmzwvwcc7FXMOMhAZLX+5p+aERT65i5XNzAq9MKT0DAP3nK/ryVwH4GpN7ui8rQESPEtFtIrp95/jdtt7n/NE9b0DIRJcM19hJyNuoYcHQ1nq3gBFBS36Ao4UIal5BjQzMA6IdxFiNsWvXuibiP1BaS94FCWHBS0ZzsXZiULsC9c+SUnospfRQSumhW4cXdoVRb2BS0nUa9QYiS4aVutWThMGwgMOe2fX8QC1RmPXrEYEXBiQU+wSK2V+EBdUcASOA/PrLspb6pYhl9J3KRXsK1s8LzCWBbxDRA11f9ACAZ/vypwG8hsm9GsDXZ/aRY8sNRKrr79VNRZo3YIYFkOfIzxeEBXwMZl5gIREUXoHiBdS8gWzlQB4ovQE3LEji/kssJYJTzMqR5KCC5ryAY+lzSeCTAN7Vn78LwCdY+SNEdD8RvRbAgwA+G9I4Go3hDbQimlvw2mh1HJo34IUFqTwv9bSHBf7sr9XNIwIZHmQyx7rRWzO+VacRgEUGFqoJxKVEAZQze8sKQQOiG5Vaf7fgsiZARL8L4McBvJyIngbwywB+BcDHiOjdAP4GwDsAIKX0BBF9DMAXAdwF8J6U0lXTiDSk1DHaMXXu0PBdfNIxITn16vWl1N20IzpKZLJFHRsLpemPO8oljA8Ar8/bTzJI3WkidGHBgZjO/vnkfYBE26lskCcACex6hz4wfXbyXVu9brKNsb4X6j76fpkMFDkLJO5BAW7w2vfhuhQiVLGGoY8dLdOXaLqORDReAz8fn1l5ro7Hqa+17VElgZTSO42qnzDkPwjgg9We9ca9sR+Bw8E13lkwSGQ0YmUsWZ1GKr1OVS67JoAOE1kQJvmcUEoj7iqY4R4xrlHr5AAMJi5JhRt7Ll8hApKy/cNbIQN+CZmgMOhCxCIAaeyirTXzVz2CrRA0xCYcqFz1WtDPPnYMemOPuvWtSUINc98rqCUJA6sFlKSe5D74PD8QSRROZfwzTfpEnXYeCQ+kLP8eyQWYScLi3ip/B3Z/PA9DYglBFBuGtkojRPVe5/87kApjVbb01ZUAYEQQkHUTgT0W7x0Y0LqJKJgfyOuRG09S3jiMEIHUVxBIriNCBmO5lieAkINOCHLMxTUxaMZ9No8AqP4fgU5mOp2VHJyB3ZCAitYNRBLRJcNAknCTvQOaEY4y4lySjUcOhdErS4cBIlBXGYRhSq/AIwPT4BXDL2Z/Zaz6tSLHGY0+vEzYYMyLNg0Z2BUJFN6Ah1O8V6CuCJR1bliQ8pl9aB95t2CYyTOdibXpZ9WhrggvMmNqJ4JCj2LYuvuvk4FFCjWPILvm7Pt0a6THxKGGCxJnJIsqluTFrs1vDGqQYUELQUQQea9AjGVWWDCW8b55e+O8NT/A+5b1chwOERQ7Cx1j1+RayMA8IPtINgEY969m+Kv/CEkFza77nB2I1/43BvuHPkX+OOdOEspxzAkLrHcLuIFG8wMDEfAZUzV0RlgGEfjty3Pd+HUyyHMmlQPC+MHLje+srDiXcqH6NqKIr+PrbZb8X4IMz4f/QGQmCWew9ypJQs11XxoWSH3FOezzzDhZm42IwJJtJQNJCLVjvH9SH/g4kcs59463u5ZQOGKNX0HaCQkMhtVusDVvwGzXkiQUOvyQYSqKhgXWbkIrP+AlCs0VA95/IxFIWS1nECEDTTbmEVSMXbu/4h5Y2DtJbPFzYhI7IYESs5YMT5EkzFx/r04hjMz4hH7AfnCPpY5RRjNYubVYNdwAESSmVzFs1eiR92N5C0UykMGst0jBuHe2NyU6bSGJU6Bilc1hw3XZJ4DUW4Mbp1eWDOeiliSM7B1oCAumMt6GG6ww3l6Xmh8wiMDU1UIE3ngU47dmetcTQMXoFVl53a7RK+RQnO8Ba20yuvb/hmyATBIGXPTVkoQNicBVwoKUYsuGlfPC+OXSIW9TIQKdFHIisIy/MHbk54WxG8Qh75U6+7NztU65T9aqgJtH4FiTPFrsVf2xkHWYY18kkMopd5UkYY0IHGweFggdi/ID0lgrewg8XTopJF2PYvyRHIAWGpjhQoVYxjLle3EOIVfDmoa/kuHW+9lE9ERYIyxoMPJI+7VWCzJ3fUZ+4FxEkJ1zPQHjN0MBiO+1A6INdO/A/A7dI7PP17T8mZix5FjWXafNQs5Nb9pJKPVtERaIPkKbiFbID4xQEoUmEQDmZqImIhAGp3oFyNvJsKAaBmhQZFQ9xhj4d46w+9+Ak2xAmutIXIvNQhwbJwlDewdkm0pY4L1bsCg/kOR5L3vU9x1A9jUYhLKHYOybG3lKGBOPqvFLkkimFzDol15AyOUXxGDKFvfQ+q6QaQM5WHmF4p/EnBMzlwn3QQLawzxggyRhIR/0BrzVgmm8hm6mtyk/AO180seJQJ8dg0QgdQt9qpGNRq389JhFBnx8hpGbJAG7XTE2DV6981idEqvtGmzAPkiAYzQex9JakoRbhgUNJNEcFij5gVbDbSYCZsSaPi3eL8aXffe9gGyc0QNCd3Gtxn2TcAlB+RudEVrM75LFtf3X5JqLLsIC9b2CrfYOGFglLDiWL8TkBjf1URqWMNyj0A3LOCpEIIxKEkGRJ2BynlcwyGtGX531kbdvCgkcYirgEasiV5zvFdfyX5MPWJokDIYFc70Bb0VgROAFpGp+wJEziUASTpQIelndwLl+WSfHPdSXbxBWvQB2D0xScOTlPaq9eVj1AuDIyTFdY+yLBALeQF43GGPD3oG1iUDR3bxsWLRn3yuJwhEeETDZViLIdMpwA4wIDANXXwZCJd73DhjtBdmFCcAlhLKougJwakII7Duo5Rn2RQJAaXRKnfu6cc2I10YtLJi5bBhOFCaNWISe4oFXiCCVOvWZXoQHUlYzThhkoBBC9MjuW0YO5T2tzuQCUflZKwMbhalLsB8S0BKBTpKw6QWjU4cFoSTiNDYtP1C0s3YU9u10YmkgglGPMoMy41avwZldy8Rg4LcFPBiymU5tTJVx8uvi0MKMPWLJ7w3uhwQA+K7/gr0Dal8bEQFDPYmoyIoZNx8DH3/ZLieWvG0rEVgbgbI8QcArGNp7brtqwNbB4LZFIwFAr1eh1GlhQqsHUsVGy4c7IQHlbrWEBYGZd/OwwOg3lkQUshk5iHbWioGUbyWClBNBVgfHeByvIEQGgghbjhGqLuT1skzCnTCm8Vl11xk7IQGG52FY0JIfqCYKe1SXDnudYSLov5tEkJzY3/AKhv7KkMAhBMWgCxhyfrJQtEd5X0PJwqEvLx+gtFvdK7BwrV8l1tz86xwWWPVq3TBuJms+oEldMZh0I5f3iCAzkpwIrJWDfCzy+6Q/HhIoxgsh55BDLFko6pQxh1Ejp7V0nRD7IQGAGZjjDSjYPCxYIT8wIpAfmPqFLRNYOqwSwSBrGXsjEXCvwO7D8QBQkkJ4lUD0Y/YF/V7Xk5z28+IuG24dfq6ARSRARF8los8T0eNEdLsvexkRfZqIvtx/vrSqSDWAmUnCLcKCuWj0FvjYmlcMetnNiCBNMXstCTj2wUME3o9lyJIUvNvvyKp6Wd1UrtwHD/yeVORP5v6bAzjtPx/5NymlN6SUHuq/vw/AZ1JKDwL4TP89Di8scJJqTVuKNZwrP5DF+fk1tawYWHsIpN4qEWhEouUJNP2SHIZ+GBlsERJIHUVbJjPVpbJMysnrjSAg6uYazuA5bBEOPAzgI/35RwC8PdIoM+JIWBBJEqrtDCNWcJL8gBhflQgAnwhqhlojgv6zWCb0iEAzcOhkUJazA3m7bcKCVF63dp9qtmjcZxfn2CjUW7i3a3ApCSQAf0hEf05Ej/Zlr0wpPQMA/ecrtIZE9CgR3Sai23fSP6CXV3rYICyoGHFTGLDB+wUmEWgyC4hgjOGhPPxcx2DoPGHIiUL04ScFSzIwcwSRP4PTpvQMUlaX6ZBlo5y4vhm7BM8eGlRwubD9m1NKXyeiVwD4NBH9j2jDlNJjAB4DgH988fLyNh1TuS96KEupjHn6upQSqKg7AoeD3m4aEMb/8c4+6ZiQDmW5q8u/8E5vSh07H9FRsew3odsF1l8XpYQEAgilzNC218Vlx34S+u9SL5Ag9aHvB7qOI7p/ya3VQ/QjygA25gF93+Mt4re10YD0BGuyZVT5Qa5hltf0rxxGbJVkXOQJpJS+3n8+C+DjAN4E4BtE9AAA9J/PhpT1M7brDeSdm3WzwgIN58oPOPGqTBRaHgGX9TyC8TuL3fN+DB0tXgEmuaJ8GLvhISwLC1L2DITGI6+XoZYbaSILp59TYzYJENGLiOglwzmAnwTwBQCfBPCuXuxdAD7RqjtpBnqOsEAfnP65ERFw3daKgUYE5q5Cjwh6HYVLLw2dG5BiGIVMxfhMQmg1EKNtfLVA09nLrfQzYnsMDZaEA68E8PHe9b4E8F9SSv+ViP47gI8R0bsB/A2Ad4Q1Dm57Vpb2FRZEULj2Ndc/mYkbNSxgrrkXGtBBhgKB0EDVOfRl6OHhARQZTP0ByMMEUTfW83s5E2rC0KrPiCEVZVLO8gLMpOuCpODWxDGbBFJKfw3gXynl/wfATywZVK+nNOJ0BOggBXuDKutGHZrRS2PXyKY1P6DpaEFLfmBPRIBppuRkQP2JRQZd3SSv1UtYb8u5hqLUtRBAZF/AJjhRqLCfHYOK266GBQNOERYoOHt+4MShQf6dfSbm+nNdx9JoCrnM6BSDTKWclHdzAVKP0j6TG+tKRVoYsCgX4CC043QD7IcEODRD1fYOrLWJSO3H+NSwdyJQZOcQgVz6U3UJzyDPAcR+WCSDJIXowaDnHnK54q1ECAKQ16qgFgqYyceVSGQu9kUCyg1etFrgvXIc2U1oEIG7rXiPRHDE6BHk236NbcDHVO4lWMMrEGQQIQRzpq+gSiqjnPHzZwoBFHqsumuGfZCAN1tnZZpcjCRm/UCpIzPn/xqe3SMw3j5cFB54s1uFDAb5YhYWMkOf85cKg/0OdQYBtHgB1wn7IAFAd9G1vQMzw4IMMj/QEhYwNOcHNFxjIgh7BR4ZsP5dQlCIwYTTpugDuUyIAOR1KzDv3w6xHxLg2Dos4PDCgpb8QJQIpJFr2DERuOGBMI4QGci2yAlBNdjaIVDVNci1EECmf6gLWLp5r/W/tUUea24y2hcJqCFAZbUgsomIF7UYsgZpiJ6nESUC7/2CCk5KBDO8gkGnRwaWdzBdY+XnxSqyBRTjbyYAeX+yMfQna74wJP+uA6w+Gp6lXZBANlwnLMgQfeV4i2VDiwhct7+RCIL1Wh9bEIEbHtS8ggoZABVCCMzwHjFYegrjh6ivEEDezv7bFwS5AUxvJLDcuAsSAAxXfY2wgGOtZUOln2p+QBvbSkSgz0YrEoGc9WR4IPTPIgOPEIBStuUQcHUPMgECmOsF7GVpcMBuSCDDWmHBeLODYcGcZUMNFhE43siqKwYregTN4YFGTPJc08/rDEKYu3/f1aEYfzMBuGQcGJ/8254YuyKB5BkkL+NYsJtw9tuGhpFTQMbzKlb1CBYQAd87oOmwwoOwVyC+V1cGGKRBR44Clm75N2kggAzq82f0ocEhlbnw+t0VCQCnDQtUuUh+QKvTiKCmZ4ZXcYrQQGtTDQ8qXkGEDIZ+Vlsq5HDamv3x76MsL096uVOm6axi4/BhPySgzujsqVSMdFFYcI78QHYduozqGp46NGgMD6pegUcG1ncYhABFPnIIuGQj+xnb8HKDABq9gDmhwJrLg8CeSIBBDQs4TrGbcM7+AYbQjsJTEAHrJ0QESngwtpEPs2YgwiuokoHQnX03CKG6GiBQbWcZ/wICcFcEvGG3GPhKS5D7IgEtXueohQXR3YSRZUOtLFK3kow7Q2xJBFyPJALWzg0PWH+q7igZ8DLjebfIIUQUAeNXx6+VW9fLxhkp83RUZWd6CDshAcWQOaJhQaZywbKh0kdZFzDklkSh1t4y8ojMTCKoJgxZu1B4oHkFg37RrxoqaCRh1VuItDOMvyUHUJRrMlqZFwqs7Ppr2AkJwHfTgXlhwVb5AWnAWp1GBIbMrKVDhggRhHYWauHBoDcaHiwgA7We67XswTLyGkkYMvoYHAJw8gCdvPM3M/qw9LWsLkSxHxLgaAkLvJeMDJ2Tnpn5AU2vM051R6FFBDNm+7hMPmZ104pFBEy3FR64uYKh30YycAkhauxWOwHT+IMEUHXhtXplHguFApF8QDDZuC8S0GJ5Di0s4M1rYUEwPzD7/QKtziMC2e7cRCAMv5YwnOUVDH0LMij6MghBNdQBMwjCJptU/K2sayjqMhIsOw97AYEZvWkzkoF9kEB2A5eFBe6yYa5U0eMZqEJAgbieY9bSIW+/NREAKhGEwgPW1iaIuiGp3oF6LxrIoUXeMH5vGTBEAFpfUS8gYOhLsA8SAJAqLnw0LLDar5of0MYQSQKiQgRy3LON3JGRszwjAithmMkb4YHlFSwhA5MQnJmtlRwK3YquDAsJYIkX4K90zGeK3ZBAhjXDglPkBzQikHWR8EEb99pEwM4lEWTtuNEHwoOsLW+vldfIoEYIfAzaoaFB3jR+12txrlcrX+IFRHalNmBXJJC0mZvXzwkL8g6YrGJ40fzAWolCLtdIBJOMoQcrEoGQr4UHVa+gqKsbHZerzu5RYjB0R8aR98frGo1wwQzu3oOGHYi7IgGgMSyovGS0WX5AK4sar0YEM3Sps0qUCApdrK9AwjBrI72CFcjA9A4qpFAjiCZ5p78MHgFYdTUv4IShALAnElCNUw8LzpofGB94J1GojcsjAk1/QNdsIlB1sbpawlBrYzzknmvs7duvzsyVJbLmvICj212qZNdT1Gt12t/AgkVODYh4JvshAQYzLNhDfsDqW45h7oqBJh/ZVQhUEoExIgglDK02jV5BUafU8z5cQnA8hgLBNvYKgpRbRgBzvABzZWMG9kUCzPiudX4gQgT8IdiSCDQ5hwg6uUEXL1OMeIZX0EwGDiH4MfE8gvCXD7XxBAkgAsNrGhH1Apz3TTRsRgJE9FYi+hIRPUVE7ws31MICjplhwVr5gdmJQq2PGhFI+bWJILqE6IUHme66V6DqUcggQgi8z2bX32irwjD+2nVkiHgB6hgrAjNnf45NSICILgD8JwA/BeB1AN5JRK9zG2mufCAsmIzbCAtWzg/UxhxK7hlyTZuJFLlmIqjKseuywoOUtynayb5EiFDoaiEE5/n3koD11QW7D/XNREUmQ2sYUCUGpTAYRmjYyhN4E4CnUkp/nVK6A+CjAB4Ot24ICzKsmR9wiGBWonBhEjDvyyeCprcPq3KV8AAwwwPVKxj6s8KEQZ9hfKoBakcUwbZu30Iuw0wPoNAVnYRmYCsSeBWAr7HvT/dlI4joUSK6TUS3v4fnusLsQTCIYEBt2TCTnZEfsOo9PXOIQMi7ewj4eTDxOIsINC+FE0EwPBjaLSaDKCFwWOTQQBZuP0pRlABKXQu9AKvPSN8ALisq50L7L/LZSFJKjwF4DACI6H//Ufq9vwfwTfMmXK07wABeDuCbJ+/Vxs146tjbmPY2nn+mFW5FAk8DeA37/moAX7eEU0o/QES3U0oPbTSeZtyMx8fexgPsb0x7G4+FrcKB/w7gQSJ6LRHdAvAIgE9u1NcNbnCDBdjEE0gp3SWi9wL4bwAuAHw4pfTEFn3d4AY3WIatwgGklD4F4FMNTR7baiwzcTMeH3sbD7C/Me1tPCpIzXLf4AY3uGewr23DN7jBDU6OGxK4wQ3ucZydBGa/Y7DuGL5KRJ8noseJ6HZf9jIi+jQRfbn/fOnGY/gwET1LRF9gZeYYiOj9/T37EhG95UTj+QAR/W1/nx4noredcDyvIaI/JqIniegJIvq5vvws98gZz9nu0WyklM52oFs5+CsAPwTgFoC/BPC6M4zjqwBeLsr+A4D39efvA/DvNx7DjwF4I4Av1MaA7n2MvwRwP4DX9vfw4gTj+QCAf6fInmI8DwB4Y3/+EgD/s+/3LPfIGc/Z7tHc49yewLJ3DLbFwwA+0p9/BMDbt+wspfQnAP4uOIaHAXw0pfRcSukrAJ5Cdy+3Ho+FU4znmZTSX/Tn3wbwJLqt6Ge5R854LGx+j+bi3CRQfcfgREgA/pCI/pyIHu3LXplSegbo/uAAXnGGcVljOOd9ey8Rfa4PFwbX+6TjIaIfBPAjAP4MO7hHYjzADu5RC85NAtV3DE6EN6eU3oju1ef3ENGPnWEMLTjXfftNAD8M4A0AngHwq6ceDxG9GMDvA/j5lNK3PNFTjEkZz9nvUSvOTQJN7xhshZTS1/vPZwF8HJ2b9g0iegAA+s9nTz0uZwxnuW8ppW+klK5SSkcAv4XJnT3JeIjoPnQG9zsppT/oi892j7TxnPsezcG5SeDs7xgQ0YuI6CXDOYCfBPCFfhzv6sXeBeATpxxXD2sMnwTwCBHdT0SvBfAggM9uPZjB2Hr8NLr7dJLxEBEB+BCAJ1NKv8aqznKPrPGc8x7NxrkzkwDehi6z+lcAfukM/f8QuqztXwJ4YhgDgH8C4DMAvtx/vmzjcfwuOvfxe+hmjXd7YwDwS/09+xKAnzrReP4zgM8D+By6h/qBE47nX6Nznz8H4PH+eNu57pEznrPdo7nHzbbhG9zgHse5w4Eb3OAGZ8YNCdzgBvc4bkjgBje4x3FDAje4wT2OGxK4wQ3ucdyQwA1ucI/jhgRucIN7HP8fKF9MBqlDovQAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "src = galaxy(\"kc96/s0\", z=0.1, amplitude=17, filter_curve=\"g\", pixel_scale=0.05, r_eff=2.5, n=4, ellip=0.5, theta=45, extend=3)\n", - "\n", - "plt.imshow(np.log10(src.fields[0].data), origin=\"lower\")" - ] - }, - { - "cell_type": "markdown", - "id": "53235ea0-b45d-45e0-9d31-b97a4f9476f9", - "metadata": {}, - "source": [ - "We can also visualize the spectrum" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4d22fa4e-549c-4c21-8a4e-65b2aad6f95b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sp = src.spectra[0]\n", - "sp.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "898361b2-c311-4f3e-bc9e-8f4c3504642e", - "metadata": {}, - "source": [ - "Finally there is much that can be done with the spectrum itself, like for example, obtaining the magnitude in a different filter" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2b2498ea-bf80-4950-be4d-49d21ce94124", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$18.848014 \\; \\mathrm{mag}$$\\mathrm{\\left( \\mathrm{AB} \\right)}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sp.get_magnitude(filter_curve=\"u\", system_name=\"AB\")" - ] - }, - { - "cell_type": "markdown", - "id": "325d1baa-1f30-4145-8e3b-a83685309e48", - "metadata": {}, - "source": [ - "More can be found in the [speXtra](https://spextra.readthedocs.io/en/latest/) package. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/notebooks/extragalactic.md b/docs/notebooks/extragalactic.md new file mode 100644 index 0000000..1fb7148 --- /dev/null +++ b/docs/notebooks/extragalactic.md @@ -0,0 +1,50 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# Extragalactic + +A few functions that can also helpt to create extragalactic objects + +```{code-cell} ipython3 +import numpy as np +import matplotlib.pyplot as plt + +from scopesim_templates.extragalactic import galaxy +``` + +The function `galaxy` will generate a sersic profile with user provided parameters. The function can accept any sed in the [speXtra](https://spextra.readthedocs.io/en/latest/) package. + +It must be noted that the pixel scale is not related to the pixel scale of the final simulation but to the pixel scale (in arcsec) of the generated image. It is recommended to be fine enough to well sample the profile of the galaxy. + +The SED and the selected filter must overlap but an error will be thrown if they don't. + +```{code-cell} ipython3 +src = galaxy("kc96/s0", z=0.1, amplitude=17, filter_curve="g", pixel_scale=0.05, r_eff=2.5, n=4, ellip=0.5, theta=45, extend=3) + +plt.imshow(np.log10(src.fields[0].data), origin="lower") +``` + +We can also visualize the spectrum + +```{code-cell} ipython3 +sp = src.spectra[0] +sp.plot() +``` + +Finally there is much that can be done with the spectrum itself, like for example, obtaining the magnitude in a different filter + +```{code-cell} ipython3 +sp.get_magnitude(filter_curve="u", system_name="AB") +``` + +More can be found in the [speXtra](https://spextra.readthedocs.io/en/latest/) package. diff --git a/docs/notebooks/starting.ipynb b/docs/notebooks/starting.ipynb deleted file mode 100644 index 45801e7..0000000 --- a/docs/notebooks/starting.ipynb +++ /dev/null @@ -1,235 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8011449c-2569-40c6-8c55-70b7819d9a81", - "metadata": {}, - "source": [ - "# Getting Started\n", - "\n", - "`scopesim.source.Source` objects are composed of a spatial description and a spectral one. Spatial description can be `astropy.table.Table` objects for point sources or an `astropy.fits.ImageHDU` for extended sources. Spectral description is provided as `synphot.SourceSpectrum` or compatible objects. Spectral datacubes can also be accepted\n", - "\n", - "## Creation of a `Source`\n", - "\n", - "The creation of `scopesim.source.Source` objects might require quite a bit of interaction from the user. For example\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "da8bfd8e-3d61-4e5f-8cfe-8def9f124c0b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from astropy.io import fits\n", - "import synphot\n", - "from scopesim import Source\n", - "\n", - "# creation of a image with a central source defined by a 2D gaussian\n", - "x, y = np.meshgrid(np.arange(100), np.arange(100))\n", - "img = np.exp(-1 * ( ( (x - 50) / 5)**2 + ( (y - 50) / 5)**2 ) )\n", - "\n", - "# Fits headers of the image. Yes it needs a WCS\n", - "hdr = fits.Header(dict(NAXIS=2,\n", - " NAXIS1=img.shape[0]+1,\n", - " NAXIS2=img.shape[1]+1,\n", - " CRPIX1=img.shape[0] / 2,\n", - " CRPIX2=img.shape[1] / 2,\n", - " CRVAL1=0,\n", - " CRVAL2=0,\n", - " CDELT1=0.2/3600,\n", - " CDELT2=0.2/3600,\n", - " CUNIT1=\"DEG\",\n", - " CUNIT2=\"DEG\",\n", - " CTYPE1='RA---TAN',\n", - " CTYPE2='DEC--TAN'))\n", - "\n", - "# Creating an ImageHDU object\n", - "hdu = fits.ImageHDU(data=img, header=hdr)\n", - "\n", - "# Creating of a black body spectrum\n", - "wave = np.arange(1000, 35000, 10 )\n", - "bb = synphot.models.BlackBody1D(temperature=5000)\n", - "sp = synphot.SourceSpectrum(synphot.Empirical1D, points=wave, lookup_table=bb(wave))\n", - "\n", - "# Source creation\n", - "src1 = Source(image_hdu=hdu, spectra=sp)\n", - "\n", - "plt.imshow(src1.fields[0].data)\n", - "src1.spectra[0].plot()" - ] - }, - { - "cell_type": "markdown", - "id": "48248245-3917-4197-aa30-1f36105185f4", - "metadata": {}, - "source": [ - "The attributes `.fields` and `.spectra` contain the spatial and spectral description of the sources respectively. Datacubes are stored in the `cube` attribute\n", - "\n", - "These attributes are actually lists of objects which allow to store several sources to be used in one simulation. " - ] - }, - { - "cell_type": "markdown", - "id": "a98baecb-c516-4acc-8a20-57a07708605c", - "metadata": {}, - "source": [ - "## Combining sources\n", - "\n", - "For example, let's create now a simple point source and combine it with the previous one" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4f8158c5-997f-4415-8060-39499544b185", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0]: ImageHDU with size (100, 100), referencing spectrum 0\n", - "[1]: Table with 1 rows, referencing spectra {1} \n", - "\n" - ] - } - ], - "source": [ - "lam = np.arange(1000, 10000, 1)\n", - "flux = np.ones(lam.shape)\n", - "\n", - "src2 = Source(x=[0], y=[0], lam=lam, spectra=flux, weight=[1], ref=[0])\n", - "\n", - "src = src1 + src2\n", - "\n", - "# printing information about the combined source\n", - "print(src)" - ] - }, - { - "cell_type": "markdown", - "id": "d5388e0b-14d4-4744-a088-30ccc697353c", - "metadata": {}, - "source": [ - "More details can be found in the respective fields" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "341962dc-b48b-4d9a-8878-a7132f1e11e9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, ]\n", - "[, \n", - " x y ref weight\n", - " arcsec arcsec \n", - "float64 float64 int64 int64 \n", - "------- ------- ----- ------\n", - " 0.0 0.0 1 1]\n" - ] - } - ], - "source": [ - "print(src.spectra)\n", - "\n", - "print(src.fields)" - ] - }, - { - "cell_type": "markdown", - "id": "43ac2fa9-754a-4c4b-85ce-8c95003d8f2b", - "metadata": {}, - "source": [ - "## ScopeSim_Templates\n", - "\n", - "The idea of `ScopeSim_Templates` is exactly to aid the creation of standard sources to used in the simulator `ScopeSim`.\n", - "\n", - "Currently the package contain sources to work with stellar and extragalactic objects, as well as general function for other purposes. \n", - "\n", - "The following example combines galaxy and a central source simulating an AGN" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d72098ac-2e1a-444e-9966-4c17a833e15f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0]: ImageHDU with size (150, 150), referencing spectrum 0\n", - "[1]: Table with 1 rows, referencing spectra {1} \n", - "\n" - ] - } - ], - "source": [ - "from scopesim_templates.extragalactic import galaxy\n", - "from scopesim_templates.misc import point_source\n", - "\n", - "gal = galaxy(sed=\"kc96/s0\", amplitude=15, filter_curve=\"g\") # This will create a galaxy with an S0 SED from the Kinney-Calzetti library (see speXtra)\n", - "agn = point_source(sed=\"agn/qso\", amplitude=13, filter_curve=\"g\") # and this an AGN\n", - "\n", - "source = gal + agn\n", - "\n", - "print(repr(source))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/notebooks/starting.md b/docs/notebooks/starting.md new file mode 100644 index 0000000..07066a1 --- /dev/null +++ b/docs/notebooks/starting.md @@ -0,0 +1,109 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# Getting Started + +`scopesim.source.Source` objects are composed of a spatial description and a spectral one. Spatial description can be `astropy.table.Table` objects for point sources or an `astropy.fits.ImageHDU` for extended sources. Spectral description is provided as `synphot.SourceSpectrum` or compatible objects. Spectral datacubes can also be accepted + +## Creation of a `Source` + +The creation of `scopesim.source.Source` objects might require quite a bit of interaction from the user. For example + +```{code-cell} ipython3 +import numpy as np +import matplotlib.pyplot as plt +from astropy.io import fits +import synphot +from scopesim import Source + +# creation of a image with a central source defined by a 2D gaussian +x, y = np.meshgrid(np.arange(100), np.arange(100)) +img = np.exp(-1 * ( ( (x - 50) / 5)**2 + ( (y - 50) / 5)**2 ) ) + +# Fits headers of the image. Yes it needs a WCS +hdr = fits.Header(dict(NAXIS=2, + NAXIS1=img.shape[0]+1, + NAXIS2=img.shape[1]+1, + CRPIX1=img.shape[0] / 2, + CRPIX2=img.shape[1] / 2, + CRVAL1=0, + CRVAL2=0, + CDELT1=0.2/3600, + CDELT2=0.2/3600, + CUNIT1="DEG", + CUNIT2="DEG", + CTYPE1='RA---TAN', + CTYPE2='DEC--TAN')) + +# Creating an ImageHDU object +hdu = fits.ImageHDU(data=img, header=hdr) + +# Creating of a black body spectrum +wave = np.arange(1000, 35000, 10 ) +bb = synphot.models.BlackBody1D(temperature=5000) +sp = synphot.SourceSpectrum(synphot.Empirical1D, points=wave, lookup_table=bb(wave)) + +# Source creation +src1 = Source(image_hdu=hdu, spectra=sp) + +plt.imshow(src1.fields[0].data) +src1.spectra[0].plot() +``` + +The attributes `.fields` and `.spectra` contain the spatial and spectral description of the sources respectively. Datacubes are stored in the `cube` attribute + +These attributes are actually lists of objects which allow to store several sources to be used in one simulation. + +## Combining sources + +For example, let's create now a simple point source and combine it with the previous one + +```{code-cell} ipython3 +lam = np.arange(1000, 10000, 1) +flux = np.ones(lam.shape) + +src2 = Source(x=[0], y=[0], lam=lam, spectra=flux, weight=[1], ref=[0]) + +src = src1 + src2 + +# printing information about the combined source +print(src) +``` + +More details can be found in the respective fields + +```{code-cell} ipython3 +print(src.spectra) + +print(src.fields) +``` + +## ScopeSim_Templates + +The idea of `ScopeSim_Templates` is exactly to aid the creation of standard sources to used in the simulator `ScopeSim`. + +Currently the package contain sources to work with stellar and extragalactic objects, as well as general function for other purposes. + +The following example combines galaxy and a central source simulating an AGN + +```{code-cell} ipython3 +from scopesim_templates.extragalactic import galaxy +from scopesim_templates.misc import point_source + +gal = galaxy(sed="kc96/s0", amplitude=15, filter_curve="g") # This will create a galaxy with an S0 SED from the Kinney-Calzetti library (see speXtra) +agn = point_source(sed="agn/qso", amplitude=13, filter_curve="g") # and this an AGN + +source = gal + agn + +print(repr(source)) +``` diff --git a/docs/notebooks/stellar.ipynb b/docs/notebooks/stellar.ipynb deleted file mode 100644 index 4cf4091..0000000 --- a/docs/notebooks/stellar.ipynb +++ /dev/null @@ -1,434 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9b0f903c-fed2-4e56-9c01-deb5a45c075d", - "metadata": {}, - "source": [ - "# Stellar Module\n", - "\n", - "This module include general functions to work with stars" - ] - }, - { - "cell_type": "markdown", - "id": "71b68992-a9eb-4bbc-9c26-e710d5797c15", - "metadata": {}, - "source": [ - "## Stellar cluster\n", - "\n", - "In the following example we generate a young star cluster with a core radius `r_c=1 pc`, `M=1000 Msun` and located in the LMC (`d=50kpc`)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "57379fe1-3dbf-4675-b588-f5a93e8b2247", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from scopesim_templates.stellar import cluster\n", - "\n", - "\n", - "src = cluster(mass=1E3, distance=50000, core_radius=1)" - ] - }, - { - "cell_type": "markdown", - "id": "a5de9c56-0db6-4a07-993e-2d9075772904", - "metadata": {}, - "source": [ - "Lets have a look inside the object:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "33afc466-1b75-4aac-b523-6a2c577d8e8a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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xyrefweightmassesspec_types
arcsecarcsecsolMass
float64float64int64float64float64str3
0.7047941071430335-0.34193400839906835322.702228328228619e-190.023450818028910993M6V
3.017163801660643-1.531947677274888275.985543114133627e-120.5030213194041789M1V
5.545342770168751-0.5059378004225308244.961396135881094e-110.6887993545486397K5V
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..................
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" - ], - "text/plain": [ - "\n", - " x y ref weight masses spec_types\n", - " arcsec arcsec solMass \n", - " float64 float64 int64 float64 float64 str3 \n", - "-------------------- -------------------- ----- ---------------------- -------------------- ----------\n", - " 0.7047941071430335 -0.34193400839906835 32 2.702228328228619e-19 0.023450818028910993 M6V\n", - " 3.017163801660643 -1.531947677274888 27 5.985543114133627e-12 0.5030213194041789 M1V\n", - " 5.545342770168751 -0.5059378004225308 24 4.961396135881094e-11 0.6887993545486397 K5V\n", - " 0.04337484405135695 0.47394950595035895 31 9.301655464729025e-14 0.16592467519086804 M5V\n", - " -0.1695198026107141 2.689008599028229 27 5.728214836974918e-12 0.4981528843145076 M1V\n", - " 2.903227971744357 -0.42880293404815345 32 7.235335661968081e-19 0.030385458123976427 M6V\n", - " 2.9147725521817955 1.4290979035892837 31 2.5344525453154387e-14 0.12048133658240405 M5V\n", - " 2.1879322601012166 1.7393082707150282 29 1.1280665937633994e-12 0.33768446357534043 M3V\n", - " -0.5711617098260228 -5.393018142881724 21 1.6102797118853998e-10 0.8167460590486888 K2V\n", - "-0.11710185806601776 -5.100956140168292 32 1.5282472923135797e-14 0.11036638034546091 M6V\n", - " -2.146176289199411 4.292793390010554 29 6.746584693259497e-13 0.284529520314177 M3V\n", - " -6.516561394056243 -0.8923876990270431 28 2.141385549412798e-12 0.4021587190742773 M2V\n", - " ... ... ... ... ... ...\n", - " -4.679661612983974 2.5527643902258825 32 8.043671952122561e-16 0.0800771445221511 M6V\n", - " -1.6708993605568665 2.038654094696279 31 3.598973661189041e-14 0.13306765925503572 M5V\n", - " 1.4678772515789662 -0.17251949373959752 24 5.335448880937463e-11 0.6961978840502374 K5V\n", - " -2.9862656254225963 0.5421006937620638 31 2.702271491358672e-14 0.12278256100621125 M5V\n", - " 3.157740384424968 3.0596228361838076 15 9.931528833544933e-10 1.1792543689064683 F8V\n", - " 2.358628131504372 -1.557299020469622 32 6.093043334547767e-20 0.012963210764621674 M6V\n", - " 1.4521739653435157 -0.851382522869428 32 2.1990614865733614e-14 0.117485467623293 M6V\n", - " -0.4565704589836154 0.18085670454520986 29 5.555250603467652e-13 0.26443896759352503 M3V\n", - " -4.2880207051930626 4.243801810096584 27 4.805552069892325e-12 0.48208630286109483 M1V\n", - " -1.0717398664268987 3.7082294509014195 32 1.471179396509044e-15 0.08623356844839875 M6V\n", - " 2.1224836756915306 1.1488760649099696 32 4.0087183177773387e-17 0.05865254767345462 M6V\n", - " -4.886571147953505 1.7463696161882218 29 9.928282883555495e-13 0.3244795598952535 M3V" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "src.fields[0]" - ] - }, - { - "cell_type": "markdown", - "id": "82d9a9e2-5a9c-49ce-a5fc-2429a5b80243", - "metadata": {}, - "source": [ - "Here we can see the spatial information is in the form of an `astropy.Table`.\n", - "\n", - "The columns `x` and `y` show the position of each star in `arcsec` relative to the centre of the field of view.\n", - "\n", - "The column `ref` connects each star in this table to a spectrum in the following list:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "17c1da19-4432-4b07-b996-d23e5188c053", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "src.spectra" - ] - }, - { - "cell_type": "markdown", - "id": "dd197678-ce6f-4456-bbb5-86f2bfd827d7", - "metadata": {}, - "source": [ - "When ScopeSim ingests this Source object, it will look primarily at these three columns.\n", - "\n", - "Now for a graphical representation of the cluster as it will be seen by ScopeSim:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "bb81d905-23d8-4df8-a5ba-1ec3a83b4f1b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Y [arcsec]')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.figure(figsize=(8, 8))\n", - "plt.plot(src.fields[0][\"x\"], src.fields[0][\"y\"], '.')\n", - "plt.xlabel(\"X [arcsec]\")\n", - "plt.ylabel(\"Y [arcsec]\")" - ] - }, - { - "cell_type": "markdown", - "id": "49c9813b-b49b-479d-bc90-76b4d8bde0c1", - "metadata": {}, - "source": [ - "## Star Grid and Field\n", - "\n", - "These are two functions that are good to test simulations quickly\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a9a3c6ca-b062-4945-a809-3ae3bbb5e488", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['A0V']\n", - "['A0V']\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from scopesim_templates.stellar import star_field, star_grid\n", - "\n", - "field = star_field(n=400, mmin=15, mmax=25, width=20, height=20, filter_name=\"Ks\")\n", - "grid = star_grid(n=400, mmin=15, mmax=25, separation=1 , filter_name=\"Ks\")\n", - "\n", - "plt.figure(figsize=(14, 7))\n", - "plt.subplot(121)\n", - "\n", - "size = np.log10(field.fields[0][\"weight\"])**2\n", - "plt.scatter(field.fields[0][\"x\"], field.fields[0][\"y\"], s=size, marker=\"o\")\n", - "\n", - "plt.subplot(122)\n", - "\n", - "size = np.log10(grid.fields[0][\"weight\"])**2\n", - "plt.scatter(grid.fields[0][\"x\"], grid.fields[0][\"y\"], s=size, marker=\"o\")" - ] - }, - { - "cell_type": "markdown", - "id": "0ee95011-cb4f-4f38-a607-2c47626d7d5a", - "metadata": {}, - "source": [ - "In both cases we generated 400 sources between magnitudes 15 (`mmin`) and 25 (`mmax`). \n", - "\n", - "`star_field` places the stars at random, whereas `star_grid` place them in a regular partern controled by `separation` distance.\n", - "\n", - "The size of the simbols illustrate the magnitudes of the stars" - ] - }, - { - "cell_type": "markdown", - "id": "8ea30de2-bfe4-46b5-999b-4e1620fe91a3", - "metadata": {}, - "source": [ - "## Stars\n", - "\n", - "The core of the `stellar` module is however the `star` function which can create any field according to the user needs\n", - "\n", - "In this case we generate a stellar field following a 2D gaussian distribution with a star of every type in the pickles stellar library\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e147a7e8-0e32-493b-baa6-037eec7e3ae5", - "metadata": {}, - "outputs": [], - "source": [ - "from scopesim_templates.stellar import stars\n", - "from spextra.database import SpecLibrary\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f0013fb2-5cf0-4caa-a94b-91d375d0cdad", - "metadata": {}, - "outputs": [], - "source": [ - "lib = SpecLibrary(\"pickles\")\n", - "\n", - "spectypes = lib.template_names\n", - "nstars = len(spectypes)\n", - "\n", - "x = np.random.randn(nstars) * 10\n", - "y = np.random.randn(nstars) * 10\n", - "mags = np.linspace(10, 20, nstars)\n", - "\n", - "src = stars(filter_name=\"J\", amplitudes=mags, x=x, y=y, spec_types=spectypes, library=\"pickles\") " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "66e63272-831e-40a7-9bbd-ebaedd887c59", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[
\n", - " x y ref weight spec_types\n", - " arcsec arcsec \n", - " float64 float64 int64 float64 str6 \n", - " ------------------- ------------------- ----- ------- ----------\n", - " 2.007451224861396 0.1681071655341375 105 1.0 o5v\n", - " 8.593728449012035 1.0022428226715767 106 1.0 o8iii\n", - " 15.794690993340524 1.048888607243668 107 1.0 o9v\n", - " 0.12354371714603121 2.9528383261426407 13 1.0 b0i\n", - " -0.4828399272073905 -1.8187067436493747 14 1.0 b0v\n", - " 8.269207260479128 -3.4031373210003206 15 1.0 b12iii\n", - " 8.302089462394221 -9.269016047589648 16 1.0 b1i\n", - " 6.231844521598118 -6.148941889604722 17 1.0 b1v\n", - " -1.6268286899162279 18.51644268014349 18 1.0 b2ii\n", - " -0.3335389243832958 0.45403662143450824 19 1.0 b2iv\n", - " -6.112911809076027 8.548989247553019 20 1.0 b3i\n", - " -18.560993941068592 -2.7797644404218276 21 1.0 b3iii\n", - " ... ... ... ... ...\n", - " 14.288097551509324 5.393734390407308 94 1.0 m3iii\n", - " -3.4645252882727835 -7.660729789710751 95 1.0 m3v\n", - " 11.695467015092449 -0.9015068233794685 96 1.0 m4iii\n", - " -3.8953999312417196 -3.202120466135513 97 1.0 m4v\n", - " 21.966363419323997 -13.237923825721369 98 1.0 m5iii\n", - " -1.0680582531325613 -11.609390621966567 99 1.0 m5v\n", - " 12.865638982994145 3.020365939546597 100 1.0 m6iii\n", - " 6.38962463871831 -6.146996576751654 101 1.0 m6v\n", - " -16.111343878523737 11.306217143666768 102 1.0 m7iii\n", - " -4.618571067109303 10.398491786225657 103 1.0 m8iii\n", - " -0.4056773956303084 19.390809913922574 104 1.0 m9iii\n", - " 13.552636359858983 -8.507559747977913 86 1.0 m10iii]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "src.fields" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/notebooks/stellar.md b/docs/notebooks/stellar.md new file mode 100644 index 0000000..600e199 --- /dev/null +++ b/docs/notebooks/stellar.md @@ -0,0 +1,108 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# Stellar Module + +This module include general functions to work with stars + +## Stellar cluster + +In the following example we generate a young star cluster with a core radius `r_c=1 pc`, `M=1000 Msun` and located in the LMC (`d=50kpc`) + +```{code-cell} ipython3 +import numpy as np +from scopesim_templates.stellar import cluster + +src = cluster(mass=1E3, distance=50000, core_radius=1) +``` + +Lets have a look inside the object: + +```{code-cell} ipython3 +src.fields[0] +``` + +Here we can see the spatial information is in the form of an `astropy.Table`. + +The columns `x` and `y` show the position of each star in `arcsec` relative to the centre of the field of view. + +The column `ref` connects each star in this table to a spectrum in the following list: + +```{code-cell} ipython3 +src.spectra +``` + +When ScopeSim ingests this Source object, it will look primarily at these three columns. + +Now for a graphical representation of the cluster as it will be seen by ScopeSim: + +```{code-cell} ipython3 +import matplotlib.pyplot as plt + +plt.figure(figsize=(8, 8)) +plt.plot(src.fields[0]["x"], src.fields[0]["y"], '.') +plt.xlabel("X [arcsec]") +plt.ylabel("Y [arcsec]") +``` + +## Star Grid and Field + +These are two functions that are good to test simulations quickly + +```{code-cell} ipython3 +from scopesim_templates.stellar import star_field, star_grid + +field = star_field(n=400, mmin=15, mmax=25, width=20, height=20, filter_name="Ks") +grid = star_grid(n=400, mmin=15, mmax=25, separation=1 , filter_name="Ks") + +plt.figure(figsize=(14, 7)) +plt.subplot(121) + +size = np.log10(field.fields[0]["weight"])**2 +plt.scatter(field.fields[0]["x"], field.fields[0]["y"], s=size, marker="o") + +plt.subplot(122) + +size = np.log10(grid.fields[0]["weight"])**2 +plt.scatter(grid.fields[0]["x"], grid.fields[0]["y"], s=size, marker="o") +``` + +In both cases we generated 400 sources between magnitudes 15 (`mmin`) and 25 (`mmax`). + +`star_field` places the stars at random, whereas `star_grid` place them in a regular partern controled by `separation` distance. + +The size of the simbols illustrate the magnitudes of the stars + +## Stars + +The core of the `stellar` module is however the `star` function which can create any field according to the user needs + +In this case we generate a stellar field following a 2D gaussian distribution with a star of every type in the pickles stellar library + +```{code-cell} ipython3 +from scopesim_templates.stellar import stars +from spextra.database import SpecLibrary + +lib = SpecLibrary("pickles") + +spectypes = lib.template_names +nstars = len(spectypes) + +x = np.random.randn(nstars) * 10 +y = np.random.randn(nstars) * 10 +mags = np.linspace(10, 20, nstars) + +src = stars(filter_name="J", amplitudes=mags, x=x, y=y, spec_types=spectypes, library="pickles") + +src.fields +``` diff --git a/docs/source_object.md b/docs/source_object.md new file mode 100644 index 0000000..f055794 --- /dev/null +++ b/docs/source_object.md @@ -0,0 +1,77 @@ +# Source object interface + +## Input required for a Source object + +A ScopeSim `Source` object is essentially a 2+1D (x, y, lambda) description of +an on-sky object. Hence to build one of these objects we need the following +data: + +- A spatial description + + Either in table form (point sources) or in image/bitmap form (extended source) + +- A spectral description + + A spectrum with wavelength and flux information + +For example we could use two FITS images to describe the spatial flux +distribution of the young and old components of a spiral galaxy, and two spectra +from a starburst and an elliptical galaxy for the spectral description. + +Internally this infor mation is stored in the following attributes: + +- `.fields` - the spatial information \[Table or ImageHDU from astropy\] +- `.spectra` - the spectral information \[SourceSpectrum from synphot\] + +More detailed information about the `scopesim.Source` object can be found in +the `scopesim` documentation. + +Below is a description of how to initialise (create) a `Source` object + +## Spatial description + +### Series of arrays + +The most basic method for creating point source `Source` objects is by +passing a series of arrays to the following argument keys: + +- `x, y` : positions \[arcsec\] from the centre of the field of view +- `ref` : refers to the spectrum associated with a given point source + +Optionally, if multiple point sources use the same spectrum, a scaling factor +can be applied to each of the degenerate point sources: + +- `weight` : scaling factor + +Code Example: + +```python +src = Source(x=[1, 2], y=[-1, 5], ref=[0, 0], weight=[3.14, 0.5]) +``` + +### Astropy Table + +`astropy.table.Table` objects are a handy way to organize the information of the objects. +Each column can contain the spatial information and the reference to the spectra used in the table. + +### FITS ImageHDU + +Extended souces should provide an intensity map in the form of an +`fits.HDUImage` object. The header keywords should contain information +regarding position or the centre of the image (`CRPIXn`) relative to the +centre of the field of view (`CRVALn`) \[degrees\], and pixel scale +\[`CDELTn` | `CDn_m`\] \[degress / pixel\]. + +Each `ImageHDU` should also be +associated with an appropriate spectrum (`synphot.SourceSpectrum`) contained +in the list `.spectra`. The keyword `SPEC_REF` must be present and +refer to the list index of the corresponding spectrum in `.spectra`. + +## Spectral description + +### Series of arrays + +- `wave` +- `flux` + +### `synphot.SourceSpectrum` diff --git a/docs/source_object.rst b/docs/source_object.rst deleted file mode 100644 index 370c9bc..0000000 --- a/docs/source_object.rst +++ /dev/null @@ -1,93 +0,0 @@ -.. _Source Object: - - - -Source object interface -======================= - -Input required for a Source object ----------------------------------- - -A ScopeSim ``Source`` object is essentially a 2+1D (x, y, lambda) description of -an on-sky object. Hence to build one of these objects we need the following -data: - -* A spatial description - - Either in table form (point sources) or in image/bitmap form (extended source) - -* A spectral description - - A spectrum with wavelength and flux information - -For example we could use two FITS images to describe the spatial flux -distribution of the young and old components of a spiral galaxy, and two spectra -from a starburst and an elliptical galaxy for the spectral description. - -Internally this infor mation is stored in the following attributes: - -* ``.fields`` - the spatial information [Table or ImageHDU from astropy] -* ``.spectra`` - the spectral information [SourceSpectrum from synphot] - -More detailed information about the ``scopesim.Source`` object can be found in -the ``scopesim`` documentation. - -Below is a description of how to initialise (create) a ``Source`` object - - -Spatial description -------------------- - -Series of arrays -++++++++++++++++ -The most basic method for creating point source ``Source`` objects is by -passing a series of arrays to the following argument keys: - -* ``x, y`` : positions [arcsec] from the centre of the field of view -* ``ref`` : refers to the spectrum associated with a given point source - -Optionally, if multiple point sources use the same spectrum, a scaling factor -can be applied to each of the degenerate point sources: - -* ``weight`` : scaling factor - -Code Example:: - - src = Source(x=[1, 2], y=[-1, 5], ref=[0, 0], weight=[3.14, 0.5]) - -Astropy Table -+++++++++++++ - -`astropy.table.Table` objects are a handy way to organize the information of the objects. -Each column can contain the spatial information and the reference to the spectra used in the table. - - - -FITS ImageHDU -+++++++++++++ -Extended souces should provide an intensity map in the form of an -``fits.HDUImage`` object. The header keywords should contain information -regarding position or the centre of the image (``CRPIXn``) relative to the -centre of the field of view (``CRVALn``) [degrees], and pixel scale -[``CDELTn`` | ``CDn_m``] [degress / pixel]. - -Each ``ImageHDU`` should also be -associated with an appropriate spectrum (``synphot.SourceSpectrum``) contained -in the list ``.spectra``. The keyword ``SPEC_REF`` must be present and -refer to the list index of the corresponding spectrum in ``.spectra``. - - -Spectral description --------------------- - - - - -Series of arrays -++++++++++++++++ -* ``wave`` -* ``flux`` - - -``synphot.SourceSpectrum`` -++++++++++++++++++++++++++ diff --git a/docs/starting.rst b/docs/starting.rst deleted file mode 100644 index add23be..0000000 --- a/docs/starting.rst +++ /dev/null @@ -1,51 +0,0 @@ -.. _start: - -*************** -Getting Started -*************** - - -``scopesim.source.Source`` objects are composed of a spatial description and a spectral one. Spatial description -can be ``astropy.table.Table`` objects for point sources or ``astropy.fits.ImageHDU`` for extended sources. -Spectral description is provided as ``synphot.SourceSpectrum`` and compatible objects - -For example, the creation of ``scopesim.source.Source`` objects might require quite a bit of interaction from the -user - -.. code-block:: python - - - import numpy as np - import matplotlib.pyplot as plt - from astropy.io import fits - import synphot - from scopesim import Source - - # creation of a - x, y = np.meshgrid(np.arange(100), np.arange(100)) - img = np.exp(-1 * ( ( (x - 50) / 5)**2 + ( (y - 50) / 5)**2 ) ) - - hdr = fits.Header(dict(NAXIS=2, - NAXIS1=img.shape[0]+1, - NAXIS2=img.shape[1]+1, - CRPIX1=(img.shape[0] + 1) / 2, - CRPIX2=(img.shape[1] + 1) / 2, - CRVAL1=0, - CRVAL2=0, - CDELT1=0.2/3600, - CDELT2=0.2/3600, - CUNIT1="DEG", - CUNIT2="DEG", - CTYPE1='RA---TAN', - CTYPE2='DEC--TAN')) - hdu = fits.ImageHDU(data=img, header=hdr) - - wave = np.arange(1000, 35000, 10 ) - bb = synphot.models.BlackBody1D(temperature=5000) - sp = synphot.SourceSpectrum(synphot.Empirical1D, points=wave, lookup_table=bb(wave)) - - src = Source(image_hdu=hdu, spectra=sp) - - plt.imshow(src.fields[0].data) - src.spectra[0].plot() - diff --git a/poetry.lock b/poetry.lock index 2e16979..249d609 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,23 @@ # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +[[package]] +name = "accessible-pygments" +version = "0.0.5" +description = "A collection of accessible pygments styles" +optional = false +python-versions = ">=3.9" +files = [ + {file = "accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7"}, + {file = "accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872"}, +] + +[package.dependencies] +pygments = ">=1.5" + +[package.extras] +dev = 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@@ -53,11 +53,11 @@ skycalc_cli = "*" # consider removal optional = true [tool.poetry.group.docs.dependencies] -sphinx = "^5.3.0" -sphinx-rtd-theme = "^0.5.1" -jupyter-sphinx = "^0.2.3" +sphinx = "^6.0.0" +sphinx-book-theme = "^1.1.3" sphinxcontrib-apidoc = "^0.4.0" -nbsphinx = "^0.9.3" +sphinx-copybutton = "^0.5.2" +myst-nb = "^1.0.0" numpydoc = "^1.6.0" [tool.poetry.urls] diff --git a/scopesim_templates/calibration/calibration.py b/scopesim_templates/calibration/calibration.py index b84bf5a..5fafbe4 100644 --- a/scopesim_templates/calibration/calibration.py +++ b/scopesim_templates/calibration/calibration.py @@ -1,5 +1,9 @@ # -*- coding: utf-8 -*- -"""Contains simple source functions for testing and calibration.""" +"""Contains simple source functions for testing and calibration. + +Simple templates that could be used to simulate calibration frames. +Make sure to turn off the corresponding effects during the simulation. +""" import warnings diff --git a/scopesim_templates/extragalactic/__init__.py b/scopesim_templates/extragalactic/__init__.py index dc67b64..2320e39 100644 --- a/scopesim_templates/extragalactic/__init__.py +++ b/scopesim_templates/extragalactic/__init__.py @@ -1,3 +1,6 @@ +# -*- coding: utf-8 -*- +"""Templates to simulate extragalactic sources.""" + from .galaxies import * from .clusters import * from .agns import * diff --git a/scopesim_templates/misc/misc.py b/scopesim_templates/misc/misc.py index 1c9f187..b2e6aef 100644 --- a/scopesim_templates/misc/misc.py +++ b/scopesim_templates/misc/misc.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -"""TBA.""" +"""Templates that could be used to simulate more general sources.""" from pathlib import Path import warnings diff --git a/scopesim_templates/stellar/__init__.py b/scopesim_templates/stellar/__init__.py index 4571b28..ff16e8a 100644 --- a/scopesim_templates/stellar/__init__.py +++ b/scopesim_templates/stellar/__init__.py @@ -1,2 +1,5 @@ +# -*- coding: utf-8 -*- +"""Templates to simulate stellar sources.""" + from .clusters import * from .stars import *