diff --git a/.gitignore b/.gitignore
index 7909d367..0b468357 100755
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
+# SPISEA-generated isochrones from test functions
+spisea/tests/isochrones/
+
# Compiled files
*.py[co]
*.a
@@ -51,4 +54,7 @@ distribute-*.tar.gz
# OS Generated Files
.DS_Store
-._*
\ No newline at end of file
+._*
+
+# Test files generated
+spisea/tests/isochrones
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 01ce5488..40ae08cb 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -9,6 +9,13 @@ version: 2
#conda:
# environment: .environment.yml
+# Set the version of Python and other tools you might need
+build:
+ os: ubuntu-22.04
+ tools:
+ python: "3.7"
+
+
# Build documentation in the docs/ directory with Sphinx
sphinx:
configuration: docs/conf.py
@@ -16,9 +23,8 @@ sphinx:
# Optionally build your docs in additional formats such as PDF and ePub
formats: all
-# Optionally set the version of Python and requirements required to build your docs
+# Set the requirements required to build your docs
python:
- version: 3.7
install:
- - requirements: docs/requirements.txt
+ - requirements: docs/requirements.txt
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 00000000..8ef8b191
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,39 @@
+FROM ubuntu:20.04
+
+RUN apt-get update && apt-get upgrade -y && apt-get clean
+
+RUN apt-get install -y curl python3 python3-dev python3-distutils python3-pip git wget
+
+RUN pip3 install astropy pysynphot scipy numpy matplotlib
+
+RUN ln -s /usr/bin/python3 /usr/bin/python
+
+RUN export PYTHONPATH=$PYTHONPATH:/SPISEA
+
+RUN cd / && git clone https://github.com/astropy/SPISEA.git
+
+ENV PYTHONPATH "${PYTHONPATH}:/SPISEA/"
+
+# Add data to cdbs folder
+RUN mkdir /cdbs
+WORKDIR /cdbs
+RUN wget https://archive.stsci.edu/hlsps/reference-atlases/hlsp_reference-atlases_hst_multi_everything_multi_v10_sed.tar
+RUN wget https://archive.stsci.edu/hlsps/reference-atlases/hlsp_reference-atlases_hst_multi_star-galaxy-models_multi_v3_synphot2.tar
+RUN tar -xvf hlsp_reference-atlases_hst_multi_everything_multi_v10_sed.tar && rm hlsp_reference-atlases_hst_multi_everything_multi_v10_sed.tar
+RUN tar -xvf hlsp_reference-atlases_hst_multi_star-galaxy-models_multi_v3_synphot2.tar && rm hlsp_reference-atlases_hst_multi_star-galaxy-models_multi_v3_synphot2.tar
+
+# Move data folders to cdbs
+RUN mv /cdbs/grp/redcat/trds/comp /cdbs
+RUN mv /cdbs/grp/redcat/trds/mtab /cdbs
+RUN mv /cdbs/grp/redcat/trds/grid /cdbs
+
+RUN mkdir /cdbs/models
+WORKDIR /cdbs/models
+RUN wget http://astro.berkeley.edu/~jlu/spisea/spisea_models.tar.gz && wget http://astro.berkeley.edu/~jlu/spisea/spisea_cdbs.tar.gz
+RUN tar -xvf spisea_cdbs.tar.gz && tar -xvf spisea_models.tar.gz && rm spisea_cdbs.tar.gz && rm spisea_models.tar.gz
+
+ENV PYSYN_CDBS /cdbs/models/cdbs/
+ENV SPISEA_MODELS /cdbs/models
+
+
+
diff --git a/README.md b/README.md
index cbb42b51..0b8ef4ce 100755
--- a/README.md
+++ b/README.md
@@ -1,3 +1,7 @@
+
+ 
+
+
# SPISEA: Stellar Population Interface for Stellar Evolution and Atmospheres
SPISEA is an python package that generates single-age, single-metallicity
@@ -24,24 +28,22 @@ demonstrating how to use SPISEA.
If you use SPISEA in your research, please cite [Hosek et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020arXiv200606691H/abstract).
-## Version Update
-Version 1 of this software package was called PyPopStar. Due to a naming conflict,
-we have renamed the package of SPISEA for version 2 and beyond.
-For reference, v2.0.0 has the same functionality as v1.0.1.
-
-Instructions for updating from version 1 to version 2 are provided in
-the documentation [here](https://spisea.readthedocs.io/en/latest/version_switch.html#version).
+## Change Log
+SPISEA is actively supported and is growing in functionality, and
+subsequently has had several updates since its
+initial release. See the
+[main documentation page](https://spisea.readthedocs.io/en/latest/)
+for the change log describing the updates in each release.
## Contributions
-We encourage contributions to SPISEA, particularly those that add
-support for star formation histories, new models, higher spectral
-resolution, etc. For feature additions, we ask that users make their
+We welcome and encourage contributions to SPISEA!
+ For feature additions, we ask that users make their
own fork of the repository, make their changes, and then submit a pull
request to the "dev" branch.
All contributions will be acknowledged on the
-[contributors page](https://spisea.readthedocs.io/en/dev/contributors.html#contributors). Contributors
-with features used in code releases will be co-authors in future SPISEA software papers.
+[contributors page](https://spisea.readthedocs.io/en/dev/contributors.html#contributors) (with permission).
+Contributors with features used in code releases will be co-authors in future SPISEA software papers.
## License
This project is Copyright (c) Matthew Hosek Jr., Jessica Lu, Casey
@@ -51,4 +53,8 @@ the [Astropy package template](https://github.com/astropy/package-template)
which is licensed under the BSD 3-clause license. See the licenses folder for
more information. This program is distributed in the hope that it will
be useful, but WITHOUT ANY WARRANTY;
-without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+PARTICULAR PURPOSE.
+
+
+Logo by Natasha Abrams
diff --git a/SPISEA_logo_final.png b/SPISEA_logo_final.png
new file mode 100644
index 00000000..94e53dd3
Binary files /dev/null and b/SPISEA_logo_final.png differ
diff --git a/docs/Quick_Start_Make_Cluster.ipynb b/docs/Quick_Start_Make_Cluster.ipynb
index c84a30ff..dd574b2e 100755
--- a/docs/Quick_Start_Make_Cluster.ipynb
+++ b/docs/Quick_Start_Make_Cluster.ipynb
@@ -11,7 +11,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "This is a quick start guide to making a synthetic cluster using the SPISEA package. The cluster is constructed using a user-specified isochrone and initial mass function (IMF). Detailed documentation is provided in the ReadtheDocs page (https://pypopstar.readthedocs.io/en/latest/).\n",
+ "This is a quick start guide to making a synthetic cluster using the SPISEA package. The cluster is constructed using a user-specified isochrone and initial mass function (IMF). Detailed documentation is provided in the ReadtheDocs page (https://spisea.readthedocs.io/en/latest/index.html).\n",
"\n",
"Before starting this tutorial, it is assumed that SPISEA has been installed and the user's python path has been altered to include the SPISEA top-level directory"
]
@@ -80,20 +80,29 @@
"# the HST WFC3-IR F127M, F139M, and F153M filters\n",
"filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m']\n",
"\n",
- "# Make Isochrone object. Note that is calculation will take a few minutes, unless the \n",
- "# isochrone has been generated previously.\n",
+ "# Specify the directory we want the output isochrone\n",
+ "# table saved in. If the directory does not already exist,\n",
+ "# SPISEA will create it.\n",
+ "iso_dir = 'isochrones/'\n",
+ "\n",
+ "# Make IsochronePhot object. Note that this will take a minute or two, \n",
+ "# unless the isochrone has been generated previously.\n",
+ "#\n",
+ "# Note that this is not show all of the user options \n",
+ "# for IsochronePhot. See docs for complete list of options.\n",
"my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n",
" evo_model=evo_model, atm_func=atm_func,\n",
- " red_law=red_law, filters=filt_list)"
+ " red_law=red_law, filters=filt_list,\n",
+ " iso_dir=iso_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Once calculated, the isochrone will be written as a fits file to a location set by the \"iso_dir\" keyword (not shown here; default location is current working directory). In the future, the IsochronePhot function will read this file directly rather than recalculating the isochrone again. \n",
+ "Once calculated, the isochrone will be written as a fits file to a location set by the \"iso_dir\" keyword (note: if iso_dir is not defined, the isochrone is saved in the current working directory by default). In the future, the IsochronePhot function will read this file directly rather than recalculating the isochrone again. \n",
"\n",
- "The file name will be the following: \"iso_logAge_AKs_distance_metallicity.fits, using the specified values"
+ "The output file is named as: \"iso_logAge_AKs_distance_metallicity.fits, using the specified values"
]
},
{
@@ -108,35 +117,35 @@
" L Teff ... m_hst_f153m \n",
" W K ... \n",
"---------------------- ------------------ ... ------------------\n",
- " 8.660109049222469e+24 2997.482453641835 ... 21.152021446472187\n",
- " 9.046193280874383e+24 3008.7858260184175 ... 21.10504146351601\n",
- " 9.449142476745298e+24 3020.1687795233192 ... 21.058402938590124\n",
- " 9.869958611246552e+24 3031.631609685951 ... 21.011969614374383\n",
- " 1.076707529684324e+25 3054.6500837349886 ... 20.919821469802322\n",
- "1.0829426036763875e+25 3065.3879429582494 ... 20.915499755622307\n",
- "1.1480726894593663e+25 3078.908328291189 ... 20.85389598461618\n",
- " 1.221834686575575e+25 3093.002145149403 ... 20.788339065157267\n",
- "1.3043887291289771e+25 3107.5858797888227 ... 20.717377389444135\n",
- "1.3956010100862058e+25 3122.5861578385725 ... 20.64218049226777\n",
+ " 8.660109049222469e+24 2997.482453641835 ... 21.1520214464892\n",
+ " 9.046193280874383e+24 3008.7858260184175 ... 21.10504146353302\n",
+ " 9.449142476745298e+24 3020.1687795233192 ... 21.058402938607138\n",
+ " 9.869958611246552e+24 3031.631609685951 ... 21.011969614391397\n",
+ " 1.076707529684324e+25 3054.6500837349886 ... 20.919821469819336\n",
+ "1.0829426036763875e+25 3065.3879429582494 ... 20.91549975563932\n",
+ "1.1480726894593663e+25 3078.908328291189 ... 20.853895984633194\n",
+ " 1.221834686575575e+25 3093.002145149403 ... 20.78833906517428\n",
+ "1.3043887291289771e+25 3107.5858797888227 ... 20.71737738946115\n",
+ "1.3956010100862058e+25 3122.5861578385725 ... 20.64218049228478\n",
" ... ... ... ...\n",
- " 3.146902975957394e+32 13997.739388490047 ... 5.930259429665395\n",
- "3.1460717791216517e+32 13998.892300822487 ... 5.9308025346063955\n",
- "3.1452525359273068e+32 14000.139431008332 ... 5.93136239765411\n",
- " 3.144405489181849e+32 14001.378759406476 ... 5.931929284462485\n",
- "3.1435076384266162e+32 14002.5320654016 ... 5.9324947269685415\n",
- "3.1426231762052773e+32 14003.694937388533 ... 5.93305773061722\n",
- "3.1418572729129726e+32 14004.747427385868 ... 5.933555391417904\n",
- "3.1412514101696487e+32 14005.539485757616 ... 5.9339401294056975\n",
- " 3.14065875101774e+32 14006.427618268113 ... 5.934341601398296\n",
- " 3.140072730828415e+32 14007.35269293023 ... 5.934748981956298\n",
+ " 3.146902975957394e+32 13997.739388490047 ... 5.930259429682409\n",
+ "3.1460717791216517e+32 13998.892300822487 ... 5.930802534623409\n",
+ "3.1452525359273068e+32 14000.139431008332 ... 5.931362397671123\n",
+ " 3.144405489181849e+32 14001.378759406476 ... 5.931929284479498\n",
+ "3.1435076384266162e+32 14002.5320654016 ... 5.932494726985555\n",
+ "3.1426231762052773e+32 14003.694937388533 ... 5.933057730634233\n",
+ "3.1418572729129726e+32 14004.747427385868 ... 5.933555391434915\n",
+ "3.1412514101696487e+32 14005.539485757616 ... 5.9339401294227105\n",
+ " 3.14065875101774e+32 14006.427618268113 ... 5.934341601415309\n",
+ " 3.140072730828415e+32 14007.35269293023 ... 5.9347489819733115\n",
"Length = 694 rows\n"
]
}
],
"source": [
- "# The individual points in the isochrone can be accessed using the points subfunction \n",
- "# on the isochrone object. This produces an astropy table that contains the \n",
- "# list of stellar masses in the isochrone and their associated properties\n",
+ "# The stars in the isochrone and associated properties \n",
+ "# are stored in an astropy table called \"points\" \n",
+ "# within the IsochronePhot object. \n",
"print(my_iso.points)"
]
},
@@ -154,7 +163,17 @@
}
],
"source": [
- "# The isochrone table has meta keywords describing its properties\n",
+ "# The isochrone table has meta keywords describing its input properties:\n",
+ "# REDLAW: which redlaw object was used\n",
+ "# ATMFUNC: atmosphere grid was used\n",
+ "# EVOMODEL: evolution model grid used\n",
+ "# LOGAGE: log(Age) of isochrone\n",
+ "# AKS: total extinction used\n",
+ "# DISTANCE: distance used\n",
+ "# METAL_IN: metallicity requested by user, in [M/H]\n",
+ "# METAL_ACT: actual metallicity of model, in [M/H] \n",
+ "# (only relevant if user chooses metallicity other than defined grid-points)\n",
+ "# WAVEMIN, WAVEMAX: the minimum and maximum wavelengths of the stellar spectra (angstroms)\n",
"print(my_iso.points.meta)"
]
},
@@ -172,6 +191,7 @@
}
],
"source": [
+ "# See Isochrone Object documentation for column definitions\n",
"print('The columns in the isochrone table are: {0}'.format(my_iso.points.keys()))"
]
},
@@ -189,6 +209,7 @@
}
],
"source": [
+ "# Example case:\n",
"# Identify a 1 M_sun star, print F127M, F139M, and F153M mags\n",
"idx = np.where( abs(my_iso.points['mass'] - 1.0) == min(abs(my_iso.points['mass'] - 1.0)) )[0]\n",
"f127m = np.round(my_iso.points[idx[0]]['m_hst_f127m'], decimals=3)\n",
@@ -205,7 +226,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 7,
@@ -226,7 +247,7 @@
}
],
"source": [
- "# Make a color-magnitude diagram\n",
+ "# Make a color-magnitude diagram from the isochrone\n",
"py.figure(1, figsize=(10,10))\n",
"py.clf()\n",
"py.plot(my_iso.points['m_hst_f127m'] - my_iso.points['m_hst_f153m'], \n",
@@ -250,7 +271,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "PopStar offers a range of initial mass functions (IMFs) a user can use from to make the cluster. In addition to the parameters defining the IMF, the user can pass in a PopStar multiplicity object, which defines the multiplicity properties of the population. The default multiplicity is None (e.g. all stars are single).\n",
+ "SPISEA offers a range of initial mass functions (IMFs) a user can use from to make the cluster. In addition to the parameters defining the IMF, the user can input a SPISEA multiplicity object, which defines the multiplicity properties of the population. The default multiplicity is None (e.g. all stars are single).\n",
"\n",
"Here we define a Kroupa IMF using the Multiplicity properties defined in Lu+13. "
]
@@ -261,10 +282,12 @@
"metadata": {},
"outputs": [],
"source": [
- "# Make multiplicity object\n",
+ "# Make multiplicity object Here, we use the MultiplicityUnresolved object, \n",
+ "# based on Lu+13. This means that star systems will be unresolved, i.e., \n",
+ "# that all components of a star system are combined into a single \"star\" in the cluster\n",
"imf_multi = multiplicity.MultiplicityUnresolved()\n",
"\n",
- "# Make IMF object; we'll use a broken power law with the parameters from Kroupa+01.\n",
+ "# Make IMF object; we'll use a broken power law with the parameters from Kroupa+01\n",
"\n",
"# NOTE: when defining the power law slope for each segment of the IMF, we define\n",
"# the entire exponent, including the negative sign. For example, if dN/dm $\\propto$ m^-alpha,\n",
@@ -280,10 +303,16 @@
"metadata": {},
"source": [
"### Step 3: Make the Cluster \n",
- "#### Option 1: No compact objects\n",
- "To create the cluster, the user passes in an isochrone object, and imf object, and specifies the total cluster mass. Here we will make a 10^5 M_sun cluster using the isochrone and imf we have defined.\n",
+ "#### Option 1: Resolved Cluster without compact objects\n",
+ "\n",
+ "Here we make a resolved cluster using the ResolvedCluster object. \n",
+ "\n",
+ "To create the cluster, the user passes in an isochrone object, and imf object, and specifies the total cluster mass. Here we will make a 10^5 M_sun cluster using the isochrone and imf we have defined. \n",
"\n",
- "The individual star systems in the cluster and their properties are stored in an astropy table accessed by the star_systems subfunction on the cluster object. Note that the photometry of these star systems includes the contributions from all companions, which are generated probabilistically using the multiplicity defined in the IMF object."
+ "###### Some Notes\n",
+ "1. Unless an IFMR object is defined, no compact objects are included. Stars that have evolved into compact objects will be dropped from the cluster. \n",
+ "2. Stars generated by the IMF object that have masses below the lowest mass in the evolution model are dropped from the cluster.\n",
+ "2. If you wish to create a cluster with differential extinction included in the output photometry, then you can use the ResolvedClusterDiffRedden object. It is the same as ResolvedCluster, but with an additional parameter to define dAKs, which characterizes the spread of extinction within the cluster"
]
},
{
@@ -297,32 +326,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Found 69 stars out of mass range\n",
- " mass isMultiple ... m_hst_f153m N_companions\n",
- "------------------- ---------- ... ------------------ ------------\n",
- " 0.3902825393188909 False ... 19.198675247781733 0\n",
- "0.36442534730486614 False ... 19.299625963230316 0\n",
- " 0.2960597619579401 False ... 19.622871173104592 0\n",
- " 4.020275631753382 True ... 14.942859924256084 1\n",
- "0.22535244432792711 False ... 20.04719216513875 0\n",
- " 0.4337891871272154 False ... 19.043037175056032 0\n",
- " 0.2550352762897599 False ... 19.84725853661653 0\n",
- " 0.3886897076801539 False ... 19.204793244269553 0\n",
- " 0.2207786288985551 False ... 20.07793050759004 0\n",
- " 3.1627242881250925 True ... 15.435498104534354 1\n",
- " ... ... ... ... ...\n",
- " 2.0209398200626008 False ... 15.286966341315408 0\n",
- " 0.5562568588776408 False ... 18.69391283084209 0\n",
- "0.29658992418674723 True ... 18.867557762455142 1\n",
- " 1.8149463760632611 False ... 16.059048383668213 0\n",
- " 0.9636380994477444 False ... 17.85758316236885 0\n",
- " 0.5042624118055835 False ... 18.83026179414743 0\n",
- "0.29801917051230525 False ... 19.612203004764787 0\n",
- " 0.2934590032614954 False ... 19.6370312274348 0\n",
- "0.23754184269272524 False ... 19.967395454198524 0\n",
- " 0.4400444501504912 False ... 19.022931594125073 0\n",
- " 3.3262445603993838 False ... 15.366054043628987 0\n",
- "Length = 79950 rows\n"
+ "Found 74 stars out of mass range\n"
]
}
],
@@ -331,22 +335,70 @@
"mass = 10**5.\n",
"\n",
"# Make cluster object\n",
- "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)\n",
+ "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The individual stars (or star systems) in the cluster are stored in an astropy table called \"star_systems\" within the cluster object. If a multiplicity object is used, then an additional \"companions\" table is created that contains the properties of the companions to the primary star within each star system. See cluster object documentation for a description of the columns in these tables. \n",
"\n",
+ "If a multiplicity object is used, then the photometry in the star_systems table is the COMBINED photometry of the system; it includes the contributions from all companions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " mass isMultiple ... m_hst_f153m N_companions\n",
+ "------------------- ---------- ... ------------------ ------------\n",
+ " 0.6876030977679913 False ... 18.389044207067464 0\n",
+ " 0.5955047093772027 False ... 18.59784842254386 0\n",
+ "0.22888954345052595 False ... 20.023421078238613 0\n",
+ " 0.7674862431783736 True ... 18.0051258246746 1\n",
+ " 0.5642222892161842 False ... 18.674063270037458 0\n",
+ " 1.089763617194751 False ... 17.646195710306777 0\n",
+ " 0.5764417363236184 False ... 18.644066247034154 0\n",
+ " 0.6945802953923282 False ... 18.373593985664552 0\n",
+ " 0.2918079653214152 False ... 19.646020445469127 0\n",
+ " 0.2654043135986742 False ... 19.784918937378045 0\n",
+ " ... ... ... ... ...\n",
+ "0.21788083183588838 False ... 20.09740516281386 0\n",
+ " 0.2084958320428418 False ... 20.160477086462425 0\n",
+ " 7.177783813711204 True ... 13.76389690674836 1\n",
+ " 0.6970980816560024 True ... 18.15184809900597 1\n",
+ " 0.6631502158305624 False ... 18.441648615714623 0\n",
+ " 1.2947809690914678 False ... 17.3222437955907 0\n",
+ "0.25029053901467957 False ... 19.87836996594056 0\n",
+ " 0.9500007704623843 False ... 17.88252985999676 0\n",
+ " 0.4392328921597474 False ... 19.02554009274596 0\n",
+ " 0.2605330473087063 False ... 19.813906355345033 0\n",
+ " 0.2341168935953943 False ... 19.990680958036442 0\n",
+ "Length = 78379 rows\n"
+ ]
+ }
+ ],
+ "source": [
"# Look at star systems table\n",
"print(cluster.star_systems)"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The cluster table contains these columns: ['mass', 'isMultiple', 'systemMass', 'Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase', 'm_hst_f127m', 'm_hst_f139m', 'm_hst_f153m', 'N_companions']\n"
+ "The cluster table contains these columns: ['mass', 'isMultiple', 'systemMass', 'Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase', 'metallicity', 'm_hst_f127m', 'm_hst_f139m', 'm_hst_f153m', 'N_companions']\n"
]
}
],
@@ -356,7 +408,49 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "system_idx mass ... m_hst_f139m m_hst_f153m \n",
+ "---------- ------------------- ... ------------------ ------------------\n",
+ " 3 0.24675997749084191 ... 20.563701959287826 19.90251395648469\n",
+ " 13 0.37427415165641575 ... 19.935549695054327 19.26089149163212\n",
+ " 21 1.1031866473217342 ... 18.27788436660256 17.62478259245194\n",
+ " 27 0.2261763927189317 ... 20.700050305132947 20.041654817137246\n",
+ " 30 0.24351327466283715 ... 20.585549714235427 19.924810664357523\n",
+ " 33 1.3739797199235617 ... 17.787945415289006 17.192454221341634\n",
+ " 36 0.8853706000102676 ... 18.68802065076431 17.993905280315655\n",
+ " 36 0.5642335448205728 ... 19.37010845835326 18.674035221483344\n",
+ " 47 0.2966822705750629 ... 20.28632611434612 19.61948187143378\n",
+ " 54 0.24975150440679514 ... 20.543655971454946 19.88205619159839\n",
+ " ... ... ... ... ...\n",
+ " 78351 1.2376751887661364 ... 18.035126418759774 17.412404186653344\n",
+ " 78354 0.31213069455188613 ... 20.204014011448535 19.535371598871034\n",
+ " 78357 0.27679441089161594 ... 20.38923702443474 19.72453916098156\n",
+ " 78360 0.9014988021676168 ... 18.657742310702165 17.965414404445802\n",
+ " 78364 0.6888881335136626 ... 19.08900955643712 18.386198639570274\n",
+ " 78366 0.6980230969223282 ... 19.068990255125353 18.365970287906695\n",
+ " 78366 4.727838039253875 ... 15.020104863055376 14.6509524800243\n",
+ " 78367 1.653298517897867 ... 17.061733897360025 16.51120386646886\n",
+ " 78367 3.3438186255283364 ... 15.73063278758436 15.355422018083411\n",
+ " 78370 0.9370632968186573 ... 18.59248602156636 17.905121399833412\n",
+ " 78371 0.230895129296845 ... 20.66949566003222 20.010460212653776\n",
+ "Length = 16156 rows\n"
+ ]
+ }
+ ],
+ "source": [
+ "# The companions table is accessed in a similar way\n",
+ "print(cluster.companions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
"metadata": {
"scrolled": true
},
@@ -364,16 +458,16 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 11,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -406,30 +500,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Option 2: Compact objects (white dwarfs, neutron stars, and black holes)\n",
- "This is quite similar to the above, but includes compact objects. The additional piece of information required is to choose an initial-final mass relation (IFMR.) Currently there's only one choice in PopStar, so that makes your decision easy...\n",
+ "#### Option 2: Resolved cluster with compact objects (white dwarfs, neutron stars, and black holes)\n",
+ "This is quite similar to the above, but includes compact objects. The additional piece of information required is to choose an initial-final mass relation (IFMR.)\n",
"\n",
"The output is the same as if we were making a cluster without using an IFMR. However, you can tell that compact objects are made by looking at the 'phase' keyword. Black holes have 'phase' = 103, neutron stars have 'phase' = 102, and white dwarfs have 'phase' = 101. For these compact objects, the luminosity and temperature will return values of zero, and photometry will return nan, since we are assuming they are totally dark.\n",
"\n",
- "Here, we make 4 different clusters, each of mass $10^6 M_\\odot$, by taking different combinations of age (either 100 Myr or 10 Gyr) and IMF (top-heavy or Kroupa). We then look at the different distributions of BH and WD masses. (Since NSs are all assigned a single mass in the IFMR, the mass distribution there is not very interesting. However, the relative number of NSs for each cluster will be different.)"
+ "Here, we make 4 different clusters, each of mass $10^6 M_\\odot$, by taking different combinations of age (either 100 Myr or 10 Gyr) and IMF (top-heavy or Kroupa). We then look at the different distributions of BH and WD masses. "
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 14,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found 5564 stars out of mass range\n",
- "Found 4249 stars out of mass range\n",
- "Found 100598 stars out of mass range\n",
- "Found 74050 stars out of mass range\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Create isochrone object \n",
"filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n",
@@ -440,15 +523,41 @@
"\n",
"my_iso_old = synthetic.IsochronePhot(10, 0, 10,\n",
" evo_model = evolution.MISTv1(),\n",
- " filters=filt_list)\n",
- "# Create IMF object \n",
+ " filters=filt_list)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create IMF objects \n",
"massLimits = np.array([0.1, 0.5, 120])\n",
"powers_kroupa = np.array([-1.3, -2.3])\n",
"powers_theavy = np.array([-1.3, -1.3]) # top heavy\n",
"trunc_kroupa = imf.IMF_broken_powerlaw(massLimits, powers_kroupa)\n",
- "trunc_theavy = imf.IMF_broken_powerlaw(massLimits, powers_theavy)\n",
- "\n",
- "# Make cluster \n",
+ "trunc_theavy = imf.IMF_broken_powerlaw(massLimits, powers_theavy)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 5586 stars out of mass range\n",
+ "Found 4106 stars out of mass range\n",
+ "Found 99584 stars out of mass range\n",
+ "Found 73734 stars out of mass range\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Make clusters \n",
"cluster_mass = 10**6\n",
"cluster_young_theavy = synthetic.ResolvedCluster(my_iso_young, trunc_theavy, cluster_mass, ifmr=my_ifmr)\n",
"cluster_old_theavy = synthetic.ResolvedCluster(my_iso_old, trunc_theavy, cluster_mass, ifmr=my_ifmr)\n",
@@ -464,22 +573,22 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 13,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
diff --git a/docs/add_atmo_models.rst b/docs/add_atmo_models.rst
index e625b718..3fbe6e54 100644
--- a/docs/add_atmo_models.rst
+++ b/docs/add_atmo_models.rst
@@ -4,8 +4,38 @@
Adding Atmosphere Models
========================================
Stellar atmosphere grids are implemented in SPISEA using STSci's `pysynphot `_ and CDBS infrastructure.
-Adding a new atmosphere grid involves saving the models in a new sub-directory under your ``PYSYN_CDBS`` directory and creating a new function in ``atmospheres.py`` to access those files.
+Adding a new atmosphere grid involves saving the models in a new
+sub-directory under your ``PYSYN_CDBS`` directory and creating a new
+function in ``atmospheres.py`` to access those files.
-Detailed documentation on this is coming soon. In the meantime, let us know on the Github `issue tracker
+We'd recommend looking at the existing atmosphere model directories as
+a template for how to make a new one. Some general notes:
+
+* The new atmosphere grid you implement should be placed in the
+ ``/cdbs/grid/`` directory
+
+* Inside the new directory you need two things: a ``catalog.fits``
+ file (which tells SPISEA the Teff, logg, and metallicity of the
+ atmospheres in your grid) and then the atmosphere model files
+ themselves.
+
+* The ``catalog.fits`` file MUST follow the format where one column
+ labeled 'INDEX', which gives the ',,' for
+ each atmosphere, and then one column labeled 'FILENAME', which gives the path to the file containing that atmosphere.
+
+* There is some flexibility as to how the atmosphere model
+ files are organized. In ``BTSettle_2015_rebin``, you'll see that each
+ file is simply stored in the same directory as the ``catalog.fits``
+ file, with one model per file. In ``phoenix_v16_rebin``, the files are
+ separated into sub-directory by metallicity, and then there is one
+ file for each teff and then multiple columns within that file for
+ different logg. It doesn't matter what you choose, as long as it is
+ reflected in the 'INDEX' column of catalog.fits.
+
+* For the atmospheres, use units of angstroms for the wavelength and
+ "FLAM" for flux ([erg/s/cm^2/A]; see `pysynphot docs
+ `_)
+
+More detailed documentation on this is coming soon. In the meantime, let us know on the Github `issue tracker
`_ if you'd like to
implement a new atmospheric model grid.
diff --git a/docs/add_evo_models.rst b/docs/add_evo_models.rst
index c1f4a30f..49655466 100644
--- a/docs/add_evo_models.rst
+++ b/docs/add_evo_models.rst
@@ -14,7 +14,7 @@ have the new models you add be added to the SPISEA package and made
available to the community, please fork or branch off of the
development repository and then submit merge requests to add your
changes to the package. These contributions will be added to the
-master branch (with attributes given!) in the next code update.
+main branch (with attributes given!) in the next code update.
.. _setup:
diff --git a/docs/add_filters.rst b/docs/add_filters.rst
index 1bbfef12..4fe10c88 100644
--- a/docs/add_filters.rst
+++ b/docs/add_filters.rst
@@ -12,8 +12,10 @@ If the user wants to add new photometric filters to SPISEA, there are 4 main ste
call the new function in ``filters.py`` when the new filter
string is called.
4) Edit the ``get_obs_str()`` function in ``synthetic.py`` to
- convert between column name and filter string (e.g input string
- for get_filter_info) for the new filters.
+ convert between column name and filter string (e.g input string
+ for get_filter_info) for the new filters.
+ 5) Add the filter to the ``filt_list`` in the ``test_filters()`` function in ``tests/test_models.py`` to
+ ensure the filter can be loaded properly.
Additional documentation on this is coming soon. In the meantime, let us know on the Github `issue tracker
`_ if you'd like to
diff --git a/docs/atmo_models.rst b/docs/atmo_models.rst
index 12a1b155..4fc8cb2d 100644
--- a/docs/atmo_models.rst
+++ b/docs/atmo_models.rst
@@ -24,20 +24,17 @@ spectrum of each star in the isochrone model.
PopStar uses the pysynphot framework to extract the model atmosphere,
and the the output spectrum is a `pysynphot.Icat object `_.
-The atmosphere model grids currently supported are:
-
-* get_merged_atmosphere (default for stars)
-* get_wd_atmospheres (default for white dwarfs)
-* get_castelli_atmosphere
-* get_phoenixv16_atmosphere
-* get_BTSettl_2015_atmosphere
-* get_BTSettl_atmosphere
-* get_wdKoester_atmosphere
-* get_kurucz_atmosphere
-* get_phoenix_atmosphere
-* get_nextgen_atmosphere
-* get_amesdusty_atmosphere
-
+Below is a table of atmosphere model grids currently supported by
+SPISEA. Note that the resolution column reports the original
+resolution of the atmosphere model grid. These are available in the
+`spisea_cdbs_highres.tar.gz` file on the installation page. However, the default grid SPISEA
+uses has degraded the resolution of all atmosphere grids to R = 250
+(the `spisea_cdbs.tar.gz` file).
+
+.. figure:: images/atm_models.png
+ :width: 900
+ :height: 196
+ :align: center
Model Atmosphere Classes
-------------------------
@@ -45,6 +42,8 @@ Model Atmosphere Classes
.. autofunction:: atmospheres.get_wd_atmosphere
+.. autofunction:: atmospheres.get_bb_atmosphere
+
.. autofunction:: atmospheres.get_castelli_atmosphere
.. autofunction:: atmospheres.get_phoenixv16_atmosphere
diff --git a/docs/conf.py b/docs/conf.py
index 4dd45e9d..f10e26c1 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -14,7 +14,7 @@
#
import os
import sys
-import pdb
+
sys.path.insert(0, os.path.abspath('../spisea/'))
sys.path.insert(0, os.path.abspath('../'))
@@ -25,9 +25,10 @@
author = 'Matthew Hosek Jr, Jessica R. Lu, Casey Y. Lam'
# The short X.Y version
-version = '2.0'
+version = '2.4'
# The full version, including alpha/beta/rc tags
-release = '2.0.0'
+release = '2.4'
+
# -- General configuration ---------------------------------------------------
diff --git a/docs/contributors.rst b/docs/contributors.rst
index e84ecef8..1944147e 100644
--- a/docs/contributors.rst
+++ b/docs/contributors.rst
@@ -9,7 +9,8 @@ Matthew Hosek Jr -- Lead Developer; collaboratively developed all aspects of cod
Casey Lam -- implemented first IFMR module (IFMR_Raithel18)
-Abhimat Gautam -- implemented non-solar metallicity evolution model support for MIST models
+Abhimat Gautam -- implemented non-solar metallicity evolution model
+support for MIST models as well as blackbody atmosphere function
Kelly Lockhart -- helped develop UnresolvedCluster class
@@ -17,13 +18,33 @@ Dongwon Kim -- code testing/debugging
Siyao Jia -- helped with early code and documentation development
-Natasha Adams -- developed resolved multiplicity capabilities
+Natasha Abrams -- developed resolved multiplicity capabilities
(ResolvedMultiplicityDK class)
Michael Medford -- developed resolved multiplicity capabilities
(ResolvedMultiplicityDK class)
-Sam Rose -- developed metallicity-dependent IFMR (IFMR_Spera15)
+Sam Rose -- developed metallicity-dependent IFMRs (IFMR_Spera15 and IFMR_N20_Sukhbold)
Rebecca Lewis -- added HAWK-I filter support, code testing/debugging
+Manuel Parra-Royón -- installation using Docker containers and Singularity
+
+Javier Moldón -- installation using Docker containers and Singularity
+
+Winston Zhang -- bugfix to make redlaw paths correct regardless of
+what operating system is used
+
+Sage Hironaka Remulla -- added Rubin Observatory filters
+
+Lingfeng Wei -- bugfix to improve creation of iso_dir in
+IsochronePhot, implemented faster cluster generation and test
+functions for primary and companion star mass generation (v2.3),
+updated random state generators (v2.4)
+
+Macy Huston -- New metallicity bound + isochrone filter checks,
+imf_mass_lim bugfix, roman filter bugfix, added Euclid filters, Synthpop compatibility
+updates (v2.2), improvements for reading in/updated existing isochrone
+files, Vega mag to ST mag conversion function (v2.4)
+
+Anna Pusack -- Added IRTF L-band filter support
diff --git a/docs/evo_models.rst b/docs/evo_models.rst
index b064f3f0..98d3541c 100644
--- a/docs/evo_models.rst
+++ b/docs/evo_models.rst
@@ -12,23 +12,47 @@ Stellar evolution models are defined as classes in
The evolution object is an input for the :ref:`isochrone_objects`.
-The stellar evolution model classes currently supported by SPISEA
-are:
-
-* MISTv1 (default)
-* MergedBaraffePisaEkstromParsec
-* MergedPisaEkstromParsec
-* Baraffe15
-* Ekstrom12
-* Parsec
-* Pisa
-
-Currently, only the MISTv1 grid offers non-solar metallicity models
-(-4 < [Fe/H] <0.5). All other grids have solar metallicity only.
+Below is a table of the evolution model grids currently supported by SPISEA.
+
+.. figure:: images/evo_models_f2.png
+ :width: 900
+ :height: 210
+ :align: center
+
+Please note the stellar mass range, age range, and metallicity values of the evolution
+model grid you choose:
+
+* Stars generated from the :ref:`imf_objects` (controlled by the
+ `massLimits` variable) that fall outside of the mass range of the
+ evolution model will be dropped from the final cluster table, unless
+ the IFMR is defined (which will catch all the high mass stars and
+ get their compact object values)
+
+* SPISEA will throw an error if you request an age outside of the
+ evolution model age range
+
+* SPISEA will use the evolution model with the closest available
+ metallicity to the requested value
+
If you require other evolution models or need to
expand the existing grids, please see
:ref:`add_evo_models`.
+ModelMismatch Error
+-----------------------------------------
+Since SPISEA v2.1.4, we began tracking the version of the evolution
+model grid (e.g., the evolution models stored in
+``/evolution``).
+Each evolution model class has a required model grid
+version assigned to it. If your evolution model grid
+does not match or exceed the minimum version required by
+your desired evolution model, a ``ModelMismatch``
+exception will be raised.
+
+To resolve the ``ModelMismatch`` error, please
+re-download the latest version of the evolution
+model grid in the installation instructions (:ref:`models`).
+
Base Evolution Model Class
------------------------------------
@@ -39,14 +63,10 @@ Specific Evolution Model Classes
--------------------------------------
.. autoclass:: evolution.MISTv1
:show-inheritance:
-
-
+
.. autoclass:: evolution.MergedBaraffePisaEkstromParsec
:show-inheritance:
-
-.. autoclass:: evolution.MergedPisaEkstromParsec
- :show-inheritance:
-
+
.. autoclass:: evolution.Baraffe15
:show-inheritance:
diff --git a/docs/extinction.rst b/docs/extinction.rst
old mode 100644
new mode 100755
index c8c84039..6b93be6b
--- a/docs/extinction.rst
+++ b/docs/extinction.rst
@@ -15,28 +15,29 @@ oject.
The reddening law is reported in terms of A_lambda / A_Ks, and thus is normalized to A_Ks = 1.
The red_law object is passed into the :ref:`isochrone_objects` in order to
-define the extinction for the stars. See the `Quick Start `_
+define the extinction for the stars. See the `Quick Start `_
for an example.
Available extinction laws:
* RedLawPowerLaw
* RedLawBrokenPowerLaw
+* RedLawRiekeLebofsky
* RedLawCardelli
-* RedLawDamineli16
-* RedLawDeMarchi16
+* RedLawIndebetouw05
+* RedLawRomanZuniga07
* RedLawFitzpatrick09
+* RedLawNishiyama09 (default)
+* RedLawSchoedel10
* RedLawFritz11
-* RedLawHosek18 (deprecated)
+* RedLawDamineli16
+* RedLawDeMarchi16
+* RedLawSchlafly16
* RedLawHosek18b
-* RedLawNishiyama09 (default)
* RedLawNoguerasLara18
-* RedLawRomanZuniga07
-* RedLawRiekeLebofsky
-* RedLawSchlafly16
+* RedLawNoguerasLara20
-
Extinction Law Classes
--------------------------
@@ -45,41 +46,48 @@ Extinction Law Classes
.. autoclass:: reddening.RedLawBrokenPowerLaw
:members: broken_powerlaw
+
+.. autoclass:: reddening.RedLawRiekeLebofsky
+ :members: RiekeLebofsky85
.. autoclass:: reddening.RedLawCardelli
:members: Cardelli89
-.. autoclass:: reddening.RedLawDamineli16
- :members: Damineli16
+.. autoclass:: reddening.RedLawIndebetouw05
+ :members: Indebetouw05
+
+.. autoclass:: reddening.RedLawRomanZuniga07
+ :members: RomanZuniga07
-.. autoclass:: reddening.RedLawDeMarchi16
- :members: DeMarchi16
-
.. autoclass:: reddening.RedLawFitzpatrick09
- :members: Fitzpatrick09
+ :members: Fitzpatrick09
+
+.. autoclass:: reddening.RedLawNishiyama09
+ :members: Nishiyama09
+
+.. autoclass:: reddening.RedLawSchoedel10
+ :members: Schoedel10
.. autoclass:: reddening.RedLawFritz11
:members: Fritz11
+
+.. autoclass:: reddening.RedLawDamineli16
+ :members: Damineli16
-.. _Hosek_old:
-.. autoclass:: reddening.RedLawHosek18
- :members: Hosek18
+.. autoclass:: reddening.RedLawDeMarchi16
+ :members: DeMarchi16
+
+.. autoclass:: reddening.RedLawSchlafly16
+ :members: Schlafly16
.. _Hosek_new:
.. autoclass:: reddening.RedLawHosek18b
:members: Hosek18b
-.. autoclass:: reddening.RedLawNishiyama09
- :members: Nishiyama09
-
.. autoclass:: reddening.RedLawNoguerasLara18
:members: NoguerasLara18
-.. autoclass:: reddening.RedLawRomanZuniga07
- :members: RomanZuniga07
+.. autoclass:: reddening.RedLawNoguerasLara20
+ :members: NoguerasLara20
-.. autoclass:: reddening.RedLawRiekeLebofsky
- :members: RiekeLebofsky85
-.. autoclass:: reddening.RedLawSchlafly16
- :members: Schlafly16
diff --git a/docs/filters.rst b/docs/filters.rst
index 85e70e4f..3112d4a1 100644
--- a/docs/filters.rst
+++ b/docs/filters.rst
@@ -7,16 +7,16 @@ Photometric Filters
The user can specify what filters are used for synthetic photometry
when defining the :ref:`isochrone_objects`. Each filter is
identified by a unique string, and an array of such strings
-are passed into the Isochrone call.
+are passed into the Isochrone call.
For example::
-
+
# Use the HST WFC3-IR F127M and F153M filters, along with NIRC2 Kp
filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f153m', 'nirc2,Kp']
my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,
evo_model=evo_model, atm_func=atm_func,
red_law=red_law, filters=filt_list)
-
+
These strings follow the format ``,``.
Note that there is no space after the comma, and case matters.
@@ -31,28 +31,34 @@ Available filters:
* 2MASS
* CTIO_OSIRIS
* DeCam
+* Euclid
* GAIA
* HAWK-I
* Hubble Space Telescope
+* IRTF
* Johnson-Cousins
* Johnson-Glass
* JWST
* Keck NIRC
* Keck NIRC2
-* NACO
+* NACO
* PanStarrs 1
+* Roman Space Telescope
* UKIRT
+* Vera C. Rubin Observatory
* VISTA
* ZTF
-
+
Filter Sets
------------
-
+
**2MASS**
-`Two-Micron Sky Survey `_ Filters: J, H, Ks
+`Two-Micron Sky Survey `_
+
+Filters: J, H, Ks
Example: ``'2mass,H'``
@@ -61,7 +67,9 @@ Example: ``'2mass,H'``
`OSIRIS imager
`_
-on the CTIO telescope. Filters: H, K
+on the CTIO telescope.
+
+Filters: H, K
Example: ``'ctio_osiris,H'``
@@ -69,10 +77,20 @@ Example: ``'ctio_osiris,H'``
**DeCam**
`Dark Energy Camera `_
+
Filters: u, g, r, i, z, Y
Example: ``'decam,r'``
+**Euclid**
+
+Euclid space telescope `NISP filters `_
+and `VIS single filter `_
+
+Filters: VIS, Y, J, H
+
+Example: ``'euclid,Y'``
+
**GAIA**
The `GAIA Space Telescope filters `_.
@@ -100,7 +118,9 @@ Example: ``'hawki,J'``
**Hubble Space Telescope**
-HST filters are defined by their `pysynphot OBSMODE strings `_.
+HST filters are defined by their `pysynphot OBSMODE strings
+`_. These
+are defined in the ``cdbs/mtab/`` and ``cdbs/comp/`` directories.
Example: ``'wfc3,ir,f125w'``
@@ -108,14 +128,17 @@ Example: ``'wfc3,ir,f125w'``
**Johnson-Cousins**
Johnson-Cousin filters (downloaded from
-http://www.aip.de/en/research/facilities/stella/instruments/data/johnson-ubvri-filter-curves). Filters:
-U, B, V, R, I
+http://www.aip.de/en/research/facilities/stella/instruments/data/johnson-ubvri-filter-curves).
+
+Filters: U, B, V, R, I
Example: ``'ubv,B'``
**Johnson-Glass**
-Johnson-Glass passbands taken from `Bessell et al. 1988 `_
+Johnson-Glass passbands taken from `Bessell et al. 1988
+`_
+
Filters: J, H, K
Example: ``'jg,K'``
@@ -124,14 +147,16 @@ Example: ``'jg,K'``
JWST NIRCam filters, downloaded from `NIRCam website `_. The filter functions in the nircam_throughputs/modAB_mean/nrc_plus_ote folder is used.
-Filters: F070W, F090W, F115W, F140M, F150W, F150W2, F162M, F164N, F182M, F187N, F200W, F210M, F212N, F250M, F277W, F300M, F322W2, F323N, F335M, F356W, F360M, F405N, F410M, F430M, F444W, F460M, F466N, F470N, F480M,
+Filters: F070W, F090W, F115W, F140M, F150W, F150W2, F162M, F164N, F182M, F187N, F200W, F210M, F212N, F250M, F277W, F300M, F322W2, F323N, F335M, F356W, F360M, F405N, F410M, F430M, F444W, F460M, F466N, F470N, F480M
Example: ``'jwst,F356W'``
-
+
**Keck NIRC**
-`NIRC1 filters `_ on the Keck Telescope
+`NIRC1 filters `_ on the Keck
+Telescope
+
Filters: H, K
Example: ``'nirc1,H'``
@@ -141,6 +166,7 @@ Example: ``'nirc1,H'``
`NIRC2 filters `_
on the Keck Telescope ()
+
Filters: J, H, Hcont, K, Kp, Ks, Kcont, Lp, Ms, Brgamma, FeII
Example: ``'nirc2,Ks'``
@@ -149,7 +175,9 @@ Example: ``'nirc2,Ks'``
**NACO**
`ESO NACO filters `_
-Filters: J, H, Ks
+
+Filters: J, H, Ks, IB_2.00, IB_2.03, IB_2.06, IB_2.24, IB_2.27,
+IB_2.30, IB_2.33, IB_2.36
Example: ``'naco,H'``
@@ -157,22 +185,48 @@ Example: ``'naco,H'``
**PanStarrs1**
PanStarrs 1 filters from `Tonry et al. 2012 `_
+
Filters: g, r, i, z, y
Example: ``'ps1, g'``
+**Roman Space Telescope**
+
+Roman Space Telescope WFI filters are defined by their `pysynphot
+OBSMODE strings
+`_.
+These are defined in the ``cdbs/mtab/`` and ``cdbs/comp/``
+directories. Note that the 2021-07-16 version of these directories
+must be downloaded from from the `STScI reference atlases
+`_ for these to
+work. See :ref:`getting_started` for how to download these files.
+
+Filters: F062, F087, F106, F129, F158, W146, F184, F213
+
+Example: ``'roman,wfi,f062'``
+
**UKIRT**
-UKIRT Telescope (http://www.ukidss.org/technical/photom/photom.html)
+`UKIRT Telescope filters `_
+
Filters: J, H, K
Example: ``'ukirt,K'``
+**Vera C. Rubin Observatory**
+
+`Rubin Telescope filters `_
+
+Filters: u, g, r, i, z, y
+
+Example: ``'rubin,g'``
+
**VISTA**
`VISTA Telescope `_
+
Filters: Z, Y, J, H, K
Example: ``'vista,Y'``
@@ -180,6 +234,15 @@ Example: ``'vista,Y'``
**ZTF**
`ZTF Telescope `_
+
Filters: g, r, i
Example: ``'ztf,g'``
+
+**IRTF**
+
+`IRTF NSFCam `_
+
+Filters: L
+
+Example: ``'nsfcam,L'``
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index 304e9fed..dd2eeb96 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -9,7 +9,10 @@ instructions below. If you had already downloaded version 1 of the
code and are switching to version 2, please see :ref:`version`.
SPISEA is hosted on `GitHub `_.
-To begin, clone the git repository.
+To begin, clone the git repository in your desired code directory::
+
+ git clone https://github.com/astropy/SPISEA.git
+
The ``main`` branch contains the current release,
while the ``dev`` branch is for code development.
@@ -17,19 +20,18 @@ while the ``dev`` branch is for code development.
Dependencies
----------------
-* python (>=3.7; preferably via AstroConda, as it includes some necessary
- packages, like astropy and pysynphot)
+The basic SPISEA installation requires ~20 GB of memory, which is
+primarily for the suite of stellar evolution and atmosphere models.
+Other dependencies:
+
+* python (>=3.7, < 3.12)
* astropy
* pysynphot
* scipy
-* numpy (>= v1.17)
+* numpy (>= 1.17, < 2.0)
* matplotlib
-* STScI CDBS data package (Download from the `website
- `_
- or directly via ftp `here
- `_. You will need 2
- files: synphot1.tar.gz (63 MB unzipped) and synphot5.tar.gz (791 MB
- unzipped). See :ref:`set-up-cdbs` below for further instructions.)
+* `pytest-astropy-header `_
+* STScI CDBS data package (See :ref:`set-up-cdbs` below for instructions)
.. _Build:
@@ -49,18 +51,40 @@ directory.
Set Up CDBS Directory
---------------------------------
SPISEA uses the STScI CDBS infrastructure to store
-model atmospheres and HST filter functions. It requires
-a top-level ``cdbs`` directory with 3 sub-directories: ``comp``, ``grid``,
-and ``mtab``. The ``comp`` and ``mtab`` directories are downloaded as
-part of the ``synphot1.tar.gz`` file in the STSci CDBS download (grp/hst/cdbs/comp,
-grp/hst/cdbs/mtab). The ``grid`` directory will eventually hold all of
-the model atmospheres, but start by putting the Phoenix model
-directory from ``synphot5.tar.gz`` there (grp/hst/cdbs/grid/phoenix).
-You will add additional atmosphere model directories to ``cdbs/grid`` in
+model atmospheres and HST filter throughputs.
+In particular, it expects a top-level ``cdbs`` directory
+with 3 sub-directories: ``comp``, ``mtab``,
+and ``grid``. If you already have these set-up then you may
+proceed to the next section. Otherwise, follow the steps below.
+
+#. Create a cdbs directory on your machine::
+
+ mkdir /cdbs
+
+#. The CDBS files can be downloaded from this STScI `website
+ `_.
+ Download the ``synphot1_throughput-master.tar`` and
+ ``synphot5_pheonix-models.tar`` files and place them in your
+ cdbs directory.
+
+#. Untar each of the files. Once completed, a new directory named ``grp`` will appear.
+
+#. The ``comp``, ``mtab``, and ``grid`` sub-directories SPISEA needs
+ are under ``grp/redcat/trds/``. Move these directories directly to
+ ``/cdbs``.
+
+#. You may remove the (now empty) ``grp`` directory and
+ the tar files if desired.
+
+You should end up with a cdbs directory
+``/cdbs`` with sub-directories
+called ``comp``, ``mtab``, and ``grid``.
+You will add additional atmosphere models to ``cdbs/grid`` in
the :ref:`models` section below.
-So, you should end up with a single ``cdbs`` directory with
-the ``cdbs/comp``, ``cdbs/grid``, and ``cdbs/mtab`` sub-directories.
+Note: To use the Roman filters, the CDBS data version should be from
+2024-03-22 or later.
+
.. _models:
@@ -74,20 +98,23 @@ STScI CDBS conventions and should be placed in the ``cdbs/grid`` directory.
You will need to download 2 files:
* `spisea_models.tar.gz
- `_ (3.4 GB; 18 GB unzipped)
+ `_ (3.4 GB; 18 GB unzipped)
-* `spisea_cdbs.tar.gz `_ (142 MB; 248 MB unzipped)
+* `spisea_cdbs.tar.gz `_ (142 MB; 248 MB unzipped)
-You can also optionally download a third file, which contains higher-resolution versions of the atmospheres in ``spisea_cdbs.tar.gz``:
+You may **optionally** download a third file, which contains
+higher-resolution stellar atmospheres. Note that this file is quite
+large, and is not necessary for most SPISEA use cases:
-* `spisea_cdbs_highres.tar.gz `_ (50 GB; 74 GB unzipped)
+* `spisea_cdbs_highres.tar.gz `_ (50 GB; 74 GB unzipped)
SPISEA uses the low-resolution atmospheres (R = 250) in
``spisea_cdbs.tar.gz`` by default, as
-it is then much faster to calculate synthetic photometry and
+it is then much faster for synthetic photometry and
is sufficient in most applications. However, the user can change
this default; see :ref:`atmo_models` for
-more details.
+more details. **Unless you change this default,**
+``spisea_cdbs_highres.tar.gz`` **is not required.**
Once downloaded, ``spisea_cdbs.tar.gz`` (and
``spisea_cdbs_highres.tar.gz``, if desired) should be
@@ -97,18 +124,18 @@ The ``spisea_models.tar.gz`` file can be expanded
anywhere, though we recommend expanding it in the same location as
your ``cdbs`` directory.
-For example, if your cdbs directory is ``/g/lu/models/cdbs/``
-then you should put the .gz files in ``/g/lu/models/``
+For example, if your cdbs directory is ``//models/cdbs/``
+then you should put the .gz files in ``//models/``
and then unzip them from there::
- cd /g/lu/models/
- tar xvf spisea_cdbs.tar.gz
- tar xvf spisea_models.tar.gz
+ cd //models/
+ tar -xvf spisea_cdbs.tar.gz
+ tar -xvf spisea_models.tar.gz
``spisea_cdbs.tar.gz`` will put the model atmospheres in
``cdbs/grid``, and ``spisea_models.tar.gz`` will put the evolution
-models in a directory called ``evolution/``
+models in a new directory called ``evolution/``
.. _setup-paths:
@@ -154,9 +181,12 @@ To test the full range of
evolution models, atmosphere models, and photometric filters,
run the test functions by going into the ``SPISEA/spisea`` directory and running::
- py.test
+ pytest
-This will trigger the test functions we have implemented. If all is
+Note that this uses the python
+`pytest
+`_
+package. This will trigger the test functions we have implemented. If all is
well, you shouldn't get any errors. Warnings are (generally) fine.
TroubleShooting
@@ -173,4 +203,69 @@ either or both of the following warnings::
UserWarning: PYSYN_CDBS is undefined; functionality will be SEVERELY crippled.
UserWarning: SPISEA_MODELS is undefined; functionality will be SEVERELY crippled.
+
+==========================
+Build and deploy from Docker
+==========================
+
+Build your own SPISEA image for Docker Containers. This installation form contains SPISEA deployed in a container and includes the data sets, models and all the necessary paths and code.
+
+Requirements
+-----------------------
+
+- Linux, Windows or MacOS with Docker installed.
+- At least 2 CPUs and 4 GB of RAM and 16 GB of storage.
+
+Installation
+-----------------------
+
+To create the container image, clone this repository and build the container::
+
+ git clone https://github.com/astropy/SPISEA.git
+ cd SPISEA
+ docker build -t spisea .
+
+Usage
+-----------------------
+To open a shell ready play with SPISEA::
+
+ docker run -ti spisea bash
+
+To execute a script you have in your current folder::
+
+ docker run -ti -v $PWD:$PWD -w $PWD spisea python myscript.py
+
+==========================
+Deploy from DockerHub
+==========================
+
+If you don't want to build the image from scratch you can use a pre-build container image from `DockerHub `_ using the following commands::
+
+ docker pull amigahub/spisea:v1
+
+Then, to open a shell ready to play with SPISEA::
+
+ docker run -ti amigahub/spisea:v1 bash
+
+To execute a script you have in your current folder::
+
+ docker run -ti -v $PWD:$PWD -w $PWD spisea python myscript.py
+
+==========================
+Deploy from Singularity containers
+==========================
+
+Download the image from DockerHub and convert it into a ``.sif`` image for Singularity.::
+
+ singularity pull spisea.sif docker://amigahub/spisea:v1
+
+After downloading the image, you can use it in singularity by opening a shell on SPISEA image::
+
+ singularity shell spisea.sif
+
+
+
+
+
+
diff --git a/docs/ifmr.rst b/docs/ifmr.rst
index ee3f4fbb..a1072d84 100644
--- a/docs/ifmr.rst
+++ b/docs/ifmr.rst
@@ -21,7 +21,7 @@ Compact objects are included in the output tables produced by
* BH: phase = 103
See `Quick Start Example
-`_
+`_
for more examples of how to interact with the :ref:`cluster_objects`
output.
@@ -31,5 +31,8 @@ IFMR Base Classes
:members: generate_death_mass
.. autoclass:: ifmr.IFMR_Raithel18
- :members: generate_death_mass
+ :members: NS_mass, generate_death_mass
+
+.. autoclass:: ifmr.IFMR_N20_Sukhbold
+ :members: NS_mass, generate_death_mass
diff --git a/docs/images/atm_models.png b/docs/images/atm_models.png
new file mode 100644
index 00000000..b8bd00dd
Binary files /dev/null and b/docs/images/atm_models.png differ
diff --git a/docs/images/cluster_table.png b/docs/images/cluster_table.png
new file mode 100644
index 00000000..991e6dc8
Binary files /dev/null and b/docs/images/cluster_table.png differ
diff --git a/docs/images/evo_models_f2.png b/docs/images/evo_models_f2.png
new file mode 100644
index 00000000..bb64d6b3
Binary files /dev/null and b/docs/images/evo_models_f2.png differ
diff --git a/docs/images/isochrone_table.png b/docs/images/isochrone_table.png
new file mode 100644
index 00000000..f93759fd
Binary files /dev/null and b/docs/images/isochrone_table.png differ
diff --git a/docs/imf.rst b/docs/imf.rst
index a532b9f9..a99b8fe1 100644
--- a/docs/imf.rst
+++ b/docs/imf.rst
@@ -9,15 +9,16 @@ Initial Mass Functions (IMFs) are defined as classes in
from spisea.imf import imf
imf_obj = imf.
+Currently, the IMF is implemented as a power-law:
+dn / dm ~ m^-alpha. The Broken Power-Law IMF object
+gives users the flexibility to define the mass range
+and exponents of the IMF.
+
The IMF object is an input for the :ref:`cluster_objects`, and will
be used to draw the inital stellar mass distribution for the cluster.
A :ref:`multi_obj` may be passed to the IMF object to
form multiple star systems.
-The IMFs are defined as broken power laws. Additional functional forms
-may be implemented as sub-classes off of the base IMF class.
-
-
Base IMF Class
--------------
.. autoclass:: imf.imf.IMF
diff --git a/docs/index.rst b/docs/index.rst
index 0b43f278..048f5c8b 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,8 +1,13 @@
-.. sampledoc documentation master file, created by
+.. sampledoc documentation main file, created by
sphinx-quickstart on Tue Aug 11 05:04:40 2009.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
+.. figure:: ../SPISEA_logo_final.png
+ :width: 380
+ :height: 336
+ :align: center
+
==========================================================================
SPISEA: Stellar Population Interface for Stellar Evolution and Atmospheres
==========================================================================
@@ -24,8 +29,8 @@ Here is a brief list of things that SPISEA can do:
* make a spectrum of a star cluster in integrated light
-Please cite `Hosek et al. (2020) `_ if you use SPISEA in
-your research.
+SPISEA can be downloaded from `Github `_. Please cite `Hosek et al. (2020) `_ if you use SPISEA in
+your research.
Getting Started
----------------
@@ -63,23 +68,174 @@ Advanced Documentation
Contributions
---------------
-We encourage contributions to SPISEA, particular
-those that add support for star formation histories,
-new models, higher spectral resolution, etc.
+Have an idea? We'd love to have you join our team!
+We welcome and encourage contributions to SPISEA.
+
For feature additions, we ask that users make their
own fork of the repository, make their changes, and then submit a pull
request to the "dev" branch.
-All contributions will be acknowledged on the :ref:`contributors` page. Contributors with features used in code
+All contributions will be acknowledged on the :ref:`contributors` page
+(with permission). Contributors with features used in code
releases will be co-authors in future SPISEA software papers.
Change Log
----------
+2.4 (2026-03-20)
+ * Added backward compatibility for isochrone file names created
+ before v2.3
+ * When reading isochrone from existing file, only keep user
+ requested filters in resulting table.
+ * Changed the global random seed to a random generator within each class,
+ but still retaining the reproducibility. Test cluster files in
+ test_data also updated accordingly for testing purposes
+ * Added filter support for IRTF L-band
+ * Added conversion function between ST mags and Vega mags
+
+2.3 (2026-02-10)
+ * Achieves faster cluster generation (factor of about 2x) by using
+ replacing ragged arrays with masked arrays when calculating
+ multiplicity properties
+ * Added new test functions (and associated test data files) ensuring
+ that the primary mass and companion
+ mass distibutions remain the same as generated with SPISEA <= v2.2
+ * Added support to Euclid VIS filter
+
+
+2.2 (2026-01-16)
+ * Compatibility updates for SPISEA to work with `SynthPop
+ `_. Updates include:
+ * Low mass objects below the isochrone grid can optionally be kept
+ in clusters (off by default) and will have
+ ``current_mass=initial_mass`` and ``phase=98``, with no other
+ evolutionary information or photometry.
+ * Evolution model versions are now logged in IsochronePhot files
+ and checked if present.
+ * The option ``synthpop_extension`` is now available for MISTv1
+ version=1.2 evolution. This fills in the missing parameter space
+ down to initial mass 0.1Msun in isochrones where needed. Use of
+ this option will require downloading updated isochrone files.
+ * Added support for Euclid filters.
+
+2.1.15 (2025-10-25)
+ * Updated Roman filter name from outdated w146 to current f146. From
+ now on a STScI CDBS data version from 2024-03-22 or later is
+ required to use the Roman filter photometry.
+
+2.1.14 (2025-10-23)
+ * Added support for NACO intermediate bands
+ * Added metallicity bounds check when calling atmosphere model grid
+ * Added check to make sure desired filter columns are present in
+ isochrone file when deciding whether a new isochrone needs to be
+ calculated
+ * Small bugfix: ensures that imf_mass_limit is reset properly when
+ generating multiple clusters from same imf object
+
+2.1.13 (2024-12-10)
+ * Small bugfix to make iso_dir creation more robust
+
+2.1.12 (2024-12-02)
+ * Added support for Rubin Observatory filters
+
+2.1.11 (2023-09-15)
+ * Small bugfix to RedLawFritz11 so paths work regardless of
+ operating system used
+
+2.1.10 (2023-06-01)
+ * Added support for Roman Space Telescope filters (via pysynphot)
+
+ * Note: this requires the pysynphot ``cdbs/mtab`` and
+ ``cdbs/comp/`` directories to be at least the 2021-07-16 version or later. See :ref:`getting_started` for how to download these files
+
+2.1.9 (2023-01-10)
+ * Small bugfix: updating pytest setup.cfg and conftest.py files to
+ work with latest python/astropy versions
+ * Added additional print statements in verbose condition flags
+
+
+2.1.8 (2023-01-09)
+ * Small bugfix: Updating numpy array definitions to use
+ dtype='objects' instead of depreciated dtype='np.object'
+
+2.1.7 (2022-10-26)
+ * Made several updates to the extinction law objects, including:
+
+ * Added Indebetouw05, Schoedel10, Fritz11, and NoguerasLara20 laws
+ * Improved docs and added plotting functions for Nishiyama09 and RomanZuniga07 laws
+ * Updated K-band scale wavelength for Fitzpatrick07 and NoguerasLara18 to match published values
+ * Updated assorted "under the hood" functions to handle new laws
+ * Added more test functions for the extinction laws
+ * Updated :ref:`ext_law` page so laws are in chronological order
+
+2.1.6 (2022-09-15)
+ * Bugfix in how evolution object identifies what age and metallicity
+ model to pull from the grid of available models. Previously, it used
+ first grid model where grid model value >
+ requested value (numpy.searchsorted routine). Now, is takes grid model
+ where the difference between the grid model value and requested
+ value is minimized. This is most relevant for metallicity
+ selection for MIST isochrones, where the model grid metallicities are
+ spaced out.
+
+2.1.5 (2022-07-20)
+ * Added additional parameters to get_bb_atmosphere function to give
+ user control over the min and max wavelength values, as well as
+ wavelength sampling. See :ref:`atmo_models` for details.
+
+2.1.4 (2022-05-24)
+
+ * Expanded MISTv1 evolution model grid age range to logAge = 5.01 --
+ 10.30
+ * Begun version tracking for evolution model grid; updated grid
+ (with new MISTv1 age range) is grid 1.1
+ * A new ModelMismatch
+ exception will be raised if evolution model object requires a
+ higher version of the model grid than the user has installed. See
+ :ref:`evo_models` for details
+ * Fixed MISTv1 sub-directory names so proper Z_solar value
+ of 0.0142 is used (only naming convention, does not effect output
+ results)
+ * Updated evolution model and atmosphere model doc pages with tables
+ describing properties of installed options
+ * Small tweaks to installation instructions for clarity
+ * Updated test functions and removed outdated dependence on nose package
+
+2.1.3 (2022-04-23)
+
+ * Updates to documentation and Quick-Start Guide
+ * Fully depreciated Hosek18 extinction law to avoid confusion with Hosek18b
+
+2.1.2 (2022-04-01)
+
+ * New installation methods using Docker containers and Singularity
+
+
+2.1.1 (2022-03-30)
+
+ * Bugfix: fixed application of IFMR to objects in companions table
+ (ResolvedCluster and associated subclasses: applies when
+ multiplicity + IFMR are used)
+
+ * Minor additions to docs
+
+2.1.0 (2022-01-24)
+
+ * Added ability to create resolved multiple systems, with orbital
+ elements assigned to each companion (see :ref:`multi_obj`)
+
+ * Added new metallicity-dependent IFMR models (see
+ :ref:`ifmr_objects`)
+
+ * Added blackbody atmosphere class
+
+ * Additional test functions, filter functions, and documentation
+ updates
2.0.0 (2020-07-09)
- * Top-level software name change from PyPopStar to SPISEA (see :ref:`version` for instructions to do this)
+ * Top-level software name change from PyPopStar to SPISEA (see
+ :ref:`version` for instructions to do this). This version of code was released in `Hosek et al. (2020) `_.
1.0.1 (2020-06-24)
@@ -87,5 +243,7 @@ Change Log
1.0.0 (2019-12-01)
- * First Release
+ * Initial release
+
+Logo by Natasha Abrams
diff --git a/docs/make_cluster.rst b/docs/make_cluster.rst
index c58eb2a8..6af85cd7 100644
--- a/docs/make_cluster.rst
+++ b/docs/make_cluster.rst
@@ -39,7 +39,7 @@ has already been created::
massLimits = np.array([0.08, 0.5, 1, 120]) # mass segments
powers = np.array([-1.3, -2.3, -2.3]) # power-law exponents
my_imf = imf.IMF_broken_powerlaw(massLimits, powers,
- imf_multi)
+ multiplicity=imf_multi)
# Define the cluster mass
mass = 10**5 # Units: solar masses
@@ -51,9 +51,28 @@ has already been created::
See `Quick Start Example
-`_
+`_
for a detailed example for how to make different
cluster sub-classes and interact with the resulting output.
+Here is a table from Hosek et al. 2020 that describes
+the values in the cluster output table:
+
+
+.. figure:: images/cluster_table.png
+ :width: 405
+ :height: 550
+ :align: center
+
+Tips and Tricks: The Cluster Object
+-----------------------------------------
+* The cluster is generated by drawing a population of initial stellar masses according
+ to the IMF/multiplicity objects and then assigning stellar
+ properties to each using the evolution model. Thus, it is
+ possible for the cluster to generate a star with an initial mass beyond
+ the boundaries of the evolution model. If this occurs, then
+ nans are assigned to the properties of that star (e.g. Teff,
+ current_mass, photometry, etc).
+
Base Cluster Class
----------------------------
diff --git a/docs/make_isochrone.rst b/docs/make_isochrone.rst
index 092271b0..5f764c93 100644
--- a/docs/make_isochrone.rst
+++ b/docs/make_isochrone.rst
@@ -9,7 +9,10 @@ total extinction, and metallicity, along with the :ref:`atmo_models`,
:ref:`evo_models`, and :ref:`ext_law`.
If the IsochronePhot sub-class is used then synthetic photometry
-will be produced. The :ref:`filters` are defined as additional inputs.
+will be produced. The :ref:`filters` are defined as additional
+inputs. The output photometry is in terms of vega mags, but the user
+can also calculate the required conversion to AB mags or ST mags using
+the functions in :ref:`Photometry Conversion Functions `.
An example of making an IsochronePhot object::
@@ -45,9 +48,19 @@ An example of making an IsochronePhot object::
iso_dir=iso_dir)
See `Quick Start Example
-`_
-for a detailed example showing how to interact with the isochrone object output.
+`_
+for a detailed example showing how to interact with the isochrone
+object output. Here is a table from Hosek et al. 2020 that describes
+the values in the isochrone output table:
+.. figure:: images/isochrone_table.png
+ :width: 468
+ :height: 400
+ :align: center
+
+
+
+
Tips and Tricks: The IsochronePhot Object
-----------------------------------------
@@ -66,10 +79,9 @@ Tips and Tricks: The IsochronePhot Object
* **WARNING**: When IsochronePhot checks to see if the desired
isochrone table already exists, it checks all isochrone properties
- except for the photometric filters (evolution models, atmosphere
- models, and reddening law are encoded in the table meta-data).
+ (evolution models, atmosphere models, and reddening law are encoded in the table meta-data).
If any of these parameters do not match, then the isochrone will
- be re-calculated.
+ be re-calculated.
However, to keep the isochrone filenames reasonable, only the
age, extinction, distance, and metallicity are encoded in the
@@ -81,13 +93,6 @@ Tips and Tricks: The IsochronePhot Object
that users specify different iso_dir paths when making isochrones
with different evolution models, atmosphere models, or reddening
laws.*
-
- * **WARNING**: IsochronePhot does not check existing
- isochrone tables to see if the photometric filters match
- those specified by the user. *So, if the user wishes to generate an
- isochrone with different filters, we recommend either using a
- different iso_dir path or setting the keyword recomp=True (see
- docs below).*
Base Isochrone Class
----------------------------
@@ -102,3 +107,13 @@ Isochrone Sub-classes
.. autoclass:: synthetic.IsochronePhot
:show-inheritance:
:members: make_photometry, plot_CMD, plot_mass_magnitude
+
+
+Photometry Conversion Functions
+-----------------------------
+.. _phot_conversions:
+
+.. autofunction:: synthetic.calc_ab_vega_filter_conversion
+
+.. autofunction:: synthetic.calc_st_vega_filter_conversion
+
diff --git a/docs/more_examples.rst b/docs/more_examples.rst
index f88408fc..51fc5d87 100644
--- a/docs/more_examples.rst
+++ b/docs/more_examples.rst
@@ -6,7 +6,7 @@ Further Examples
Additional Jupyter notebooks with tutorials to produce the plots shown
in the SPISEA paper (`Hosek et al. (2020)
`_) can be found `here
-`_.
+`_.
Figure 2: HR-Diagrams with Different Evolution Models; CMDs with different Extinction Laws
--------------------------------------------------------------------------------------------------------
diff --git a/docs/quick_start.rst b/docs/quick_start.rst
old mode 100644
new mode 100755
index 2c8084ee..51854eef
--- a/docs/quick_start.rst
+++ b/docs/quick_start.rst
@@ -5,7 +5,7 @@ Quick Start Guide
===================
We provide a `jupyter notebook
-`_
+`_
as a Quick-Start Guide to demonstrate the steps required to simulate a cluster and interact
with the resulting output. This guide shows you how to:
@@ -38,7 +38,7 @@ Examine Compact Remnants Produced by Different Clusters
----------------------------------------------------------------
You will generate two massive clusters, one at 100 Myr and the other at 10
Gyr, and compare the distribution of compact remnants produced given
-the IFMR IFMR_Raithel18. You will also test how
+the default IFMR. You will also test how
changing the IMF of these clusters changes these distributions.
.. figure:: images/compact_remnants_example.png
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 0ed56588..285b1e61 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -4,3 +4,4 @@ numpydoc
astropy
pysynphot
scipy
+docutils<0.18
diff --git a/docs/resolved_multiples_example.ipynb b/docs/resolved_multiples_example.ipynb
index afa8723c..856a1a48 100755
--- a/docs/resolved_multiples_example.ipynb
+++ b/docs/resolved_multiples_example.ipynb
@@ -225,7 +225,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.6.9"
}
},
"nbformat": 4,
diff --git a/filt_func/euclid/H.dat b/filt_func/euclid/H.dat
new file mode 100644
index 00000000..502cbacd
--- /dev/null
+++ b/filt_func/euclid/H.dat
@@ -0,0 +1,743 @@
+# WAVE T_TOTAL T_FILTER T_TELESCOPE T_NI-OA T_QE
+1405 7.39323e-05 9.33327e-05 0.892197 0.939733 0.94479
+1406 7.87152e-05 9.94054e-05 0.892028 0.939565 0.944808
+1407 8.80195e-05 0.000111139 0.892299 0.939398 0.944827
+1408 7.98104e-05 0.000100787 0.892321 0.939232 0.944846
+1409 8.04887e-05 0.000101581 0.893008 0.939068 0.944866
+1410 8.96503e-05 0.000113211 0.892609 0.938906 0.944887
+1411 8.68784e-05 0.000109826 0.891803 0.938745 0.944909
+1412 8.0046e-05 0.000101211 0.891739 0.938586 0.944931
+1413 9.3824e-05 0.000118704 0.891327 0.938428 0.944954
+1414 8.66659e-05 0.000109466 0.892933 0.938272 0.944977
+1415 8.03055e-05 0.000101524 0.892251 0.938117 0.945001
+1416 7.79284e-05 9.84914e-05 0.892622 0.937964 0.945026
+1417 7.65934e-05 9.68994e-05 0.891863 0.937813 0.945052
+1418 7.4442e-05 9.40761e-05 0.892945 0.937662 0.945078
+1419 6.86357e-05 8.67011e-05 0.893444 0.937514 0.945105
+1420 7.16165e-05 9.05779e-05 0.89246 0.937367 0.945132
+1421 6.53319e-05 8.26349e-05 0.892512 0.937221 0.945161
+1422 5.90971e-05 7.47355e-05 0.892781 0.937077 0.94519
+1423 5.90591e-05 7.46765e-05 0.893019 0.936935 0.94522
+1424 5.24493e-05 6.63188e-05 0.893125 0.936794 0.94525
+1425 6.19594e-05 7.83478e-05 0.893181 0.936655 0.945281
+1426 6.99315e-05 8.84655e-05 0.89291 0.936517 0.945313
+1427 6.8377e-05 8.65558e-05 0.892423 0.93638 0.945346
+1428 6.15276e-05 7.78272e-05 0.893187 0.936245 0.94538
+1429 6.08152e-05 7.69474e-05 0.893035 0.936112 0.945414
+1430 6.09746e-05 7.71689e-05 0.892898 0.93598 0.945449
+1431 5.59075e-05 7.08047e-05 0.892374 0.93585 0.945485
+1432 6.0059e-05 7.60971e-05 0.892055 0.935721 0.945522
+1433 7.32291e-05 9.27549e-05 0.892423 0.935593 0.94556
+1434 6.01578e-05 7.61836e-05 0.892679 0.935467 0.945598
+1435 5.73082e-05 7.25592e-05 0.892954 0.935343 0.945637
+1436 6.50065e-05 8.23424e-05 0.892641 0.93522 0.945677
+1437 6.72835e-05 8.52591e-05 0.892379 0.935098 0.945718
+1438 6.99598e-05 8.86337e-05 0.892621 0.934978 0.94576
+1439 7.30334e-05 9.24484e-05 0.89346 0.93486 0.945802
+1440 6.94575e-05 8.79594e-05 0.893151 0.934742 0.945846
+1441 7.03754e-05 8.92115e-05 0.892321 0.934627 0.94589
+1442 7.71861e-05 9.77905e-05 0.892886 0.934513 0.945935
+1443 8.04869e-05 0.000101983 0.892858 0.9344 0.945981
+1444 7.7133e-05 9.77372e-05 0.892884 0.934289 0.946028
+1445 8.13834e-05 0.000103196 0.892312 0.934179 0.946076
+1446 8.7556e-05 0.000111011 0.892467 0.93407 0.946125
+1447 8.60918e-05 0.00010921 0.892069 0.933963 0.946175
+1448 8.68341e-05 0.000110185 0.891852 0.933858 0.946225
+1449 8.1573e-05 0.000103476 0.892187 0.933754 0.946277
+1450 9.2378e-05 0.000117184 0.892223 0.933651 0.946329
+1451 9.60159e-05 0.000121793 0.892295 0.93355 0.9464
+1452 9.41343e-05 0.000119525 0.891436 0.93345 0.946472
+1453 9.52216e-05 0.000120798 0.892256 0.933352 0.946544
+1454 9.52347e-05 0.000120876 0.891826 0.933255 0.946618
+1455 9.31461e-05 0.000118325 0.891094 0.93316 0.946692
+1456 8.73947e-05 0.000110982 0.891409 0.933066 0.946767
+1457 9.52506e-05 0.000120903 0.891833 0.932973 0.946843
+1458 9.13414e-05 0.000115801 0.892927 0.932882 0.946919
+1459 8.86748e-05 0.000112485 0.892427 0.932792 0.946996
+1460 9.51156e-05 0.000120609 0.89278 0.932704 0.947074
+1461 8.93641e-05 0.000113322 0.892741 0.932617 0.947153
+1462 9.07162e-05 0.000115101 0.89225 0.932531 0.947231
+1463 8.18078e-05 0.00010376 0.892582 0.932447 0.947311
+1464 8.73969e-05 0.000110887 0.892279 0.932364 0.947391
+1465 9.07752e-05 0.000115137 0.892562 0.932283 0.947471
+1466 9.03505e-05 0.000114552 0.892924 0.932203 0.947552
+1467 9.09592e-05 0.000115389 0.892418 0.932124 0.947633
+1468 8.78956e-05 0.000111533 0.892171 0.932047 0.947715
+1469 9.30295e-05 0.00011799 0.892603 0.931971 0.947796
+1470 9.15267e-05 0.000116173 0.891912 0.931897 0.947879
+1471 9.45012e-05 0.000119975 0.891707 0.931824 0.947961
+1472 0.000100035 0.00012706 0.891276 0.931752 0.948044
+1473 0.000108739 0.000137976 0.892174 0.931682 0.948126
+1474 0.000114919 0.000145742 0.892621 0.931613 0.948209
+1475 0.000113776 0.000144273 0.892727 0.931545 0.948292
+1476 0.000116091 0.000147176 0.892913 0.931479 0.948375
+1477 0.000124944 0.000158514 0.892254 0.931414 0.948458
+1478 0.000137812 0.000174802 0.892427 0.931351 0.948541
+1479 0.000141931 0.00018 0.892537 0.931289 0.948625
+1480 0.00014338 0.000182059 0.89143 0.931228 0.948708
+1481 0.000159024 0.000201838 0.89179 0.931168 0.94879
+1482 0.00017842 0.000226572 0.891307 0.93111 0.948873
+1483 0.000189318 0.000240317 0.891636 0.931054 0.948956
+1484 0.000202388 0.000256923 0.891558 0.930998 0.949038
+1485 0.000225402 0.000286176 0.891414 0.930944 0.94912
+1486 0.000242987 0.000308383 0.891732 0.930891 0.949202
+1487 0.000271876 0.000345153 0.891432 0.93084 0.949284
+1488 0.000305781 0.000388105 0.891612 0.93079 0.949365
+1489 0.00034163 0.000433857 0.891068 0.930741 0.949446
+1490 0.000376218 0.000477573 0.891427 0.930694 0.949526
+1491 0.00041923 0.000532175 0.891392 0.930648 0.949606
+1492 0.000476692 0.000605811 0.89034 0.930603 0.949686
+1493 0.000543016 0.000689258 0.891396 0.930559 0.949765
+1494 0.000632657 0.000803217 0.891167 0.930517 0.949843
+1495 0.000722235 0.000917894 0.890211 0.930477 0.949921
+1496 0.00083504 0.0010609 0.890478 0.930437 0.949998
+1497 0.000980891 0.00124718 0.889743 0.930399 0.950075
+1498 0.00116038 0.00147419 0.890434 0.930362 0.950151
+1499 0.00137198 0.00174235 0.890737 0.930326 0.950226
+1500 0.00162886 0.00206763 0.89111 0.930292 0.9503
+1501 0.00195542 0.00248643 0.889566 0.930259 0.950347
+1502 0.00235931 0.00300147 0.889117 0.930227 0.950393
+1503 0.00288759 0.00366945 0.890093 0.930197 0.950438
+1504 0.00356297 0.00453175 0.889284 0.930168 0.950483
+1505 0.00443364 0.0056334 0.890179 0.93014 0.950527
+1506 0.00557431 0.00709066 0.88917 0.930113 0.95057
+1507 0.00706914 0.00899108 0.889256 0.930088 0.950613
+1508 0.00902959 0.011482 0.889436 0.930064 0.950655
+1509 0.0116515 0.0148251 0.888871 0.930041 0.950696
+1510 0.0152647 0.0194254 0.888721 0.930019 0.950737
+1511 0.0202314 0.0257511 0.888524 0.929999 0.950778
+1512 0.0269514 0.034324 0.888 0.92998 0.950818
+1513 0.0360307 0.0458339 0.889007 0.929962 0.950857
+1514 0.0488028 0.0621582 0.887883 0.929946 0.950896
+1515 0.0664098 0.0845864 0.887831 0.929931 0.950935
+1516 0.0903584 0.115072 0.887945 0.929917 0.950974
+1517 0.121673 0.15486 0.888442 0.929904 0.951012
+1518 0.16406 0.208677 0.88898 0.929893 0.95105
+1519 0.216612 0.275985 0.887459 0.929882 0.951087
+1520 0.280621 0.357428 0.887708 0.929873 0.951124
+1521 0.352135 0.448417 0.887877 0.929866 0.951161
+1522 0.430497 0.548909 0.88671 0.929859 0.951198
+1523 0.511821 0.652197 0.887231 0.929854 0.951235
+1524 0.587219 0.748312 0.887155 0.92985 0.951272
+1525 0.651441 0.830256 0.887014 0.929847 0.951308
+1526 0.700785 0.893187 0.886939 0.929845 0.951345
+1527 0.733658 0.935999 0.88604 0.929845 0.951381
+1528 0.754616 0.961496 0.887148 0.929846 0.951418
+1529 0.765391 0.975609 0.886763 0.929848 0.951454
+1530 0.771366 0.982634 0.887259 0.929851 0.951491
+1531 0.773498 0.985928 0.886702 0.929855 0.951527
+1532 0.773704 0.987624 0.885375 0.929861 0.951564
+1533 0.775868 0.988698 0.886845 0.929868 0.951601
+1534 0.776319 0.989699 0.886421 0.929876 0.951638
+1535 0.7774 0.990618 0.886789 0.929885 0.951675
+1536 0.777375 0.991327 0.886081 0.929896 0.951713
+1537 0.777207 0.991688 0.885521 0.929907 0.951751
+1538 0.777688 0.991759 0.885958 0.92992 0.951789
+1539 0.777066 0.991695 0.885257 0.929934 0.951827
+1540 0.777448 0.9912 0.886084 0.929949 0.951866
+1541 0.775572 0.990517 0.884503 0.929966 0.951905
+1542 0.775593 0.989885 0.88504 0.929983 0.951944
+1543 0.775613 0.989295 0.885535 0.930002 0.951984
+1544 0.774861 0.989115 0.88478 0.930022 0.952025
+1545 0.775547 0.98917 0.885456 0.930043 0.952066
+1546 0.775386 0.989527 0.884894 0.930065 0.952107
+1547 0.774579 0.990218 0.883295 0.930088 0.952149
+1548 0.77736 0.9912 0.885525 0.930113 0.952192
+1549 0.776683 0.992261 0.883743 0.930139 0.952235
+1550 0.7784 0.993451 0.884569 0.930166 0.952279
+1551 0.779675 0.994531 0.884951 0.930194 0.952363
+1552 0.780004 0.995527 0.884331 0.930223 0.952449
+1553 0.78105 0.996174 0.884834 0.930253 0.952534
+1554 0.781216 0.996529 0.884598 0.930284 0.95262
+1555 0.78195 0.996577 0.885274 0.930317 0.952707
+1556 0.78109 0.996049 0.884656 0.930351 0.952794
+1557 0.780006 0.995305 0.883975 0.930386 0.952881
+1558 0.779429 0.994119 0.88426 0.930422 0.952968
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+
diff --git a/filt_func/euclid/J.dat b/filt_func/euclid/J.dat
new file mode 100644
index 00000000..63dc1afb
--- /dev/null
+++ b/filt_func/euclid/J.dat
@@ -0,0 +1,693 @@
+# WAVE T_TOTAL T_FILTER T_TELESCOPE T_NI-OA T_QE
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diff --git a/filt_func/euclid/VIS.dat b/filt_func/euclid/VIS.dat
new file mode 100755
index 00000000..8071c831
--- /dev/null
+++ b/filt_func/euclid/VIS.dat
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diff --git a/filt_func/euclid/Y.dat b/filt_func/euclid/Y.dat
new file mode 100644
index 00000000..72bee09c
--- /dev/null
+++ b/filt_func/euclid/Y.dat
@@ -0,0 +1,343 @@
+# WAVE T_TOTAL T_FILTER T_TELESCOPE T_NI-OA T_QE
+905 7.5113e-08 0.000105286 0.000916272 0.87738 0.887426
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+914 1.18578e-07 8.99259e-06 0.0164541 0.896066 0.894348
+915 1.74339e-07 8.82801e-06 0.0245702 0.897977 0.89507
+916 2.4088e-07 8.49972e-06 0.0351578 0.899853 0.895783
+917 3.34094e-07 8.12229e-06 0.0508847 0.901693 0.896487
+918 4.35089e-07 7.29911e-06 0.0735359 0.903499 0.897183
+919 6.60982e-07 8.01873e-06 0.101413 0.905269 0.897869
+920 1.06528e-06 9.24164e-06 0.141437 0.907005 0.898548
+921 1.5872e-06 1.01605e-05 0.191174 0.908707 0.899218
+922 2.30254e-06 1.11427e-05 0.252239 0.910375 0.89988
+923 3.31826e-06 1.26196e-05 0.320159 0.912009 0.900534
+924 4.94132e-06 1.50349e-05 0.399181 0.91361 0.90118
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diff --git a/filt_func/naco/IB_2.00.dat b/filt_func/naco/IB_2.00.dat
new file mode 100644
index 00000000..d7246991
--- /dev/null
+++ b/filt_func/naco/IB_2.00.dat
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diff --git a/filt_func/naco/IB_2.03.dat b/filt_func/naco/IB_2.03.dat
new file mode 100644
index 00000000..43d11534
--- /dev/null
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diff --git a/filt_func/naco/IB_2.06.dat b/filt_func/naco/IB_2.06.dat
new file mode 100644
index 00000000..0aa9dfc5
--- /dev/null
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new file mode 100644
index 00000000..08a22ff6
--- /dev/null
+++ b/filt_func/naco/IB_2.24.dat
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new file mode 100644
index 00000000..2503f847
--- /dev/null
+++ b/filt_func/naco/IB_2.27.dat
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new file mode 100644
index 00000000..1b343703
--- /dev/null
+++ b/filt_func/naco/IB_2.30.dat
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new file mode 100644
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--- /dev/null
+++ b/filt_func/naco/IB_2.33.dat
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new file mode 100644
index 00000000..11d2bd5c
--- /dev/null
+++ b/filt_func/naco/IB_2.36.dat
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new file mode 100755
index 00000000..43589640
--- /dev/null
+++ b/filt_func/nsfcam/L.dat
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diff --git a/filt_func/rubin/README.txt b/filt_func/rubin/README.txt
new file mode 100755
index 00000000..d185c3c7
--- /dev/null
+++ b/filt_func/rubin/README.txt
@@ -0,0 +1,5 @@
+These are the Vera C. Rubin Observatory or LSST filters, downloaded 10/25/2024 from: https://github.com/lsst/throughputs/tree/main/baseline
+
+Specifically the total_*.dat file was used
+
+The wavelengths are in nm, the transmission is in fractions
\ No newline at end of file
diff --git a/filt_func/rubin/g.dat b/filt_func/rubin/g.dat
new file mode 100755
index 00000000..55dfa2c5
--- /dev/null
+++ b/filt_func/rubin/g.dat
@@ -0,0 +1,8508 @@
+# LSST Throughputs files created from syseng_throughputs repo
+# Version 1.9
+# sha1 fcc05772f99427e4a45cd1b9da1628dded9a06d5
+# Aerosols added to atmosphere
+# Wavelen_cutoff_BLUE 385.60
+# Wavelen_cutoff_RED 566.30
+# Wavelength(nm) Throughput(0-1)
+300.0 0.0
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diff --git a/filt_func/rubin/i.dat b/filt_func/rubin/i.dat
new file mode 100755
index 00000000..3479c5bf
--- /dev/null
+++ b/filt_func/rubin/i.dat
@@ -0,0 +1,8508 @@
+# LSST Throughputs files created from syseng_throughputs repo
+# Version 1.9
+# sha1 fcc05772f99427e4a45cd1b9da1628dded9a06d5
+# Aerosols added to atmosphere
+# Wavelen_cutoff_BLUE 669.90
+# Wavelen_cutoff_RED 837.80
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diff --git a/filt_func/rubin/r.dat b/filt_func/rubin/r.dat
new file mode 100755
index 00000000..4bb64fa6
--- /dev/null
+++ b/filt_func/rubin/r.dat
@@ -0,0 +1,8508 @@
+# LSST Throughputs files created from syseng_throughputs repo
+# Version 1.9
+# sha1 fcc05772f99427e4a45cd1b9da1628dded9a06d5
+# Aerosols added to atmosphere
+# Wavelen_cutoff_BLUE 533.70
+# Wavelen_cutoff_RED 705.70
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diff --git a/filt_func/rubin/u.dat b/filt_func/rubin/u.dat
new file mode 100755
index 00000000..2ff028a4
--- /dev/null
+++ b/filt_func/rubin/u.dat
@@ -0,0 +1,8508 @@
+# LSST Throughputs files created from syseng_throughputs repo
+# Version 1.9
+# sha1 fcc05772f99427e4a45cd1b9da1628dded9a06d5
+# Aerosols added to atmosphere
+# Wavelen_cutoff_BLUE 304.30
+# Wavelen_cutoff_RED 403.50
+# Wavelength(nm) Throughput(0-1)
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diff --git a/filt_func/rubin/y.dat b/filt_func/rubin/y.dat
new file mode 100755
index 00000000..522c8cec
--- /dev/null
+++ b/filt_func/rubin/y.dat
@@ -0,0 +1,8508 @@
+# LSST Throughputs files created from syseng_throughputs repo
+# Version 1.9
+# sha1 fcc05772f99427e4a45cd1b9da1628dded9a06d5
+# Aerosols added to atmosphere
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+# Wavelen_cutoff_RED 1100.00
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diff --git a/filt_func/rubin/z.dat b/filt_func/rubin/z.dat
new file mode 100755
index 00000000..40e525d3
--- /dev/null
+++ b/filt_func/rubin/z.dat
@@ -0,0 +1,8508 @@
+# LSST Throughputs files created from syseng_throughputs repo
+# Version 1.9
+# sha1 fcc05772f99427e4a45cd1b9da1628dded9a06d5
+# Aerosols added to atmosphere
+# Wavelen_cutoff_BLUE 799.30
+# Wavelen_cutoff_RED 939.20
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diff --git a/setup.cfg b/setup.cfg
index 337d4d33..4d69fa9c 100755
--- a/setup.cfg
+++ b/setup.cfg
@@ -41,7 +41,7 @@ testpaths = "spisea" "docs"
astropy_header = true
doctest_plus = enabled
text_file_format = rst
-addopts = --doctest-rst
+#addopts = --doctest-rst
[coverage:run]
omit =
diff --git a/spisea/_astropy_init.py b/spisea/_astropy_init.py
index 2dffe8fd..58189c4b 100755
--- a/spisea/_astropy_init.py
+++ b/spisea/_astropy_init.py
@@ -14,14 +14,13 @@
except ImportError:
__version__ = ''
-
if not _ASTROPY_SETUP_: # noqa
import os
from warnings import warn
- from astropy.config.configuration import (
- update_default_config,
- ConfigurationDefaultMissingError,
- ConfigurationDefaultMissingWarning)
+ #from astropy.config.configuration import (
+ # update_default_config,
+ # ConfigurationDefaultMissingError,
+ # ConfigurationDefaultMissingWarning)
# Create the test function for self test
from astropy.tests.runner import TestRunner
@@ -30,23 +29,23 @@
__all__ += ['test']
# add these here so we only need to cleanup the namespace at the end
- config_dir = None
-
- if not os.environ.get('ASTROPY_SKIP_CONFIG_UPDATE', False):
- config_dir = os.path.dirname(__file__)
- config_template = os.path.join(config_dir, __package__ + ".cfg")
- if os.path.isfile(config_template):
- try:
- update_default_config(
- __package__, config_dir, version=__version__)
- except TypeError as orig_error:
- try:
- update_default_config(__package__, config_dir)
- except ConfigurationDefaultMissingError as e:
- wmsg = (e.args[0] +
- " Cannot install default profile. If you are "
- "importing from source, this is expected.")
- warn(ConfigurationDefaultMissingWarning(wmsg))
- del e
- except Exception:
- raise orig_error
+ #config_dir = None
+
+ #if not os.environ.get('ASTROPY_SKIP_CONFIG_UPDATE', False):
+ # config_dir = os.path.dirname(__file__)
+ # config_template = os.path.join(config_dir, __package__ + ".cfg")
+ # if os.path.isfile(config_template):
+ # try:
+ # update_default_config(
+ # __package__, config_dir, version=__version__)
+ # except TypeError as orig_error:
+ # try:
+ # update_default_config(__package__, config_dir)
+ # except ConfigurationDefaultMissingError as e:
+ # wmsg = (e.args[0] +
+ # " Cannot install default profile. If you are "
+ # "importing from source, this is expected.")
+ # warn(ConfigurationDefaultMissingWarning(wmsg))
+ # del e
+ # except Exception:
+ # raise orig_error
diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py
index b00b217a..9867db81 100755
--- a/spisea/atmospheres.py
+++ b/spisea/atmospheres.py
@@ -8,16 +8,17 @@
import pysynphot
import time
import pdb
+import warnings
log = logging.getLogger('atmospheres')
-def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4):
+def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False):
"""
Given atmosphere model, get temperature and gravity bounds
"""
# Open catalog fits file and break out row indices
catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir))
-
+
teff_arr = []
z_arr = []
logg_arr = []
@@ -34,11 +35,12 @@ def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4
# Filter by metallicity. Will chose the closest metallicity to desired input
metal_list = np.unique(np.array(z_arr))
metal_idx = np.argmin(np.abs(metal_list - metallicity))
-
+ metallicity_new = metal_list[metal_idx]
+
z_filt = np.where(z_arr == metal_list[metal_idx])
teff_arr = teff_arr[z_filt]
logg_arr = logg_arr[z_filt]
-
+
# # Now find the closest atmosphere in parameter space to
# # the one we want. We'll find the match with the lowest
# # fractional difference
@@ -50,46 +52,51 @@ def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4
#
# temperature_new = teff_arr[idx_f]
# gravity_new = logg_arr[idx_f]
-
+
# First check if temperature within bounds
temperature_new = temperature
if temperature > np.max(teff_arr):
temperature_new = np.max(teff_arr)
if temperature < np.min(teff_arr):
temperature_new = np.min(teff_arr)
-
+
# If temperature within bounds, then check if metallicity within bounds
teff_diff = np.abs(teff_arr - temperature)
sorted_min_diffs = np.unique(teff_diff)
-
+
## Find two closest temperatures
teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]]
teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]]
-
+
logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)]
logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)]
-
+
## Switch to most conservative bound of logg out of two closest temps
gravity_new = gravity
if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]):
gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)])
if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]):
gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)])
-
- # Print out changes, if any
- if temperature_new != temperature:
- teff_msg = 'Changing to T={0:6.0f} for T={1:6.0f} logg={2:4.2f}'
- print( teff_msg.format(temperature_new, temperature, gravity))
-
- if gravity_new != gravity:
- logg_msg = 'Changing to logg={0:4.2f} for T={1:6.0f} logg={2:4.2f}'
- print( logg_msg.format(gravity_new, temperature, gravity))
-
- return (temperature_new, gravity_new)
+
+ if verbose:
+ # Print out changes, if any
+ if temperature_new != temperature:
+ teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}'
+ print( teff_msg.format(temperature_new, metallicity, temperature, gravity))
+
+ if gravity_new != gravity:
+ logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}'
+ print( logg_msg.format(gravity_new, metallicity, temperature, gravity))
+
+ if metallicity_new != metallicity:
+ logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}'
+ print( logg_msg.format(metallicity_new, metallicity, temperature, gravity))
+
+ return (temperature_new, gravity_new, metallicity_new)
def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False):
"""
- Return atmosphere from the Kurucz pysnphot grid
+ Return atmosphere from the Kurucz pysnphot grid
(`Kurucz 1993 `_).
Grid Range:
@@ -108,7 +115,7 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal
gravity: float
The stellar gravity, in cgs units
-
+
rebin: boolean
Always false for this particular function
"""
@@ -116,11 +123,11 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal
sp = pysynphot.Icat('k93models', temperature, metallicity, gravity)
except:
# Check atmosphere catalog bounds
- (temperature, gravity) = get_atmosphere_bounds('k93models',
+ (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models',
metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
sp = pysynphot.Icat('k93models', temperature, metallicity, gravity)
# Do some error checking
@@ -135,10 +142,10 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal
def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False):
"""
- Return atmospheres from the pysynphot ATLAS9 atlas
+ Return atmospheres from the pysynphot ATLAS9 atlas
(`Castelli & Kurucz 2004 `_).
- Grid Range:
+ Grid Range:
* Teff: 3500 - 50000 K
* gravity: 0 - 5.0 cgs
@@ -154,7 +161,7 @@ def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=F
gravity: float
The stellar gravity, in cgs units
-
+
rebin: boolean
If true, rebins the atmospheres so that they are the same
resolution as the Castelli+04 atmospheres. Default is False,
@@ -167,13 +174,13 @@ def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=F
sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity)
except:
# Check atmosphere catalog bounds
- (temperature, gravity) = get_atmosphere_bounds('ck04models',
+ (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models',
metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity)
-
+
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
@@ -194,11 +201,11 @@ def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=Fal
sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity)
except:
# Check atmosphere catalog bounds
- (temperature, gravity) = get_atmosphere_bounds('nextgen',
+ (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen',
metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity)
# Do some error checking
@@ -232,7 +239,7 @@ def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=F
def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4,
rebin=False):
"""
- Return atmosphere from the pysynphot
+ Return atmosphere from the pysynphot
`PHOENIX atlas `_.
Parameters
@@ -245,7 +252,7 @@ def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4,
gravity: float
The stellar gravity, in cgs units
-
+
rebin: boolean
If true, rebins the atmospheres so that they are the same
resolution as the Castelli+04 atmospheres. Default is False,
@@ -256,11 +263,11 @@ def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4,
sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity)
except:
# Check atmosphere catalog bounds
- (temperature, gravity) = get_atmosphere_bounds('phoenix',
+ (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix',
metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity)
# Do some error checking
@@ -273,7 +280,7 @@ def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4,
return sp
-def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True):
+def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False):
"""
metallicity = [M/H] (def = 0)
temperature = Kelvin (def = 24000)
@@ -284,14 +291,15 @@ def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebi
# Take care of atmospheres outside the catalog boundaries
logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}'
if gravity > 4.3:
- print( logg_msg.format(4.3, temperature, gravity))
+ if verbose:
+ print( logg_msg.format(4.3, temperature, gravity))
gravity = 4.3
-
+
if rebin:
sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity)
else:
sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity)
-
+
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
@@ -305,7 +313,7 @@ def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebi
def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True,
verbose=False):
"""
- For a given stellar atmosphere, get extract the closest possible match in
+ For a given stellar atmosphere, get extract the closest possible match in
Teff/logg space. Note that this is different from the normal routine
which interpolates along the input grid to get final spectrum. We can't
do this here because the Fierro+15 atmosphere grid is so sparse
@@ -340,7 +348,7 @@ def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4
# fractional difference
teff_diff = (teff_arr - temperature) / temperature
logg_diff = (logg_arr - gravity) / gravity
-
+
diff_tot = abs(teff_diff) + abs(logg_diff)
idx_f = np.where(diff_tot == min(diff_tot))[0][0]
@@ -348,7 +356,7 @@ def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4
# pysynphot object
infile = cat[idx_f]['FILENAME'].split('.')
spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0]))
-
+
# Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the
# ATLAS models are in FLAM at the surface. So, we need to multiply the
# CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface.
@@ -362,13 +370,13 @@ def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4
radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm
radius /= 3.08*10**18 # in pc
-
+
# Make the pysynphot spectrum
w = spec['Wavelength']
f = spec['Flux'] * (1000 / radius)**2.
sp = pysynphot.ArraySpectrum(w,f)
-
+
#sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0]))
# Print out parameters of match, if desired
@@ -390,7 +398,7 @@ def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, r
sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity)
else:
sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity)
-
+
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
@@ -421,9 +429,9 @@ def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14):
def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True):
"""
- Return PHOENIX v16 atmospheres from
- `Husser et al. 2013 `_.
-
+ Return PHOENIX v16 atmospheres from
+ `Husser et al. 2013 `_.
+
Models originally downloaded via `ftp `_.
Solar metallicity and [alpha/Fe] is used.
@@ -443,7 +451,7 @@ def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=
gravity: float
The stellar gravity, in cgs units
-
+
rebin: boolean
If true, rebins the atmospheres so that they are the same
resolution as the Castelli+04 atmospheres. Default is False,
@@ -460,13 +468,13 @@ def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=
sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity)
except:
# Check atmosphere catalog bounds
- (temperature, gravity) = get_atmosphere_bounds(atm_model_name,
+ (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name,
metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity)
-
+
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
@@ -479,14 +487,14 @@ def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=
def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True):
"""
- Return atmosphere from CIFIST2011_2015 grid
- (`Allard et al. 2012 `_,
+ Return atmosphere from CIFIST2011_2015 grid
+ (`Allard et al. 2012 `_,
`Baraffe et al. 2015 `_ )
Grid originally downloaded from `website `_.
Grid Range:
-
+
* Teff: 1200 - 7000 K
* gravity: 2.5 - 5.5 cgs
* [M/H] = 0
@@ -501,11 +509,11 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi
gravity: float
The stellar gravity, in cgs units
-
+
rebin: boolean
If true, rebins the atmospheres so that they are the same
resolution as the Castelli+04 atmospheres. Default is False,
- which is often sufficient synthetic photometry in most cases.
+ which is often sufficient synthetic photometry in most cases.
"""
if rebin == True:
atm_name = 'BTSettl_2015_rebin'
@@ -516,14 +524,14 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi
sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity)
except:
# Check atmosphere catalog bounds
- (temperature, gravity) = get_atmosphere_bounds(atm_name,
+ (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name,
metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity)
-
-
+
+
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
@@ -536,7 +544,7 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi
def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True):
"""
- Return atmosphere from CIFIST2011 grid
+ Return atmosphere from CIFIST2011 grid
(`Allard et al. 2012 `_)
Grid originally downloaded `here `_
@@ -544,16 +552,16 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T
Notes
------
Grid Range:
-
+
* [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5
-
+
Teff and gravity ranges depend on metallicity:
[M/H] = -2.5
* Teff: 2600 - 4600 K
* gravity: 4.5 - 5.5
-
+
[M/H] = -2.0
* Teff: 2600 - 7000
@@ -567,7 +575,7 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T
[M/H] = -1.0
* Teff: 2600 - 7000
- * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5
+ * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5
[M/H] = -0.5
@@ -601,7 +609,7 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T
gravity: float
The stellar gravity, in cgs units
-
+
rebin: boolean
If true, rebins the atmospheres so that they are the same
resolution as the Castelli+04 atmospheres. Default is False,
@@ -616,14 +624,14 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T
sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity)
except:
# Check atmosphere catalog bounds
- (temperature, gravity) = get_atmosphere_bounds(atm_name,
+ (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name,
metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity)
-
-
+
+
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
@@ -636,7 +644,7 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T
def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7):
"""
- Return white dwarf atmospheres from
+ Return white dwarf atmospheres from
`Koester et al. 2010 `_
Parameters
@@ -649,7 +657,7 @@ def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7):
gravity: float
The stellar gravity, in cgs units
-
+
rebin: boolean
If true, rebins the atmospheres so that they are the same
resolution as the Castelli+04 atmospheres. Default is False,
@@ -664,24 +672,24 @@ def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7):
print( ' temperature = %d' % temperature)
print( ' metallicity = %.1f' % metallicity)
print( ' log gravity = %.1f' % gravity)
-
+
return sp
def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4):
"""
Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16.
- Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0
+ Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0
"""
try:
sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity)
except:
# Check atmosphere catalog bounds
- (temperature, gravity) = get_atmosphere_bounds('merged_atlas_phoenix',
+ (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix',
metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity)
# Do some error checking
@@ -699,17 +707,17 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4):
Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model
and phoenixV16.
- Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5
+ Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5
"""
try:
sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity)
except:
# Check atmosphere catalog bounds
- (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_phoenix',
+ (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix',
metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity)
# Do some error checking
@@ -726,7 +734,7 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4):
def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False,
rebin=True):
"""
- Return a stellar atmosphere from a suite of different model grids,
+ Return a stellar atmosphere from a suite of different model grids,
depending on the input temperature, (all values in K).
Parameters
@@ -739,7 +747,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose
gravity: float
The stellar gravity, in cgs units
-
+
rebin: boolean
If true, rebins the atmospheres so that they are the same
resolution as the Castelli+04 atmospheres. Default is False,
@@ -750,7 +758,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose
Notes
-----
- The underlying stellar model grid used changes as a function of
+ The underlying stellar model grid used changes as a function of
stellar temperature (in K):
* T > 20,000: ATLAS
@@ -761,14 +769,14 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose
For T < 3800, there is an additional gravity and metallicity
dependence:
- If T < 3800 and [M/H] = 0:
-
+ If T < 3800 and [M/H] = 0:
+
* T < 3800, logg < 2.5: PHOENIX v16
* 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge
* 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015
Otherwise, if T < 3800 and [M/H] != 0:
-
+
* T < 3800: PHOENIX v16
References:
@@ -777,21 +785,21 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose
* PHOENIXv16 (`Husser et al. 2013 `_)
* BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/)
- LTE WARNING:
+ LTE WARNING:
The ATLAS atmospheres are calculated with LTE, and so they
are less accurate when non-LTE conditions apply (e.g. T > 20,000
K). Ultimately we'd like to add a non-LTE atmosphere grid for
the hottest stars in the future.
- HOW BOUNDARIES BETWEEN MODELS ARE TREATED:
+ HOW BOUNDARIES BETWEEN MODELS ARE TREATED:
- At the boundary between two models grids a temperature range is defined
- where the resulting atmosphere is a weighted average between the two
+ At the boundary between two models grids a temperature range is defined
+ where the resulting atmosphere is a weighted average between the two
grids. Near one boundary one model
- is weighted more heavily, while at the other boundary the other
- model is weighted more heavily. These are calculated in the
- temperature ranges where we switch between model grids, to
+ is weighted more heavily, while at the other boundary the other
+ model is weighted more heavily. These are calculated in the
+ temperature ranges where we switch between model grids, to
ensure a smooth transition.
"""
# For T < 3800, atmosphere depends on metallicity + gravity.
@@ -807,7 +815,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose
temperature=temperature,
gravity=gravity,
rebin=rebin)
-
+
if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5):
if verbose:
print( 'BTSettl/Phoenixv16 merged atmosphere')
@@ -823,7 +831,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose
temperature=temperature,
gravity=gravity,
rebin=rebin)
-
+
if (temperature <= 3800) & (metallicity != 0):
if verbose:
print( 'Phoenixv16 atmosphere')
@@ -847,7 +855,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose
return get_atlas_phoenix_atmosphere(metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
if (temperature >= 5500) & (temperature < 20000):
if verbose:
print( 'ATLAS merged atmosphere')
@@ -867,14 +875,14 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose
# temperature=temperature,
# gravity=gravity)
-
+
def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False):
"""
- Return the white dwarf atmosphere from
- `Koester et al. 2010 `_.
- If desired parameters are
+ Return the white dwarf atmosphere from
+ `Koester et al. 2010 `_.
+ If desired parameters are
outside of grid, return a blackbody spectrum instead
Parameters
@@ -887,7 +895,7 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False
gravity: float
The stellar gravity, in cgs units
-
+
rebin: boolean
If true, rebins the atmospheres so that they are the same
resolution as the Castelli+04 atmospheres. Default is False,
@@ -903,17 +911,60 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False
return get_wdKoester_atmosphere(metallicity=metallicity,
temperature=temperature,
gravity=gravity)
-
+
except pysynphot.exceptions.ParameterOutOfBounds:
- if verbose:
- print('BB atmosphere')
-
- # Use a blackbody.
- bbspec = pysynphot.spectrum.BlackBody(temperature)
- bbspec.convert('flam')
- bbspec *= (1000 * 3.08e18 / 6.957e10)**2
+ # Use a black-body atmosphere.
+ bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose)
return bbspec
-
+
+def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None,
+ verbose=False, rebin=None,
+ wave_min=500, wave_max=50_000, wave_num=20_000):
+ """
+ Return a blackbody spectrum
+
+ Parameters
+ ----------
+ temperature: float, default=20_000
+ The stellar temperature, in units of K
+ wave_min: float, default=500
+ Sets the minimum wavelength (in Angstroms) of the wavelength range
+ for the blackbody spectrum
+ wave_max: float, default=50_000
+ Sets the maximum wavelength (in Angstroms) of the wavelength range
+ for the blackbody spectrum
+ wave_num: int, default=20_000
+ Sets the number of wavelength points in the wavelength range
+ Note: the wavelength range is evenly spaced in log space
+ """
+ if ((metallicity is not None) or (gravity is not None) or
+ (rebin is not None)):
+ warnings.warn(
+ 'Only `temperature` keyword is used for black-body atmosphere'
+ )
+
+ if verbose:
+ print('Black-body atmosphere')
+
+ # Modify pysynphot's default waveset to specified bounds
+ pysynphot.refs.set_default_waveset(
+ minwave=wave_min, maxwave=wave_max, num=wave_num
+ )
+
+ # Get black-body atmosphere for specified temperature from pysynphot
+ bbspec = pysynphot.spectrum.BlackBody(temperature)
+
+ # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam`
+ bbspec.convert('flam')
+
+ # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec.
+ # Need to remove this normalization for SPISEA by multiplying bbspec
+ # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2
+ bbspec *= (1000 * 3.086e18 / 6.957e10)**2
+
+ return bbspec
+
+
#--------------------------------------#
# Atmosphere formatting functions
#--------------------------------------#
@@ -929,7 +980,7 @@ def download_CMFGEN_atmospheres(Table_rot, Table_norot):
Fierro+15 paper
Website addresses are hardcoded
-
+
Puts downloaded models in the current working directory.
"""
print( 'WARNING: THIS DOES NOT COMPLETELY WORK')
@@ -998,7 +1049,7 @@ def organize_CMFGEN_atmospheres(path_to_dir):
"""
# First, record current working directory to return to later
start_dir = os.getcwd()
-
+
# Enter atmosphere directory, collect rotating and non-rotating
# file names (assumed to all start with "t")
os.chdir(path_to_dir)
@@ -1023,10 +1074,10 @@ def organize_CMFGEN_atmospheres(path_to_dir):
# Also move Tables with model parameters into correct directory
os.system('mv Table_rot.txt cmfgenF15_rot')
os.system('mv Table_noRot.txt cmfgenF15_noRot')
-
+
# Return to original directory
os.chdir(start_dir)
-
+
return
def make_CMFGEN_catalog(path_to_dir):
@@ -1048,10 +1099,10 @@ def make_CMFGEN_catalog(path_to_dir):
"""
# Record current working directory for later
start_dir = os.getcwd()
-
+
# Enter atmosphere directory
os.chdir(path_to_dir)
-
+
# Extract parameters for each atmosphere
# Note: can't rely on filename for this because not precise enough!!
@@ -1066,7 +1117,7 @@ def make_CMFGEN_catalog(path_to_dir):
# lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun
# mass = float(lumtmp[0][5:-1]) # In M_sun
-
+
# Need to calculate log g from T and L (cgs)
# lum_sun = 3.846 * 10**33 # erg/s
# M_sun = 2 * 10**33 # g
@@ -1094,18 +1145,18 @@ def make_CMFGEN_catalog(path_to_dir):
#---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---#
#path = path_to_dir + '/' + names[i]
path = names[i] + '.fits[Flux]'
-
+
index_str.append(index)
name_str.append(path)
-
+
catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME'))
# Create catalog.fits file in directory with the models
catalog.write('catalog.fits', format = 'fits')
-
+
# Move back to original directory, create the catalog.fits file
os.chdir(start_dir)
-
+
return
def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir):
@@ -1141,23 +1192,23 @@ def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir):
unique = np.unique(wave, return_index=True)
wave = wave[unique[1]]
flux = flux[unique[1]]
-
- # Make fits table from individual columns.
+
+ # Make fits table from individual columns.
c0 = fits.Column(name='Wavelength', format='D', array=wave)
c1 = fits.Column(name='Flux', format='E', array=flux)
cols = fits.ColDefs([c0, c1])
tbhdu = fits.BinTableHDU.from_columns(cols)
- #Adding unit keywords
+ #Adding unit keywords
tbhdu.header['TUNIT1'] = 'ANGSTROM'
tbhdu.header['TUNIT2'] = 'FLAM'
prihdu = fits.PrimaryHDU()
-
+
finalhdu = fits.HDUList([prihdu, tbhdu])
finalhdu.writeto(i[:-4]+'.fits', overwrite=True)
-
+
print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files)))
# Return to original directory, copy over new .fits files to cdbs directory
@@ -1174,7 +1225,7 @@ def rebin_cmfgen(cdbs_path, rot=True):
cdbs_path: path to cdbs directory
rot=True for rotating models (cmfgen_rot), False for non-rotating models
-
+
makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin
"""
# Get an atlas ck04 model, we will use this to set wavelength grid
@@ -1190,7 +1241,7 @@ def rebin_cmfgen(cdbs_path, rot=True):
tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits'
path = cdbs_path+'/grid/cmfgen_norot_rebin/'
orig_path = cdbs_path+'/grid/cmfgen_norot/'
-
+
cmfgen_hdu = fits.open(tmp)
header0 = cmfgen_hdu[0].header
# Create rebin directories if they don't already exist. Copy over
@@ -1205,7 +1256,7 @@ def rebin_cmfgen(cdbs_path, rot=True):
files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))]
# First column in new files will be for [atlas] wavelength
- c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave)
+ c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave)
# For each catalog.fits entry, read the unbinned spectrum and rebin to
# the atlas resolution. Make a new fits file in rebin directory
@@ -1217,16 +1268,16 @@ def rebin_cmfgen(cdbs_path, rot=True):
temp = float(vals[0])
metal = float(vals[1])
grav = float(vals[2])
-
+
# Fetch the spectrum
- if rot == True:
+ if rot == True:
sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav)
else:
sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav)
# Rebin
flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave)
- c1 = fits.Column(name='Flux', format='E', array=flux_rebin)
+ c1 = fits.Column(name='Flux', format='E', array=flux_rebin)
# Make the FITS file from the columns with header
cols = fits.ColDefs([c0,c1])
@@ -1251,7 +1302,7 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'):
path_to_dir is the path to the directory containing all of the downloaded
files
-
+
met_str is the name of the current metallicity
Creates new fits files for each atmosphere: phoenix_.fits,
@@ -1268,7 +1319,7 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'):
pass
else:
os.mkdir(sub_dir)
-
+
# Extract wavelength array, make column for later
wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits')
wave = wavefile[0].data
@@ -1291,7 +1342,7 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'):
for f in files:
# Extract the logg out of filename
logg = f[9:13]
-
+
# Extract fluxes from file
spectrum = fits.open(f)
flux = spectrum[0].data
@@ -1300,11 +1351,11 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'):
# Make Column object with fluxes, add to table
col = Column(flux, name = 'g{0:2.1f}'.format(float(logg)))
t.add_column(col)
-
+
# Now, construct final fits file for the given temp
outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp)
- t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True)
-
+ t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True)
+
# Progress counter for user
i += 1
print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr)))
@@ -1321,18 +1372,18 @@ def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'):
path_to_directory is the path to the directory with the reformatted
models (i.e. the output from construct_atmospheres, phoenix[met_str])
-
+
Puts catalog.fits file in directory the user starts in
"""
# Save starting directory for later, move into working directory
start_dir = os.getcwd()
os.chdir(path_to_dir)
-
+
# Extract metallicity from metallicity string
met = float(met_str[1]) + (float(met_str[2]) * 0.1)
if 'm' in met_str:
met *= -1.
-
+
# Collect the filenames. Each is a unique temp with many different log g's
files = glob.glob('phoenix*.fits')
files.sort()
@@ -1345,7 +1396,7 @@ def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'):
t = Table.read(i, format='fits')
keys = t.keys()
logg_vals = keys[1:]
-
+
# Extract temp from filename
name = i.split('_')
temp = float(name[1][:-5])
@@ -1358,20 +1409,20 @@ def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'):
filename_arr.append(filename)
catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME'))
-
+
# Return to starting directory, write catalog
os.chdir(start_dir)
-
+
if os.path.exists('catalog.fits'):
from astropy.table import vstack
-
+
prev_catalog = Table.read('catalog.fits', format='fits')
joined_catalog = vstack([prev_catalog, catalog])
-
+
joined_catalog.write('catalog.fits', format='fits', overwrite=True)
else:
catalog.write('catalog.fits', format='fits', overwrite=True)
-
+
return
def cdbs_PHOENIXv16(path_to_cdbs_dir):
@@ -1392,7 +1443,7 @@ def cdbs_PHOENIXv16(path_to_cdbs_dir):
# Collect the filenames, make necessary changes to each one
files = glob.glob('phoenix*.fits')
-
+
## Need to sort filenames; glob doesn't always give them in order
files.sort()
@@ -1400,28 +1451,28 @@ def cdbs_PHOENIXv16(path_to_cdbs_dir):
counter = 0
for i in files:
counter += 1
-
+
# Read in current FITS table
cur_table = Table.read(i, format='fits')
-
+
cur_table.columns[0].name = 'Wavelength'
-
+
num_cols = len(cur_table.colnames)
-
- # Multiplying each flux column by 10^-8 for conversion
+
+ # Multiplying each flux column by 10^-8 for conversion
for cur_col_index in range(1, num_cols, 1):
cur_col_name = cur_table.colnames[cur_col_index]
cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8
-
-
+
+
# Construct new FITS file based on old one
hdu = fits.open(i)
header_0 = hdu[0].header
header_1 = hdu[1].header
sci = hdu[1].data
-
+
tbhdu = fits.table_to_hdu(cur_table)
-
+
# Copying over the older headers, adding unit keywords
prihdu = fits.PrimaryHDU(header=header_0)
tbhdu.header['TUNIT1'] = 'ANGSTROM'
@@ -1438,17 +1489,17 @@ def cdbs_PHOENIXv16(path_to_cdbs_dir):
tbhdu.header['TUNIT12'] = 'FLAM'
tbhdu.header['TUNIT13'] = 'FLAM'
tbhdu.header['TUNIT14'] = 'FLAM'
-
+
# Construct and write out final FITS file
finalhdu = fits.HDUList([prihdu, tbhdu])
finalhdu.writeto(i, overwrite=True)
-
+
hdu.close()
print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files)))
-
+
# Change back to starting directory
os.chdir(start_dir)
-
+
return
def rebin_phoenixV16(cdbs_path):
@@ -1473,7 +1524,7 @@ def rebin_phoenixV16(cdbs_path):
path = cdbs_path+'/grid/phoenix_v16_rebin/'
if not os.path.exists(path):
os.mkdir(path)
-
+
# Read in the existing catalog.fits file and rebin every spectrum.
cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits')
@@ -1488,51 +1539,51 @@ def rebin_phoenixV16(cdbs_path):
temp_arr[ff] = float(vals[0])
metal_arr[ff] = float(vals[1])
logg_arr[ff] = float(vals[2])
-
+
metal_uniq = np.unique(metal_arr)
temp_uniq = np.unique(temp_arr)
-
+
for mm in range(len(metal_uniq)):
metal = metal_uniq[mm] # metallicity
-
+
# Construct str for metallicity (for appropriate directory name)
met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10))
if metal > 0:
met_str = 'p' + met_str
else:
met_str = 'm' + met_str
-
+
# Make directory for current metallicity if it does not exist yet
if not os.path.exists(path + 'phoenix' + met_str):
os.mkdir(path + 'phoenix' + met_str)
-
+
for tt in range(len(temp_uniq)):
temp = temp_uniq[tt] # temperature
- # Pick out the list of gravities for this T, Z combo
+ # Pick out the list of gravities for this T, Z combo
idx = np.where((metal_arr == metal) & (temp_arr == temp))[0]
logg_exist = logg_arr[idx]
-
+
# All gravities will go in one file. Here is the output
# file name.
outfile = path + files_all[idx[0]].split('[')[0]
-
+
## If the rebinned file already exists, continue
if os.path.exists(outfile):
continue
-
+
# Build a columns array. One column for each gravity.
cols_arr = []
# Make the wavelength column, which is first in the cols array.
c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave)
cols_arr.append(c0)
-
+
for gg in range(len(logg_exist)):
grav = logg_exist[gg] # gravity
- # Fetch the spectrum
+ # Fetch the spectrum
sp = pysynphot.Icat('phoenix_v16', temp, metal, grav)
flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave)
@@ -1540,7 +1591,7 @@ def rebin_phoenixV16(cdbs_path):
name = 'g{0:3.1f}'.format(grav)
col = fits.Column(name=name, format='E', array=flux_rebin)
cols_arr.append(col)
-
+
# Make the FITS file from the columns with header.
cols = fits.ColDefs(cols_arr)
@@ -1556,7 +1607,7 @@ def rebin_phoenixV16(cdbs_path):
finalhdu.writeto(outfile)
print( 'Finished file ' + outfile + ' with gravities: ', logg_exist)
-
+
return
@@ -1571,7 +1622,7 @@ def rebin_spec(wave, specin, wavnew):
f = np.ones(len(wave))
filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom')
obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper')
-
+
return obs.binflux
def organize_BTSettl_2015_atmospheres(path_to_dir):
@@ -1602,7 +1653,7 @@ def organize_BTSettl_2015_atmospheres(path_to_dir):
spec = hdu[1].data
header_0 = hdu[0].header
header_1 = hdu[1].header
-
+
wave = spec.field(0)
flux = spec.field(1)
@@ -1623,13 +1674,13 @@ def organize_BTSettl_2015_atmospheres(path_to_dir):
tbhdu.header['TUNIT1'] = 'ANGSTROM'
tbhdu.header['TUNIT2'] = 'FLAM'
hdu_new = fits.HDUList([prihdu, tbhdu])
-
+
# Write new fits table in cdbs directory
hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True)
hdu.close()
hdu_new.close()
-
+
# Return to original directory
os.chdir(start_dir)
return
@@ -1646,10 +1697,10 @@ def make_BTSettl_2015_catalog(path_to_dir):
"""
# Record current working directory for later
start_dir = os.getcwd()
-
+
# Enter atmosphere directory
os.chdir(path_to_dir)
-
+
# Extract parameters for each atmosphere from the filename,
# construct columns for catalog file
files = glob.glob("*spec.fits")
@@ -1668,10 +1719,10 @@ def make_BTSettl_2015_catalog(path_to_dir):
# Create catalog.fits file in directory with the models
catalog.write('catalog.fits', format = 'fits', overwrite=True)
-
+
# Move back to original directory, create the catalog.fits file
os.chdir(start_dir)
-
+
return
def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']):
@@ -1714,8 +1765,8 @@ def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']):
# Make new output
c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave)
- c1 = fits.Column(name='Flux', format='E', array=flux_rebin)
-
+ c1 = fits.Column(name='Flux', format='E', array=flux_rebin)
+
cols = fits.ColDefs([c0, c1])
tbhdu = fits.BinTableHDU.from_columns(cols)
prihdu = fits.PrimaryHDU(header=header0)
@@ -1724,7 +1775,7 @@ def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']):
outfile = path + files_all[ff].split('[')[0]
finalhdu = fits.HDUList([prihdu, tbhdu])
- finalhdu.writeto(outfile, overwrite=True)
+ finalhdu.writeto(outfile, overwrite=True)
return
@@ -1743,7 +1794,7 @@ def make_wavelength_unique(files, dirname):
if len(t) != len(test[0]):
t = t[test[1]]
-
+
c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength'])
c1 = fits.Column(name='Flux', format='E', array=t['Flux'])
cols = fits.ColDefs([c0, c1])
@@ -1781,14 +1832,14 @@ def organize_BTSettl_atmospheres():
"""
Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011).
The code expects tp be run in cdbs/grid/BTSettl, and expects that the
- individual model files have been downloaded from online
+ individual model files have been downloaded from online
(https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/)
- and processed into python-readable ascii files.
+ and processed into python-readable ascii files.
"""
orig_dir = os.getcwd()
dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05']
#dirs = ['btm10', 'btm05', 'btp00', 'btp05']
-
+
# Go through each directory, turning each spectrum into a cdbs-ready file.
# Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file,
@@ -1820,13 +1871,13 @@ def organize_BTSettl_atmospheres():
tbhdu.header['TUNIT1'] = 'ANGSTROM'
tbhdu.header['TUNIT2'] = 'FLAM'
hdu_new = fits.HDUList([prihdu, tbhdu])
-
+
# Write new fits table in cdbs directory
hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True)
hdu_new.close()
count += 1
print('Done {0} of {1}'.format(count, len(files)))
-
+
# Now, clean up all the files made when unzipping the spectra
cmd1 = 'rm *.bz2'
cmd2 = 'rm *.tmp'
@@ -1837,7 +1888,7 @@ def organize_BTSettl_atmospheres():
print('==============================')
print('Done {0}'.format(ii))
print('==============================')
-
+
# Go back to original directory, move to next metallicity directory
os.chdir(orig_dir)
@@ -1873,7 +1924,7 @@ def make_BTSettl_catalog():
metal_flag = -1 * float(ii[3:])*0.1
else:
metal_flag = float(ii[3:])*0.1
-
+
# Now collect the info from the files
for jj in files:
tmp = jj.split('-')
@@ -1887,7 +1938,7 @@ def make_BTSettl_catalog():
else:
temp = float(tmp[0][3:]) * 100.0 # In kelvin
logg = float(tmp[1])
-
+
index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg))
name_str.append('{0}/{1}[Flux]'.format(ii, jj))
@@ -1900,10 +1951,10 @@ def make_BTSettl_catalog():
# Create catalog.fits file in directory with the models
catalog.write('catalog.fits', format = 'fits', overwrite=True)
-
+
# Move back to original directory, create the catalog.fits file
os.chdir(start_dir)
-
+
return
def rebin_BTSettl(make_unique=False):
@@ -1934,7 +1985,7 @@ def rebin_BTSettl(make_unique=False):
# tmp.append(ii)
#files_all = tmp
#=============================#
-
+
print( 'Rebinning BTSettl spectra')
if make_unique:
print('Making unique')
@@ -1954,14 +2005,14 @@ def rebin_BTSettl(make_unique=False):
# Make new output
c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave)
- c1 = fits.Column(name='Flux', format='E', array=flux_rebin)
-
+ c1 = fits.Column(name='Flux', format='E', array=flux_rebin)
+
cols = fits.ColDefs([c0, c1])
tbhdu = fits.BinTableHDU.from_columns(cols)
prihdu = fits.PrimaryHDU()
tbhdu.header['TUNIT1'] = 'ANGSTROM'
tbhdu.header['TUNIT2'] = 'FLAM'
-
+
outfile = path + files_all[ff].split('[')[0]
finalhdu = fits.HDUList([prihdu, tbhdu])
finalhdu.writeto(outfile, overwrite=True)
@@ -1971,9 +2022,9 @@ def rebin_BTSettl(make_unique=False):
outfile = path + files_all[ff].split('[')[0]
cmd = 'cp {0} {1}'.format(orig_file, outfile)
os.system(cmd)
-
+
print('Done {0} of {1}'.format(ff, len(files_all)))
-
+
return
def organize_WDKoester_atmospheres(path_to_dir):
@@ -1997,7 +2048,7 @@ def organize_WDKoester_atmospheres(path_to_dir):
for i in files:
data = Table.read(i, format='ascii')
-
+
wave = data['col1'] # angstrom
flux = data['col2'] # erg/s/cm^2/A
@@ -2013,12 +2064,12 @@ def organize_WDKoester_atmospheres(path_to_dir):
tbhdu.header['TUNIT1'] = 'ANGSTROM'
tbhdu.header['TUNIT2'] = 'FLAM'
hdu_new = fits.HDUList([prihdu, tbhdu])
-
+
# Write new fits table in cdbs directory
hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True)
hdu_new.close()
-
+
# Return to original directory
os.chdir(start_dir)
return
@@ -2035,10 +2086,10 @@ def make_WDKoester_catalog(path_to_dir):
"""
# Record current working directory for later
start_dir = os.getcwd()
-
+
# Enter atmosphere directory
os.chdir(path_to_dir)
-
+
# Extract parameters for each atmosphere from the filename,
# construct columns for catalog file
files = glob.glob("*dk.dat.fits")
@@ -2058,10 +2109,10 @@ def make_WDKoester_catalog(path_to_dir):
# Create catalog.fits file in directory with the models
catalog.write('catalog.fits', format = 'fits', overwrite=True)
-
+
# Move back to original directory, create the catalog.fits file
os.chdir(start_dir)
-
+
return
def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']):
@@ -2104,8 +2155,8 @@ def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']):
# Make new output
c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave)
- c1 = fits.Column(name='Flux', format='E', array=flux_rebin)
-
+ c1 = fits.Column(name='Flux', format='E', array=flux_rebin)
+
cols = fits.ColDefs([c0, c1])
tbhdu = fits.BinTableHDU.from_columns(cols)
prihdu = fits.PrimaryHDU(header=header0)
@@ -2114,8 +2165,8 @@ def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']):
outfile = path + files_all[ff].split('[')[0]
finalhdu = fits.HDUList([prihdu, tbhdu])
- finalhdu.writeto(outfile, overwrite=True)
+ finalhdu.writeto(outfile, overwrite=True)
return
-
-
+
+
diff --git a/spisea/conftest.py b/spisea/conftest.py
index 672b2733..fab9a899 100755
--- a/spisea/conftest.py
+++ b/spisea/conftest.py
@@ -12,13 +12,21 @@
from astropy.tests.pytest_plugins import * # noqa
del pytest_report_header
ASTROPY_HEADER = True
-else:
+elif astropy_version >= '3.0' and astropy_version < '5.1':
try:
from pytest_astropy_header.display import PYTEST_HEADER_MODULES, TESTED_VERSIONS
ASTROPY_HEADER = True
except ImportError:
ASTROPY_HEADER = False
+else:
+ # As of Astropy 5.1, the pytest plugins provided by Astropy have been removed
+ # and are instead provided by pytest-astropy-header
+ # (https://github.com/astropy/pytest-astropy-header)
+ from pytest_astropy_header.display import PYTEST_HEADER_MODULES, TESTED_VERSIONS
+ ASTROPY_HEADER = True
+
+
def pytest_configure(config):
diff --git a/spisea/el_files/fritz11_EL_table2.txt b/spisea/el_files/fritz11_EL_table2.txt
new file mode 100644
index 00000000..4e336c1d
--- /dev/null
+++ b/spisea/el_files/fritz11_EL_table2.txt
@@ -0,0 +1,22 @@
+wave ext ext_err
+1.282 7.91 0.11
+1.736 4.30 0.13
+2.166 2.49 0.11
+2.625 1.83 0.13
+2.758 1.51 0.19
+2.873 1.84 0.19
+3.039 2.07 0.19
+3.297 1.66 0.15
+3.740 1.19 0.14
+3.819 1.19 0.31
+3.907 1.09 0.30
+4.052 1.01 0.13
+4.376 1.09 0.29
+5.128 0.99 0.24
+5.908 1.04 0.24
+6.772 0.84 0.29
+7.459 0.81 0.23
+7.502 0.79 0.24
+8.760 2.04 0.34
+12.371 1.30 0.32
+19.062 1.30 0.50
diff --git a/spisea/el_files/fritz11_EL_table8.fits b/spisea/el_files/fritz11_EL_table8.fits
new file mode 100644
index 00000000..1c75b29a
Binary files /dev/null and b/spisea/el_files/fritz11_EL_table8.fits differ
diff --git a/spisea/evolution.py b/spisea/evolution.py
index e3ddf23c..2f9dd760 100755
--- a/spisea/evolution.py
+++ b/spisea/evolution.py
@@ -1,6 +1,6 @@
import math
import logging
-from numpy import searchsorted, genfromtxt
+from numpy import genfromtxt
import numpy as np
import os
import glob
@@ -10,6 +10,7 @@
from scipy import interpolate
import pylab as py
from spisea.utils import objects
+from spisea import exceptions
logger = logging.getLogger('evolution')
@@ -21,7 +22,46 @@
warnings.warn("SPISEA_MODELS is undefined; functionality "
"will be SEVERELY crippled.")
models_dir = ''
-
+
+# Function to get installed evo grid number
+def get_installed_grid_num(input_models_dir):
+ """
+ Get installed grid number
+ """
+ # Define the installed model grid number
+ file_name = input_models_dir + '/grid_version.txt'
+
+ # Read in the file. In the case where it doesn't
+ # exist, then grid version is assumed to be 1.0
+ # (since this didn't always exist)
+ try:
+ file1 = open(file_name, 'r')
+ read = file1.readlines()
+ evo_grid_num = float(read[1])
+ file1.close()
+ except FileNotFoundError:
+ evo_grid_num = 1.0
+
+ return evo_grid_num
+
+# Function to check evo grid version number
+def check_evo_grid_number(required_num, input_models_dir):
+ """
+ Check if installed grid meets the required
+ grid version number. Installed grid number must
+ be greater than or equal to this number
+ """
+
+ # Get installed gridnumber
+ grid_num = get_installed_grid_num(input_models_dir)
+
+ # Check: is installed grid number < required_num?
+ # If not, raise mismatch error
+ if grid_num < required_num:
+ raise exceptions.ModelMismatch(required_num, grid_num, 'evolution')
+
+ return grid_num
+
class StellarEvolution(object):
"""
Base Stellar evolution class.
@@ -39,67 +79,83 @@ class StellarEvolution(object):
z_list: list
List of metallicities
+
+ model_version_name: string
+ Name of the model class plus additional details like version
+ numbers and rotation if relevant.
"""
def __init__(self, model_dir, age_list, mass_list, z_list):
self.model_dir = model_dir
self.z_list = z_list
self.mass_list = mass_list
self.age_list = age_list
-
+ self.model_version_name = "None"
+
return
-
+
class Geneva(StellarEvolution):
def __init__(self):
r"""
Define intrinsic properties for Geneva stellar models.
"""
+ self.model_version_name = "Geneva"
# populate list of model masses (in solar masses)
mass_list = [(0.1 + i*0.005) for i in range(181)]
-
+
# define metallicity parameters for Geneva models
z_list = [0.01, 0.02, 0.03]
-
+
# populate list of isochrone ages (log scale)
age_list = [round(5.5 + 0.01*i, 2) for i in range(190)]
age_list += [round(7.4 + 0.05*i, 2) for i in range(12)]
age_list += [round(math.log10(1.e8*x), 2) for x in range(1, 10)]
age_list += [round(math.log10(1.e9*x), 2) for x in range(1, 10)]
age_list = age_list
-
- # specify location of model files
- model_dir = models_dir + 'geneva/'
+ # specify location of model files
+ self.model_dir = models_dir + 'geneva/'
+
StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list)
self.z_solar = 0.02
self.z_file_map = {0.01: 'z01/', 0.02: 'z02/', 0.03: 'z03/'}
-
+
+ # Define required evo_grid number
+ self.evo_grid_min = 1.0
+
def isochrone(self, age=1.e8, metallicity=0.0):
r"""
Extract an individual isochrone from the Geneva collection.
"""
+ # Error check to see if installed evolution model
+ # grid is compatible with code version. Also return
+ # current grid num
+ self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir)
+
# convert metallicity to mass fraction
z_defined = self.z_solar*10.**metallicity
-
+
# check age and metallicity are within bounds
if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))):
- logger.error('Requested age {0} is out of bounds.'.format(log_age))
-
+ raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.')
+
if ((z_defined < np.min(self.z_list)) or
(z_defined > np.max(self.z_list))):
- logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined))
-
+ raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.')
+
# convert age (in yrs) to log scale and find nearest value in grid
- age_idx = searchsorted(self.age_list, math.log10(age), side='right')
+ log_age = np.log10(age)
+
+ age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0]
iso_file = 'iso_' + str(self.age_list[age_idx]) + '.fits'
-
+
# find closest metallicity value
- z_idx = searchsorted(self.z_list, z_defined, side='left')
+ z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0]
z_dir = self.z_file_map[self.z_list[z_idx]]
-
+
# generate isochrone file string
full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file
-
+
# return isochrone data
return genfromtxt(full_iso_file, comments='#')
@@ -110,7 +166,7 @@ def isochrone(self, age=1.e8, metallicity=0.0):
class Ekstrom12(StellarEvolution):
"""
- Evolution models from
+ Evolution models from
`Ekstrom et al. 2012 `_.
Downloaded from `website `_.
@@ -121,12 +177,16 @@ class Ekstrom12(StellarEvolution):
If true, then use rotating Ekstrom models. Default is true.
"""
def __init__(self, rot=True):
+ if rot:
+ self.model_version_name = "Ekstrom12-rot"
+ else:
+ self.model_version_name = "Ekstrom12-norot"
# define metallicity parameters for Ekstrom+12 models
self.z_list = [0.014]
-
+
# populate list of isochrone ages (log scale)
self.age_list = np.arange(6.0, 8.0+0.005, 0.01)
-
+
# Specify location of model files
self.model_dir = models_dir+'Ekstrom2012/'
@@ -136,41 +196,46 @@ def __init__(self, rot=True):
# Specify rotation or not
self.rot = rot
-
+
+ # Define required evo_grid number
+ self.evo_grid_min = 1.0
+
def isochrone(self, age=1.e8, metallicity=0.0):
r"""
Extract an individual isochrone from the Ekstrom+12 Geneva collection.
"""
+ # Error check to see if installed evolution model
+ # grid is compatible with code version. Also return
+ # current grid num
+ self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir)
+
# convert metallicity to mass fraction
z_defined = self.z_solar*10.**metallicity
log_age = math.log10(age)
-
+
# check age and metallicity are within bounds
if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))):
- logger.error('Requested age {0} is out of bounds.'.format(log_age))
-
+ raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.')
+
if ((z_defined < np.min(self.z_list)) or
(z_defined > np.max(self.z_list))):
- logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined))
-
+ raise ValueError(f'Requested metallicity z_solar * 10^{metallicity} = {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.')
+
# Find nearest age in grid to input grid
- if log_age != self.age_list[0]:
- age_idx = searchsorted(self.age_list, log_age, side='right')
- else:
- age_idx = searchsorted(self.age_list, log_age, side='left')
+ age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0]
iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx])
-
+
# find closest metallicity value
- z_idx = searchsorted(self.z_list, z_defined, side='left')
+ z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0]
z_dir = self.z_file_map[self.z_list[z_idx]]
-
+
# generate isochrone file string
- if self.rot:
+ if self.rot:
full_iso_file = self.model_dir + 'iso/' + z_dir + 'rot/' + iso_file
else:
full_iso_file = self.model_dir + 'iso/' + z_dir + 'norot/' + iso_file
-
+
# Return isochrone data
iso = Table.read(full_iso_file, format='fits')
iso.rename_column('col4', 'Z')
@@ -202,11 +267,11 @@ def format_isochrones(input_iso_dir):
Parse iso.fits (filename hardcoded) file downloaded from Ekstrom+12
models, create individual isochrone files for the different ages.
- input_iso_directory should lead to
- Ekstrom2012/iso/
+ input_iso_directory should lead to
+ Ekstrom2012/iso/
directory, where iso.fits file should be located.
- Creates two new directories, rot and norot, which contain their
+ Creates two new directories, rot and norot, which contain their
respective isochrones.
"""
# Store current directory for later
@@ -214,13 +279,13 @@ def format_isochrones(input_iso_dir):
# Move into metallicity direcotry, read iso.fits file
os.chdir(input_iso_dir)
-
+
print( 'Read Input: this is slow')
iso = Table.read('iso.fits')
print( 'Done' )
-
+
ages_all = iso['col1']
-
+
# Extract the unique ages
age_arr = np.unique(ages_all)
@@ -234,7 +299,7 @@ def format_isochrones(input_iso_dir):
else:
os.mkdir('rot')
os.mkdir('norot')
-
+
print( 'Making individual isochrone files')
for age in age_arr:
good = np.where(ages_all == age)
@@ -245,7 +310,7 @@ def format_isochrones(input_iso_dir):
tmp_r = iso[good][idx_r]
tmp_n = iso[good][idx_n]
-
+
# Write tables
tmp_r.write('rot/iso_{0:4.2f}.fits'.format(age))
tmp_n.write('norot/iso_{0:4.2f}.fits'.format(age))
@@ -262,14 +327,14 @@ def create_iso(fileList, ageList, rot=True):
iso.fits format for parse_iso code.
fileList: list of downloaded isochrone files (could be one)
-
+
ageList: list of lists of ages associated with each file in filelist.
MUST BE IN SAME ORDER AS ISOCHRONES IN FILE! Also needs to be in logAge
-
+
rot = TRUE: assumes that models are rotating, will add appropriate column
-
+
This code writes the individual files, which is then easiest to combine by hand
- in aquamacs
+ in aquamacs
"""
# Read each file in fileList individually, add necessary columns
for i in range(len(fileList)):
@@ -280,14 +345,14 @@ def create_iso(fileList, ageList, rot=True):
start = np.where(t['M_ini'] == 0.8)
# Now, each identified start is assumed to be associated with the
- # corresponding age in ages
+ # corresponding age in ages
if len(start[0]) != len(ages):
print( 'Ages mismatched in file! Quitting...')
return
age_arr = np.zeros(len(t))
-
+
for j in range(len(start[0])):
low_ind = start[0][j]
# Deal with case at end of file
@@ -306,9 +371,9 @@ def create_iso(fileList, ageList, rot=True):
rot_val[:] = 'r'
if not rot:
rot_val[:] = 'n'
-
+
col_rot = Column(rot_val, name='Rot')
-
+
t.add_column(col_rot, index=0)
t.add_column(col_age, index=0)
@@ -322,7 +387,7 @@ def create_iso(fileList, ageList, rot=True):
class Parsec(StellarEvolution):
"""
- Evolution models from
+ Evolution models from
`Bressan et al. 2012 `_,
version 1.2s.
@@ -346,55 +411,60 @@ def __init__(self):
models.
"""
# populate list of model masses (in solar masses)
+ self.model_version_name = "Parsec1.2s"
#mass_list = [(0.1 + i*0.005) for i in range(181)]
-
+
# define metallicity parameters for Parsec models
self.z_list = [0.005, 0.015, 0.04]
-
+
# populate list of isochrone ages (log scale)
self.age_list = np.arange(6.6, 10.12+0.005, 0.01)
self.age_list = np.append(6.40, self.age_list)
-
+
# Specify location of model files
self.model_dir = models_dir+'ParsecV1.2s/'
# Specifying metallicity
self.z_solar = 0.015
self.z_file_map = {0.005: 'z005/', 0.015: 'z015/', 0.04: 'z04/'}
-
-
+
+ # Define required evo_grid number
+ self.evo_grid_min = 1.0
+
def isochrone(self, age=1.e8, metallicity=0.0):
r"""
Extract an individual isochrone from the Parsec version 1.2s
collection.
"""
+ # Error check to see if installed evolution model
+ # grid is compatible with code version. Also return
+ # current grid num
+ self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir)
+
# convert metallicity to mass fraction
z_defined = self.z_solar*10.**metallicity
log_age = math.log10(age)
-
+
# check age and metallicity are within bounds
if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))):
- logger.error('Requested age {0} is out of bounds.'.format(log_age))
-
+ raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.')
+
if ((z_defined < np.min(self.z_list)) or
(z_defined > np.max(self.z_list))):
- logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined))
-
+ raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.')
+
# Find nearest age in grid to input grid
- if log_age != self.age_list[0]:
- age_idx = searchsorted(self.age_list, log_age, side='right')
- else:
- age_idx = searchsorted(self.age_list, log_age, side='left')
+ age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0]
iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx])
-
+
# find closest metallicity value
- z_idx = searchsorted(self.z_list, z_defined, side='left')
+ z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0]
z_dir = self.z_file_map[self.z_list[z_idx]]
-
+
# generate isochrone file string
full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file
-
+
# return isochrone data
iso = Table.read(full_iso_file, format='fits')
iso.rename_column('col1', 'Z')
@@ -410,19 +480,19 @@ def isochrone(self, age=1.e8, metallicity=0.0):
# Parsec doesn't identify WR stars, so identify all as "False"
isWR = Column([False] * len(iso), name='isWR')
iso.add_column(isWR)
-
+
iso.meta['log_age'] = log_age
iso.meta['metallicity_in'] = metallicity
iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar)
return iso
-
+
def format_isochrones(input_iso_dir, metallicity_list):
r"""
Parse isochrone file downloaded from Parsec version 1.2 for different
metallicities, create individual isochrone files for the different ages.
-
+
input_iso_dir: points to ParsecV1.2s/iso directory. Assumes metallicity
subdirectories already exist with isochrone files downloaded in them
(isochrones files expected to start with "output*")
@@ -435,7 +505,7 @@ def format_isochrones(input_iso_dir, metallicity_list):
# Move into isochrone directory
os.chdir(input_iso_dir)
-
+
# Work on each metallicity isochrones individually
for metal in metallicity_list:
# More into metallicity directory, read isochrone file
@@ -445,7 +515,7 @@ def format_isochrones(input_iso_dir, metallicity_list):
print( 'Read Input: this is slow')
iso = Table.read(isoFile[0], format='fits')
print( 'Done')
-
+
ages_all = iso['col2']
# Extract the unique ages
@@ -474,9 +544,9 @@ def format_isochrones(input_iso_dir, metallicity_list):
class Pisa(StellarEvolution):
"""
- Evolution models from
+ Evolution models from
`Tognelli et al. 2011 `_.
-
+
Downloaded `online `_
Notes
@@ -492,51 +562,57 @@ def __init__(self):
Define intrinsic properties for the Pisa (Tognelli+11) stellar
models.
"""
+ self.model_version_name = "Pisa"
# define metallicity parameters for Pisa models
self.z_list = [0.015]
-
+
# populate list of isochrone ages (log scale)
self.age_list = np.arange(6.0, 8.01+0.005, 0.01)
-
+
# Specify location of model files
self.model_dir = models_dir+'Pisa2011/'
# Specifying metallicity
self.z_solar = 0.015
self.z_file_map = {0.015: 'z015/'}
-
+
+ # Define required evo_grid number
+ self.evo_grid_min = 1.0
+
def isochrone(self, age=1.e8, metallicity=0.0):
r"""
Extract an individual isochrone from the Pisa (Tognelli+11)
collection.
"""
+ # Error check to see if installed evolution model
+ # grid is compatible with code version. Also return
+ # current grid num
+ self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir)
+
# convert metallicity to mass fraction
z_defined = self.z_solar*10.**metallicity
log_age = math.log10(age)
-
+
# check age and metallicity are within bounds
if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))):
- logger.error('Requested age {0} is out of bounds.'.format(log_age))
-
+ raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.')
+ return
if ((z_defined < np.min(self.z_list)) or
(z_defined > np.max(self.z_list))):
- logger.error('Requested metallicity {0} is out of bounds for evolution model. Available z-vals: {1}.'.format(z_defined, self.z_list))
-
+ raise ValueError(f'Requested metallicity {z_defined} is out of bounds for evolution model. Available z-vals: {self.z_list}.')
+
# Find nearest age in grid to input grid
- if log_age != self.age_list[0]:
- age_idx = searchsorted(self.age_list, log_age, side='right')
- else:
- age_idx = searchsorted(self.age_list, log_age, side='left')
+ age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0]
iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx])
-
+
# find closest metallicity value
- z_idx = searchsorted(self.z_list, z_defined, side='left')
+ z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0]
z_dir = self.z_file_map[self.z_list[z_idx]]
-
+
# generate isochrone file string
full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file
-
+
# return isochrone data
iso = Table.read(full_iso_file, format='fits')
iso.rename_column('col1', 'logL')
@@ -549,7 +625,7 @@ def isochrone(self, age=1.e8, metallicity=0.0):
isWR = Column([False] * len(iso), name='isWR')
iso.add_column(isWR)
- # Add columns for current mass and phase.
+ # Add columns for current mass and phase.
iso.add_column( Column(np.zeros(len(iso)), name = 'phase'))
iso.add_column( Column(iso['mass'], name = 'mass_current'))
@@ -567,7 +643,7 @@ def format_isochrones(input_iso_dir, metallicity_list):
input_iso_dir: points to Pisa2011/iso directory. Individual
metallicity directories with the downloaded isochrones are
expected to already exist there
-
+
metallicity_list is the list of metallicities on which function
is to be run.
@@ -589,7 +665,7 @@ def format_isochrones(input_iso_dir, metallicity_list):
else:
# Create a ReadMe with the original file names to preserve the
# model details
-
+
cmd = "ls *.FITS > ReadMe"
os.system(cmd)
@@ -623,14 +699,14 @@ def make_isochrone_grid(metallicity=0.015):
while 0.0150 would not)
"""
logAge_arr = np.arange(6.0, 8.0+0.005, 0.01)
-
+
count = 0
for logAge in logAge_arr:
# Could interpolate using evolutionary tracks, but less accurate.
make_isochrone_pisa_interp(logAge, metallicity=metallicity)
count += 1
-
+
print( 'Done {0} of {1} models'.format(count, (len(logAge_arr))))
return
@@ -640,70 +716,75 @@ def make_isochrone_grid(metallicity=0.015):
#==============================#
class Baraffe15(StellarEvolution):
"""
- Evolution models published in
+ Evolution models published in
`Baraffe et al. 2015 `_.
Downloaded from `BHAC15 site `_.
"""
def __init__(self):
+ self.model_version_name = "Baraffe15"
# define metallicity parameters for Baraffe models
self.z_list = [0.015]
-
+
# populate list of isochrone ages (log scale)
self.age_list = np.arange(6.0, 8.0+0.005, 0.01)
-
+
# Specify location of model files
self.model_dir = models_dir+'Baraffe15/'
# Specifying metallicity
self.z_solar = 0.015
self.z_file_map = {0.015: 'z015/'}
-
-
+
+ # Define required evo_grid number
+ self.evo_grid_min = 1.0
+
def isochrone(self, age=5.e7, metallicity=0.0):
r"""
Extract an individual isochrone from the Baraffe+15
collection.
"""
- # convert metallicity to mass fraction
+ # Error check to see if installed evolution model
+ # grid is compatible with code version. Also return
+ # current grid num
+ self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir)
+
+ # convert metallicity to mass fraction
z_defined = self.z_solar*10.**metallicity
log_age = math.log10(age)
-
+
# check age and metallicity are within bounds
if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))):
- logger.error('Requested age {0} is out of bounds.'.format(log_age))
-
+ raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.')
+
if ((z_defined < np.min(self.z_list)) or
(z_defined > np.max(self.z_list))):
- logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined))
-
+ raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.')
+
# Find nearest age in grid to input grid
- if log_age != self.age_list[0]:
- age_idx = searchsorted(self.age_list, log_age, side='right')
- else:
- age_idx = searchsorted(self.age_list, log_age, side='left')
+ age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0]
iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx])
-
+
# find closest metallicity value
- z_idx = searchsorted(self.z_list, z_defined, side='left')
+ z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0]
z_dir = self.z_file_map[self.z_list[z_idx]]
-
+
# generate isochrone file string
full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file
-
+
# Read isochrone, get in proper format
iso = Table.read(full_iso_file, format='fits')
iso.rename_column('Mass', 'mass')
iso.rename_column('logG', 'logg')
iso['logT'] = np.log10(iso['Teff'])
-
+
# Pisa models are too low for WR phase, add WR column with all False
iso['logT_WR'] = iso['logT']
isWR = Column([False] * len(iso), name='isWR')
iso.add_column(isWR)
- # Add columns for current mass and phase.
+ # Add columns for current mass and phase.
iso.add_column( Column(np.zeros(len(iso)), name = 'phase'))
iso.add_column( Column(iso['mass'], name = 'mass_current'))
@@ -717,11 +798,11 @@ def tracks_to_isochrones(self, tracksFile):
r"""
Create isochrones at desired age sampling (6.0 < logAge < 8.0,
steps of 0.01; hardcoded) from the Baraffe+15 tracks downloaded
- online.
+ online.
tracksFile: tracks.dat file downloaded from Baraffe+15, with format
modified to be read in python
-
+
Writes isochrones in iso/ subdirectory off of work directory. Will
create this subdirectory if it doesn't already exist
"""
@@ -729,7 +810,7 @@ def tracks_to_isochrones(self, tracksFile):
age_arr = np.arange(6.0, 8.0+0.005, 0.01)
#age_arr = [6.28]
-
+
# Loop through the masses, interpolating track over time at each.
# Resample track properties at hardcoded ages
masses = np.unique(tracks['col1'])
@@ -754,13 +835,13 @@ def tracks_to_isochrones(self, tracksFile):
# Interpolate Teff, logL, and logG using linear interpolator
tck_Teff = interpolate.interp1d(tmp['col2'], tmp['col3'])
tck_logL = interpolate.interp1d(tmp['col2'], tmp['col4'])
- tck_logG = interpolate.interp1d(tmp['col2'], tmp['col5'])
+ tck_logG = interpolate.interp1d(tmp['col2'], tmp['col5'])
Teff = tck_Teff(age_arr)
logL = tck_logL(age_arr)
logG = tck_logG(age_arr)
-
+
# Test interpolation if desired
test=False
if test:
@@ -787,9 +868,9 @@ def tracks_to_isochrones(self, tracksFile):
py.xlabel('logAge')
py.ylabel('logG')
py.savefig('test_logG.png')
-
+
pdb.set_trace()
-
+
# Build upon arrays of interpolated values
mass_interp = np.concatenate((mass_interp, np.ones(len(Teff)) * mass))
age_interp = np.concatenate((age_interp, age_arr))
@@ -824,11 +905,11 @@ def tracks_to_isochrones(self, tracksFile):
def test_age_interp(self, onlineIso, interpIso):
r"""
Compare one of our interpolated ischrones with one
- of the isochrones provided online by Baraffe+15.
+ of the isochrones provided online by Baraffe+15.
"""
true_iso = Table.read(onlineIso, format='ascii')
our_iso = Table.read(interpIso, format='fits')
-
+
# Compare the two isochrones using plots. Look at mass vs. Teff,
# mass vs. logG, mass vs. logL. Ideally these isochrones should
# be identical
@@ -860,7 +941,7 @@ def test_age_interp(self, onlineIso, interpIso):
Teff_diff = np.mean(abs(true_iso['col2'][7:] - our_iso['Teff']))
logL_diff = np.mean(abs(true_iso['col3'][7:] - our_iso['logL']))
logG_diff = np.mean(abs(true_iso['col4'][7:] - our_iso['logG']))
-
+
print( 'Average abs difference in Teff: {0}'.format(Teff_diff))
print( 'Average abs difference in logL: {0}'.format(logL_diff))
print( 'Average abs difference in logg: {0}'.format(logG_diff))
@@ -877,7 +958,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso):
name = BaraffeIso.split('_')
age = name[1][:4]
-
+
# Extract paramters we need
b_mass = b['Mass']
b_logT = np.log10(b['Teff'])
@@ -891,7 +972,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso):
m05_b = np.where( abs(b_mass - 0.5) == min(abs(b_mass - 0.5)) )
m05_p = np.where( abs(p_mass - 0.5) == min(abs(p_mass - 0.5)) )
-
+
# Comparison plots
py.figure(1, figsize=(10,10))
py.clf()
@@ -919,7 +1000,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso):
#py.axis([4.4, 3.4, -3, 4])
#py.gca().invert_xaxis()
py.legend()
- py.savefig('BaraffePisa_comp_mass_{0}.png'.format(age))
+ py.savefig('BaraffePisa_comp_mass_{0}.png'.format(age))
return
@@ -929,7 +1010,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso):
class MISTv1(StellarEvolution):
"""
Define intrinsic properties for the MIST v1 stellar
- models.
+ models.
Models originally downloaded from `online server `_.
@@ -940,64 +1021,90 @@ class MISTv1(StellarEvolution):
was downloaded from MIST website on 2/2017, while Version 1.2
was downloaded on 8/2018 (solar metallicity)
and 4/2019 (other metallicities). Default is 1.2.
+
+ synthpop_extension: boolean (default False)
+ If True, the isochrones are extended down to a minimum initial
+ mass of 0.1Msun using grids interpolated via SynthPop. If False,
+ the web-downloaded MIST isochrones are used with their varying
+ lower mass limits. True option is only valid for version=1.2.
"""
- def __init__(self, version=1.2):
- # define metallicity parameters for Parsec models
- self.z_list = [0.0000015, # [Fe/H] = -4.00
- 0.0000047, # [Fe/H] = -3.50
- 0.000015, # [Fe/H] = -3.00
- 0.000047, # [Fe/H] = -2.50
- 0.00015, # [Fe/H] = -2.00
- 0.00026, # [Fe/H] = -1.75
- 0.00047, # [Fe/H] = -1.50
- 0.00084, # [Fe/H] = -1.25
- 0.0015, # [Fe/H] = -1.00
- 0.0026, # [Fe/H] = -0.75
- 0.0046, # [Fe/H] = -0.50
- 0.0082, # [Fe/H] = -0.25
- 0.015, # [Fe/H] = 0.00
- 0.024, # [Fe/H] = 0.25
- 0.041] # [Fe/H] = 0.50
-
+ def __init__(self, version=1.2, synthpop_extension=False):
+ # define metallicity parameters for MIST models
+ self.z_list = [0.0000014, # [Fe/H] = -4.00
+ 0.0000045, # [Fe/H] = -3.50
+ 0.000014, # [Fe/H] = -3.00
+ 0.000045, # [Fe/H] = -2.50
+ 0.00014, # [Fe/H] = -2.00
+ 0.00025, # [Fe/H] = -1.75
+ 0.00045, # [Fe/H] = -1.50
+ 0.00080, # [Fe/H] = -1.25
+ 0.0014, # [Fe/H] = -1.00
+ 0.0025, # [Fe/H] = -0.75
+ 0.0045, # [Fe/H] = -0.50
+ 0.0080, # [Fe/H] = -0.25
+ 0.014, # [Fe/H] = 0.00
+ 0.025, # [Fe/H] = 0.25
+ 0.045] # [Fe/H] = 0.50
+
# populate list of isochrone ages (log scale)
self.age_list = np.arange(5.01, 10.30+0.005, 0.01)
# Set version directory
self.version = version
- if self.version == 1.0:
+ self.synthpop_extension = synthpop_extension
+ if (self.version == 1.0) and (not synthpop_extension):
+ self.model_version_name = 'MISTv1.0'
version_dir = 'v1.0/'
+ elif (self.version == 1.0) and synthpop_extension:
+ raise ValueError('Synthpop isochrone extension not supported for MISTv1.0 isochrones')
elif self.version == 1.2:
+ self.model_version_name = 'MISTv1.2'
version_dir = 'v1.2/'
else:
raise ValueError('Version {0} not supported for MIST isochrones'.format(version))
-
+
# Specify location of model files
self.model_dir = models_dir+'MISTv1/' + version_dir
+ if self.synthpop_extension:
+ self.model_version_name = self.model_version_name + '-synthpop'
+ self.model_extension_dir = models_dir+'MISTv1/' + version_dir[:-1] + '-synthpop/'
+ if not os.path.exists(self.model_extension_dir):
+ raise ValueError(f'Missing {self.model_extension_dir}. Please download the latest SPISEA data at https://w.astro.berkeley.edu/~jlu/spisea/spisea_models.tar.gz.')
+
+ else:
+ self.model_extension_dir = None
# Specifying metallicity
self.z_solar = 0.0142
- self.z_file_map = {0.0000015: 'z0000015/',
- 0.0000047: 'z0000047/',
- 0.000015: 'z000015/',
- 0.000047: 'z000047/',
- 0.00015: 'z00015/',
- 0.00026: 'z00026/',
- 0.00047: 'z00047/',
- 0.00084: 'z00084/',
- 0.0015: 'z0015/',
- 0.0026: 'z0026/',
- 0.0046: 'z0046/',
- 0.0082: 'z0082/',
- 0.015: 'z015/',
- 0.024: 'z024/',
- 0.041: 'z041/'}
-
-
+ self.z_file_map = {0.0000014: 'z0000014/',
+ 0.0000045: 'z0000045/',
+ 0.000014: 'z000014/',
+ 0.000045: 'z000045/',
+ 0.00014: 'z00014/',
+ 0.00025: 'z00025/',
+ 0.00045: 'z00045/',
+ 0.00080: 'z00080/',
+ 0.0014: 'z0014/',
+ 0.0025: 'z0025/',
+ 0.0045: 'z0045/',
+ 0.0080: 'z0080/',
+ 0.014: 'z014/',
+ 0.025: 'z025/',
+ 0.045: 'z045/'}
+
+ # Define required evo_grid number (now 1.2 for synthpop extension)
+ self.evo_grid_min = 1.2
+
def isochrone(self, age=1.e8, metallicity=0.0):
r"""
Extract an individual isochrone from the MISTv1
collection.
"""
+ # First, error check to see if installed evolution model
+ # grid is compatible with code version. Also return
+ # current grid num
+ self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir)
+
# convert metallicity to mass fraction
z_defined = self.z_solar * (10.**metallicity)
@@ -1005,31 +1112,34 @@ def isochrone(self, age=1.e8, metallicity=0.0):
# check age and metallicity are within bounds
if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))):
- logger.error('Requested age {0} is out of bounds.'.format(log_age))
-
+ raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.')
+
if ((z_defined < np.min(self.z_list)) or
(z_defined > np.max(self.z_list))):
- logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined))
+ raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.')
# Find nearest age in grid to input grid
- if log_age != self.age_list[0]:
- age_idx = searchsorted(self.age_list, log_age, side='right')
- else:
- age_idx = searchsorted(self.age_list, log_age, side='left')
+ age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0]
iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx])
-
+
# find closest metallicity value
- z_idx = searchsorted(self.z_list, z_defined, side='left')
- if z_idx == len(self.z_list): # in case just over last index
- z_idx = z_idx - 1
+ z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0]
z_dir = self.z_file_map[self.z_list[z_idx]]
-
+
# generate isochrone file string
full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file
-
+ if self.synthpop_extension:
+ addl_iso_file = self.model_extension_dir + 'iso/' + z_dir + iso_file
+
# return isochrone data. Column locations depend on
# version
iso = Table.read(full_iso_file, format='fits')
+ if self.synthpop_extension:
+ addl_iso = Table.read(addl_iso_file, format='fits')
+ for row in addl_iso:
+ iso.add_row(row)
+ iso.sort('col3')
+ iso.meta['comments2'] = addl_iso.meta['comments']
if self.version == 1.0:
iso.rename_column('col7', 'Z')
iso.rename_column('col2', 'logAge')
@@ -1064,8 +1174,8 @@ def isochrone(self, age=1.e8, metallicity=0.0):
iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar)
return iso
-
- def format_isochrones(self, input_iso_dir, metallicity_list):
+
+ def format_isochrones(self):
r"""
Parse isochrone file downloaded from MIST web server,
create individual isochrone files for the different ages.
@@ -1080,17 +1190,22 @@ def format_isochrones(self, input_iso_dir, metallicity_list):
List of metallicity directories to check (i.e. z015 is solar)
"""
+ # Get input iso dir, metallicity list from evo object
+ input_iso_dir = '{0}/iso'.format(self.model_dir)
+ metallicity_list = list(self.z_file_map.values())
+
# Store current directory for later
start_dir = os.getcwd()
# Move into isochrone directory
os.chdir(input_iso_dir)
-
+
# Work on each metallicity isochrones individually
for metal in metallicity_list:
# More into metallicity directory, read isochrone file
os.chdir(metal)
+ # Read all available iso files, stack them together
isoFile = glob.glob('MIST_iso*')
print( 'Read Input: this is slow')
iso_f = Table()
@@ -1138,77 +1253,99 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution):
The model used depends on the age of the population and what stellar masses
are being modeled:
-
+
For logAge < 7.4:
* Baraffe: 0.08 - 0.4 M_sun
- * Baraffe/Pisa transition: 0.4 - 0.5 M_sun
+ * Baraffe/Pisa transition: 0.4 - 0.5 M_sun
* Pisa: 0.5 M_sun to the highest mass in Pisa isochrone (typically 5 - 7 Msun)
* Geneva: Highest mass of Pisa models to 120 M_sun
For logAge > 7.4:
* Parsec v1.2s: full mass range
-
+
Parameters
----------
rot: boolean, optional
If true, then use rotating Ekstrom models. Default is true.
"""
def __init__(self, rot=True):
+ if rot:
+ self.model_version_name = "MergedBaraffePisaEkstromParsec-rot"
+ else:
+ self.model_version_name = "MergedBaraffePisaEkstromParsec-norot"
# populate list of model masses (in solar masses)
mass_list = [(0.1 + i*0.005) for i in range(181)]
-
+
# define metallicity parameters for Geneva models
z_list = [0.015]
-
+
# populate list of isochrone ages (log scale)
age_list = np.arange(6.0, 10.091, 0.01).tolist()
-
+
# specify location of model files
model_dir = models_dir + 'merged/baraffe_pisa_ekstrom_parsec/'
StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list)
self.z_solar = 0.015
-
+
# Switch to specify rotating/non-rotating models
if rot:
self.z_file_map = {0.015: 'z015_rot/'}
else:
self.z_file_map = {0.015: 'z015_norot/'}
-
-
+
+ # Define required evo_grid number
+ self.evo_grid_min = 1.0
+
+
def isochrone(self, age=1.e8, metallicity=0.0):
r"""
- Extract an individual isochrone from the Baraffe-Pisa-Ekstrom-Parsec
+ Extract an individual isochrone from the Baraffe-Pisa-Ekstrom-Parsec
collection
"""
+ # Error check to see if installed evolution model
+ # grid is compatible with code version. Also return
+ # current grid num
+ self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir)
+
# convert metallicity to mass fraction
z_defined = self.z_solar*10.**metallicity
log_age = math.log10(age)
-
+
# check age and metallicity are within bounds
if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))):
- logger.error('Requested age {0} is out of bounds.'.format(log_age))
-
+ raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.')
+
if ((z_defined < np.min(self.z_list)) or
(z_defined > np.max(self.z_list))):
- logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined))
-
- # convert age (in yrs) to log scale and find nearest value in grid
- age_idx = searchsorted(self.age_list, log_age, side='right')
+ raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.')
+
+ # Find nearest age in grid to input grid
+ age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0]
iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx])
-
+
+
# find closest metallicity value
- z_idx = searchsorted(self.z_list, z_defined, side='left')
+ z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0]
z_dir = self.z_file_map[self.z_list[z_idx]]
# generate isochrone file string
full_iso_file = self.model_dir + z_dir + iso_file
# return isochrone data
- iso = Table.read(full_iso_file, format='fits')
+ try:
+ # FITS version of file (older model evolution grids)
+ iso = Table.read(full_iso_file, format='fits')
+ except:
+ # ASCII version of files (newer model evo grids
+ iso_file = 'iso_{0:.2f}.dat'.format(self.age_list[age_idx])
+ full_iso_file = self.model_dir + z_dir + iso_file
+
+ iso = Table.read(full_iso_file, format='ascii')
+
iso.rename_column('col1', 'mass')
iso.rename_column('col2', 'logT')
iso.rename_column('col3', 'logL')
@@ -1223,18 +1360,18 @@ def isochrone(self, age=1.e8, metallicity=0.0):
idx_WR = np.where(iso['logT'] != iso['logT_WR'])
isWR[idx_WR] = True
iso.add_column(isWR)
-
+
iso.meta['log_age'] = log_age
iso.meta['metallicity_in'] = metallicity
iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar)
-
+
return iso
class MergedPisaEkstromParsec(StellarEvolution):
"""
Same as MergedBaraffePisaEkstromParsec, but without
- the Baraffe models.
+ the Baraffe models.
Parameters
----------
@@ -1242,15 +1379,19 @@ class MergedPisaEkstromParsec(StellarEvolution):
If true, then use rotating Ekstrom models. Default is true.
"""
def __init__(self, rot=True):
+ if rot:
+ self.model_version_name = "MergedPisaEkstromParsec-rot"
+ else:
+ self.model_version_name = "MergedPisaEkstromParsec-norot"
# populate list of model masses (in solar masses)
mass_list = [(0.1 + i*0.005) for i in range(181)]
-
+
# define metallicity parameters for Geneva models
z_list = [0.015]
-
+
# populate list of isochrone ages (log scale)
age_list = np.arange(6.0, 8.001, 0.01).tolist()
-
+
# specify location of model files
model_dir = models_dir + 'merged/pisa_ekstrom_parsec/'
StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list)
@@ -1261,8 +1402,16 @@ def __init__(self, rot=True):
self.z_file_map = {0.015: 'z015_rot/'}
else:
self.z_file_map = {0.015: 'z015_norot/'}
-
-
+
+ # Define required evo_grid number
+ self.evo_grid_min = 1.0
+
+ # Error check to see if installed evolution model
+ # grid is compatible with code version. Also return
+ # current grid num
+ self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir)
+
+
def isochrone(self, age=1.e8, metallicity=0.0):
r"""
Extract an individual isochrone from the Pisa-Ekstrom-Parsec collection.
@@ -1271,20 +1420,20 @@ def isochrone(self, age=1.e8, metallicity=0.0):
z_defined = self.z_solar*10.**metallicity
log_age = math.log10(age)
-
+
# check age and metallicity are within bounds
if (log_age < self.age_list[0]) or (log_age > self.age_list[-1]):
- logger.error('Requested age {0} is out of bounds.'.format(log_age))
-
+ raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.')
+
if not z_defined in self.z_list:
- logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined))
-
- # convert age (in yrs) to log scale and find nearest value in grid
- age_idx = searchsorted(self.age_list, log_age, side='right')
+ raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.')
+
+ # Find nearest age in grid to input grid
+ age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0]
iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx])
-
+
# find closest metallicity value
- z_idx = searchsorted(self.z_list, z_defined, side='left')
+ z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0]
z_dir = self.z_file_map[self.z_list[z_idx]]
# generate isochrone file string
@@ -1302,7 +1451,7 @@ def isochrone(self, age=1.e8, metallicity=0.0):
iso.meta['log_age'] = log_age
iso.meta['metallicity_in'] = metallicity
iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar)
-
+
return iso
class MergedSiessGenevaPadova(StellarEvolution):
@@ -1317,26 +1466,27 @@ class MergedSiessGenevaPadova(StellarEvolution):
* Padova (`Marigo et al. 2008 `_)
For logAge < 7.4:
-
+
* Siess: 0.1 - 7 M_sun
* Siess/Geneva transition: 7 - 9 M_sun
* Geneva: > 9 M_sun
For logAge > 7.4:
-
+
* Padova: full mass range
"""
def __init__(self):
"""
- Define intrinsic properties for merged Siess-meynetMaeder-Padova
+ Define intrinsic properties for merged Siess-meynetMaeder-Padova
stellar models.
"""
+ self.model_version_name = "MergedSiessGenevaPadova"
# populate list of model masses (in solar masses)
mass_list = [(0.1 + i*0.005) for i in range(181)]
-
+
# define metallicity parameters for Geneva models
z_list = [0.02]
-
+
# populate list of isochrone ages (log scale)
age_list = np.arange(5.5, 7.41, 0.01).tolist()
age_list.append(7.48)
@@ -1356,16 +1506,24 @@ def __init__(self):
age_list.append(9.60)
age_list.append(9.70)
age_list.append(9.78)
-
+
# specify location of model files
model_dir = models_dir + 'merged/siess_meynetMaeder_padova/'
StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list)
self.z_solar = 0.02
-
+
# Metallicity map
self.z_file_map = {0.02: 'z02/'}
-
-
+
+ # Define required evo_grid number
+ self.evo_grid_min = 1.0
+
+ # Error check to see if installed evolution model
+ # grid is compatible with code version. Also return
+ # current grid num
+ self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir)
+
+
def isochrone(self, age=1.e8, metallicity=0.0):
r"""
Extract an individual isochrone from the Siess-Geneva-Padova collection.
@@ -1374,20 +1532,20 @@ def isochrone(self, age=1.e8, metallicity=0.0):
z_defined = self.z_solar*10.**metallicity
log_age = math.log10(age)
-
+
# check age and metallicity are within bounds
if (log_age < self.age_list[0]) or (log_age > self.age_list[-1]):
- logger.error('Requested age {0} is out of bounds.'.format(log_age))
-
+ raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.')
+
if not z_defined in self.z_list:
- logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined))
-
- # convert age (in yrs) to log scale and find nearest value in grid
- age_idx = searchsorted(self.age_list, log_age, side='right')
- iso_file = 'iso_{0:.2f}.dat'.format(self.age_list[age_idx])
-
+ raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.')
+
+ # Find nearest age in grid to input grid
+ age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0]
+ iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx])
+
# find closest metallicity value
- z_idx = searchsorted(self.z_list, z_defined, side='left')
+ z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0]
z_dir = self.z_file_map[self.z_list[z_idx]]
# generate isochrone file string
@@ -1401,16 +1559,16 @@ def isochrone(self, age=1.e8, metallicity=0.0):
iso.rename_column('col4', 'logg')
iso.rename_column('col5', 'logT_WR')
iso.rename_column('col6', 'model_ref')
-
+
iso.meta['log_age'] = log_age
iso.meta['metallicity_in'] = metallicity
iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar)
-
+
return iso
#================================================#
-
-def make_isochrone_pisa_interp(log_age, metallicity=0.015,
+
+def make_isochrone_pisa_interp(log_age, metallicity=0.015,
tracks=None, test=False):
"""
Read in a set of isochrones and generate an isochrone at log_age
@@ -1437,14 +1595,14 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015,
if os.path.exists(rootDir+'iso_{0:3.2f}.fits'.format(log_age)):
print( 'Isochrone at logAge = {0:3.2f} already exists'.format(log_age))
return
-
+
# Name/directory for interpolated isochrone
isoFile = rootDir+'iso_%3.2f.fits' % log_age
outSuffix = '_%.2f' % (log_age)
print( '*** Generating Pisa isochrone for log t = %3.2f and Z = %.3f' % \
(log_age, metallicity))
-
+ import time
print( time.asctime(), 'Getting original Pisa isochrones.')
iso = get_orig_pisa_isochrones(metallicity=metallicity)
@@ -1458,7 +1616,7 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015,
good = np.where(tmp == log_age)
young_model_logage = tmp[good[0]-1]
old_model_logage = tmp[good[0]+1]
-
+
# Isolate younger/older isochrones
young_ind = np.where(iso.log_ages == young_model_logage)
old_ind = np.where(iso.log_ages == old_model_logage)
@@ -1471,7 +1629,7 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015,
if abs(young_model_logage - log_age) <= abs(old_model_logage - log_age):
# Use young model mass grid
young_iso, old_iso = interpolate_iso_tempgrid(young_iso, old_iso)
-
+
else:
# Use old model mass grid
old_iso, young_iso = interpolate_iso_tempgrid(old_iso, young_iso)
@@ -1479,25 +1637,25 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015,
# Now, can interpolate in time over the two models. Do this star by star.
# Work in linear time here!!
numStars = len(young_iso.M)
-
+
interp_iso = Isochrone(log_age)
interp_iso.log_Teff = np.zeros(numStars, dtype=float)
interp_iso.log_L = np.zeros(numStars, dtype=float)
interp_iso.log_g = np.zeros(numStars, dtype=float)
interp_iso.M = young_iso.M # Since mass grids should already be matched
-
+
for i in range(numStars):
# Do interpolations in linear space
model_ages = [10**young_model_logage[0], 10**old_model_logage[0]]
target_age = 10**log_age
#model_ages = [young_model_logage[0], old_model_logage[0]]
#target_age = log_age
-
+
# Build interpolation functions
Teff_arr = [10**young_iso.log_Teff[i], 10**old_iso.log_Teff[i]]
logL_arr = [10**young_iso.log_L[i], 10**old_iso.log_L[i]]
logg_arr = [10**young_iso.log_g[i], 10**old_iso.log_g[i]]
-
+
f_log_Teff = interpolate.interp1d(model_ages, Teff_arr, kind='linear')
f_log_L = interpolate.interp1d(model_ages, logL_arr, kind='linear')
f_log_g = interpolate.interp1d(model_ages, logg_arr, kind='linear')
@@ -1523,15 +1681,15 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015,
py.legend()
py.title('Pisa 2011 Isochrone at log t = %.2f' % log_age)
py.savefig(rootDir + 'plots/interp_isochrone_at' + outSuffix + '.png')
-
+
print( time.asctime(), 'Finished.')
# Write output to file, MUST BE IN SAME ORDER AS ORIG FILES
_out = open(isoFile, 'w')
-
- _out.write('%10s %10s %10s %10s\n' %
+
+ _out.write('%10s %10s %10s %10s\n' %
('# log L', 'log Teff', 'Mass', 'log g'))
- _out.write('%10s %10s %10s %10s\n' %
+ _out.write('%10s %10s %10s %10s\n' %
('# (Lsun)', '(Kelvin)', '(Msun)', '(cgs)'))
for ii in range(len(interp_iso.M)):
@@ -1555,7 +1713,7 @@ def get_orig_pisa_isochrones(metallicity=0.015):
if not os.path.exists(pms_dir):
print( 'Failed to find Siess PMS isochrones for metallicity = ' + metSuffix)
return
-
+
# Collect the isochrones
files = glob.glob(pms_dir + '*.dat')
count = len(files)
@@ -1564,7 +1722,7 @@ def get_orig_pisa_isochrones(metallicity=0.015):
data.isochrones = []
data.log_ages = []
-
+
# Extract useful params from isochrones
for ff in range(len(files)):
d = Table.read(files[ff], format='ascii')
@@ -1572,7 +1730,7 @@ def get_orig_pisa_isochrones(metallicity=0.015):
# Extract logAge from filename
log_age = float(files[ff].split('_')[2][:-4])
- # Create an isochrone object
+ # Create an isochrone object
iso = Isochrone(log_age)
iso.M = d['col3']
iso.log_Teff = d['col2']
@@ -1581,13 +1739,13 @@ def get_orig_pisa_isochrones(metallicity=0.015):
# If a log g column exist, extract it. Otherwise, calculate
# log g from T and L and add column at end
if len(d.keys()) == 3:
-
+
# Calculate log g from T and L
L_sun = 3.8 * 10**33 #cgs
SB_sig = 5.67 * 10**-5 #cgs
M_sun = 2. * 10**33 #cgs
G_const = 6.67 * 10**-8 #cgs
-
+
radius = np.sqrt( (10**d['col1'] * L_sun) /
(4 * np.pi * SB_sig * (10**d['col2'])**4) )
g = (G_const * d['col3'] * M_sun) / radius**2
@@ -1596,7 +1754,7 @@ def get_orig_pisa_isochrones(metallicity=0.015):
iso.log_g = np.log10(g.astype(np.float))
else:
iso.log_g = d['col4']
-
+
data.isochrones.append(iso)
data.log_ages.append(log_age)
diff --git a/spisea/exceptions.py b/spisea/exceptions.py
new file mode 100644
index 00000000..e0587d90
--- /dev/null
+++ b/spisea/exceptions.py
@@ -0,0 +1,24 @@
+import pdb
+
+
+class ModelMismatch(Exception):
+ """
+ Error raised when installed model grid version number is mismatched
+ with the user's version of SPISEA code
+ """
+ def __init__(self, required_num, grid_num, model_type):
+ assert (model_type == 'evolution') | (model_type == 'atmosphere')
+
+ if model_type == 'evolution':
+ model_file = 'spisea_models.tar.gz'
+ elif model_type == 'atmosphere':
+ model_file = 'spisea_cdbs.tar.gz'
+
+ # Compose error message
+ str1 = 'WARNING: Desired {0} model requires model grid version >= {1},'.format(model_type, required_num)
+ str2 = 'but model grid version {0} is installed.'.format(grid_num)
+ str3 = 'Please re-download {0} from installation documentation to resolve mismatch.'.format(model_file)
+ self.message = '{0} {1} {2}'.format(str1, str2, str3)
+
+ # Define error
+ super().__init__(self.message)
diff --git a/spisea/filters.py b/spisea/filters.py
index e13ccd9c..e2a2aee9 100755
--- a/spisea/filters.py
+++ b/spisea/filters.py
@@ -29,7 +29,7 @@ def get_nirc2_filt(name):
while len(idx) != 0:
wavelength[idx+1] += 1.0e-8
-
+
diff = np.diff(wavelength)
idx = np.where(diff <= 0)[0]
#print( 'Duplicate entry loop' )
@@ -68,7 +68,7 @@ def get_2mass_filt(name):
name='2MASS_{0}'.format(name))
return spectrum
-
+
def get_vista_filt(name):
"""
@@ -79,21 +79,21 @@ def get_vista_filt(name):
t = Table.read('{0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name),
format='ascii')
except:
- raise ValueError('Could not find VISTA filter file {0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name))
+ raise ValueError('Could not find VISTA filter file {0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name))
# Wavelength must be in angstroms, transmission in fraction
wave = t['col1'] * 10
trans = t['col2'] * 0.01
-
+
# Change any negative numbers to 0, as well as anything shortward
# of 0.4 microns or longward of 2.9 microns
# (no VISTA filter transmissions beyond these boundaries)
bad = np.where( (trans < 0) | (wave < 4000) | (wave > 29000) )
trans[bad] = 0
-
+
# Now we can define the VISTA filter bandpass objects
spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='VISTA_{0}'.format(name))
-
+
return spectrum
def get_decam_filt(name):
@@ -104,18 +104,18 @@ def get_decam_filt(name):
try:
t = Table.read('{0}/decam/DECam_filters.txt'.format(filters_dir), format='ascii')
t.rename_column('Y', 'y')
-
+
cols = np.array(t.keys())
idx = np.where(cols == name)[0][0]
trans = t[cols[idx]]
except:
- raise ValueError('Could not find DECAM filter {0} in {1}/decam/DECam_filters.txt'.format(name, filters_dir))
+ raise ValueError('Could not find DECAM filter {0} in {1}/decam/DECam_filters.txt'.format(name, filters_dir))
# Limit to unmasked regions only
mask = np.ma.getmask(trans)
good = np.where(mask == False)
-
+
# Convert wavelengths from nm to angstroms, while eliminating masked regions
wave = t['wavelength'][good] * 10.
trans = trans[good]
@@ -126,7 +126,7 @@ def get_decam_filt(name):
return spectrum
-def get_PS1_filt(name):
+def get_PS1_filt(name):
"""
Define PS1 filter as pysynphot object
"""
@@ -145,11 +145,11 @@ def get_PS1_filt(name):
trans = t[cols[idx]]
except:
- raise ValueError('Could not find PS1 filter {0} in {1}/ps1'.format(name, filters_dir))
+ raise ValueError('Could not find PS1 filter {0} in {1}/ps1'.format(name, filters_dir))
# Convert wavelengths from nm to angstroms
wave = t['wave'] * 10.
-
+
spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ps1_{0}'.format(name))
return spectrum
@@ -161,7 +161,7 @@ def get_jwst_filt(name):
try:
t = Table.read('{0}/jwst/{1}.txt'.format(filters_dir, name), format='ascii')
except:
- raise ValueError('Could not find JWST filter {0} in {1}/jwst'.format(name, filters_dir))
+ raise ValueError('Could not find JWST filter {0} in {1}/jwst'.format(name, filters_dir))
# Convert wavelengths to angstroms
wave = t['microns'] * 10**4.
@@ -170,10 +170,10 @@ def get_jwst_filt(name):
# Change any negative numbers to 0
bad = np.where(trans < 0)
trans[bad] = 0
-
+
spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='jwst_{0}'.format(name))
- return spectrum
+ return spectrum
def get_Johnson_Glass_filt(name):
"""
@@ -182,7 +182,7 @@ def get_Johnson_Glass_filt(name):
try:
t = Table.read('{0}/Johnson_Glass/{1}.txt'.format(filters_dir, name), format='ascii')
except:
- raise ValueError('Could not find Johnson-Glass filter {0} in {1}/Johnson_Glass'.format(name, filters_dir))
+ raise ValueError('Could not find Johnson-Glass filter {0} in {1}/Johnson_Glass'.format(name, filters_dir))
# Convert wavelengths to angstroms
wave = t['col1'] * 10.
@@ -191,10 +191,10 @@ def get_Johnson_Glass_filt(name):
# Change any negative numbers to 0
bad = np.where(trans < 0)
trans[bad] = 0
-
+
spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='jg_{0}'.format(name))
- return spectrum
+ return spectrum
def get_nirc1_filt(name):
"""
@@ -203,12 +203,12 @@ def get_nirc1_filt(name):
try:
t = Table.read('{0}/nirc1/{1}.txt'.format(filters_dir, name), format='ascii')
except:
- raise ValueError('Could not find NIRC1 filter {0} in {1}/nirc1'.format(name, filters_dir))
+ raise ValueError('Could not find NIRC1 filter {0} in {1}/nirc1'.format(name, filters_dir))
# Convert wavelengths to angstroms
wave = t['col1'] * 10**4
trans = t['col2']
-
+
# Lets fix wavelength array for duplicate values or negative vals;
# delete these entries
diff = np.diff(wave)
@@ -222,14 +222,14 @@ def get_nirc1_filt(name):
diff = np.diff(wave)
idx = np.where(diff <= 0)[0]
-
+
# Change any negative transmission vals to 0
bad = np.where(trans < 0)
trans[bad] = 0
spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='nirc1_{0}'.format(name))
- return spectrum
+ return spectrum
def get_ctio_osiris_filt(name):
"""
@@ -238,7 +238,7 @@ def get_ctio_osiris_filt(name):
try:
t = Table.read('{0}/CTIO_OSIRIS/{1}.txt'.format(filters_dir, name), format='ascii')
except:
- raise ValueError('Could not find CTIO/OSIRIS filter {0} in {1}/CTIO_OSIRIS'.format(name, filters_dir))
+ raise ValueError('Could not find CTIO/OSIRIS filter {0} in {1}/CTIO_OSIRIS'.format(name, filters_dir))
# Convert wavelengths to angstroms
wave = t['col1'] * 10**4
@@ -247,7 +247,7 @@ def get_ctio_osiris_filt(name):
# Change any negative numbers to 0
bad = np.where(trans < 0)
trans[bad] = 0
-
+
spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ctio_osiris_{0}'.format(name))
return spectrum
@@ -259,7 +259,7 @@ def get_naco_filt(name):
try:
t = Table.read('{0}/naco/{1}.dat'.format(filters_dir, name), format='ascii')
except:
- raise ValueError('Could not find NACO filter {0} in {1}/naco'.format(name, filters_dir))
+ raise ValueError('Could not find NACO filter {0} in {1}/naco'.format(name, filters_dir))
# Convert wavelengths to angstroms
wave = t['col1'] * 10**4
@@ -268,7 +268,7 @@ def get_naco_filt(name):
# Change any negative numbers to 0
bad = np.where(trans < 0)
trans[bad] = 0
-
+
spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='naco_{0}'.format(name))
return spectrum
@@ -282,7 +282,7 @@ def get_ubv_filt(name):
except:
raise ValueError('Could not find ubv filter {0} in {1}/ubv'.format(name, filters_dir))
- # Convert wavelength from nm to angstroms
+ # Convert wavelength from nm to angstroms
wave = t[t.keys()[0]] * 10.
# Convert transmission to ratio (from percent)
trans = t[t.keys()[1]] / 100.
@@ -336,8 +336,8 @@ def get_keck_osiris_filt(name):
def get_gaia_filt(version, name):
"""
Define Gaia filters as pysynphot object.
- To avoid confusion, we will only support
- the revised DR2 zeropoints from
+ To avoid confusion, we will only support
+ the revised DR2 zeropoints from
Evans+18.
version: specify dr1, dr2, or dr2_rev
@@ -357,7 +357,7 @@ def get_gaia_filt(version, name):
path = '{0}/gaia/dr2_rev/'.format(filters_dir)
else:
raise ValueError('GAIA filter version {0} not understood. Please use dr1, dr2, or dr2_rev'.format(version))
-
+
# Get the filter info
try:
t = Table.read('{0}/Gaia_passbands.txt'.format(path), format='ascii')
@@ -380,7 +380,7 @@ def get_gaia_filt(version, name):
bad = np.where(trans > 90)
trans[bad] = 0
except:
- raise ValueError('Could not find Gaia filter {0}'.format(name))
+ raise ValueError('Could not find Gaia filter {0}'.format(name))
# Convert wavelengths to angstroms (from nm)
wave = t['LAMBDA'] * 10
@@ -428,3 +428,65 @@ def get_hawki_filt(name):
name='hawki_{0}'.format(name))
return spectrum
+
+def get_rubin_filt(name):
+ """
+ Define the Rubin Vera C LSST filters as a pysynphot spectrum object
+ """
+ # Read in filter info
+ try:
+ t = Table.read('{0}/rubin/{1}.dat'.format(filters_dir, name), format='ascii')
+ except:
+ raise ValueError('Could not find Rubin LSST filter file {0}/rubin/{1}.dat'.format(filters_dir, name))
+
+ wavelength = t[t.keys()[0]]
+ transmission = t[t.keys()[1]]
+
+ # Convert wavelength to Angstroms
+ wavelength = wavelength * 10
+
+ # Make spectrum object
+ spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom',
+ name='rubin_{0}'.format(name))
+
+ return spectrum
+
+def get_euclid_filt(name):
+ """
+ Define the Euclid filters as a pysynphot spectrum object
+ """
+ # Read in filter info
+ try:
+ t = Table.read('{0}/euclid/{1}.dat'.format(filters_dir, name), format='ascii')
+ except:
+ raise ValueError('Could not find Euclid filter file {0}/euclid/{1}.dat'.format(filters_dir, name))
+
+ wavelength = t[t.keys()[0]]
+ transmission = t[t.keys()[1]]
+
+ # Convert wavelength to Angstroms
+ wavelength = wavelength * 10
+
+ # Make spectrum object
+ spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom',
+ name='euclid_{0}'.format(name))
+
+ return spectrum
+
+def get_nsfcam_filt(name):
+
+ """
+ Define irtf nsfcam filters as pysynphot object
+ """
+ try:
+ t = Table.read('{0}/nsfcam/{1}.dat'.format(filters_dir, name), format='ascii')
+ except:
+ raise ValueError('Could not find nsfcam filter {0} in {1}/nsfcam'.format(name, filters_dir))
+
+ # Wavelength already in angstrom and and transmission in fraction
+ wave = t['col1']
+ trans = t['col2']
+
+ spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='nsfcam_{0}'.format(name))
+
+ return spectrum
diff --git a/spisea/ifmr.py b/spisea/ifmr.py
index b8a03ee0..b7cc7235 100755
--- a/spisea/ifmr.py
+++ b/spisea/ifmr.py
@@ -7,7 +7,17 @@
#IFMR_Spera15 comes from Spera et al. 2015 appendix C and includes metallicity dependence
#https://ui.adsabs.harvard.edu/abs/2015MNRAS.451.4086S/abstract
#
-#Both IFMRs rely on Kalirai et al. 2008 WD IFMR on the low mass end < 9 M_sun for Raitehl18, and < 7 M_sun for Spera15
+#IFMR_N20_Sukhbold combines the follwing
+#BH/NS IFMR based on Sukhbold & Woosley 2014 for zero-Z models:
+#https://ui.adsabs.harvard.edu/abs/2014ApJ...783...10S/abstract
+#BH/NS IFMR based on Sukhbold et al. 2016 for solar-Z models::
+#https://ui.adsabs.harvard.edu/abs/2016ApJ...821...38S/abstract
+#PPISN based on Woosley 2017:
+#https://ui.adsabs.harvard.edu/abs/2017ApJ...836..244W/abstract
+#PPSIN based on Woosley et al. 2020:
+#https://ui.adsabs.harvard.edu/abs/2020ApJ...896...56W/abstract
+#
+#All IFMRs rely on Kalirai et al. 2008 WD IFMR on the low mass end < 9 M_sun for Raitehl18 and N20_Sukhbold, and < 7 M_sun for Spera15
#https://ui.adsabs.harvard.edu/abs/2008ApJ...676..594K/abstract
#
#########################################################
@@ -15,18 +25,20 @@
import numpy as np
class IFMR(object):
- def __init__(self):
- pass
+ def __init__(self, seed=None):
+ self.seed = seed
+ self.rng = np.random.default_rng(seed)
def get_Z(self, Fe_H):
"""
This function converts metallicity given as [Fe/H] into Z values assuming Z_solar = 0.014.
+ Ekstrom+12 https://ui.adsabs.harvard.edu/abs/2012A%26A...537A.146E/abstract
"""
return 10**(Fe_H - 1.85387)
def Kalirai_mass(self, MZAMS):
- """
+ """
From Kalirai+08 https://ui.adsabs.harvard.edu/abs/2008ApJ...676..594K/abstract
1.16 < MZAMS < 6.5
But we use this function for anything between 0.5 and 9 depending on the IFMR.
@@ -36,35 +48,34 @@ def Kalirai_mass(self, MZAMS):
result = 0.109*MZAMS + 0.394
final = np.zeros(len(MZAMS))
-
- bad_idx = np.where(MZAMS < 0.5)
- final[bad_idx] = -99
-
- good_idx = np.where(MZAMS >= 0.5)
+
+ good_idx = MZAMS >= 0.5
final[good_idx] = result[good_idx]
+ final[~good_idx] = -99
return final
-
-
+
+
class IFMR_Spera15(IFMR):
"""
- This IFMR comes from Spera et. al. 2015 Appendix C (Used for all MZAMS>= 7 M_sun)
- https://ui.adsabs.harvard.edu/abs/2015MNRAS.451.4086S/abstract
+ The BH/NS IFMR (used for MZAMS>= 7 M_sun) comes from
+ `Spera et. al. (2015) Appendix C `_.
+ The WD IFMR (used for MZAMS< 7_Msun) comes from
+ `Kalirai et al. (2008) `_.
+
+ See Rose et al. (submitted) for more details.
- The WD IFMR (used for MZAMS< 7_Msun) comes from
- `Kalirai et al. (2008) `_
-
"""
-
+
#The get_Mco functions come from C11 of Spera
def get_Mco_low_metal(self, Z, MZAMS):
"""
C15 of Spera, valid for Z < 1.0e-3
-
+
"""
-
+
B1 = 67.07
K1 = 46.89
K2 = 1.138e2
@@ -83,7 +94,7 @@ def get_Mco_med_metal(self, Z, MZAMS):
C14 of Spera, valid for Z >= 1.0e-3 and Z <= 4.0e-3
"""
-
+
B1 = 40.98 + 3.415e4*Z - 8.064e6*Z**2
K1 = 35.17 + 1.548e4*Z - 3.759e6*Z**2
K2 = 20.36 + 1.162e5*Z - 2.276e7*Z**2
@@ -102,7 +113,7 @@ def get_Mco_high_metal(self, Z, MZAMS):
C13 of Spera, valid for Z > 4.0e-3
"""
-
+
B1 = 59.63 - 2.969e3*Z + 4.988e4*Z**2
K1 = 45.04 - 2.176e3*Z + 3.806e4*Z**2
K2 = 1.389e2 - 4.664e3*Z + 5.106e4*Z**2
@@ -112,7 +123,7 @@ def get_Mco_high_metal(self, Z, MZAMS):
g1 = 0.5/(1+10**((K1-MZAMS)*(d1))) #C12 of Spera
g2 = 0.5/(1+10**((K2-MZAMS)*(d2))) #C12 of Spera
-
+
return -2.0 + (B1 + 2.0)*(g1 + g2) #C11 of Spera
@@ -121,8 +132,8 @@ def get_Mco(self, Z, MZAMS):
This function uses Spera C11-C15 in order to reurn an array of core masses from an array of metallicities
and ZAMS masses. It will be the same length as these two arrays with -99 entries where invalid (ie MZAMS< 7 M_sun)
- Parameters:
-
+ Parameters:
+
Z: an array with metallicities reported as Z where Z is metal_mass/total_mass
MZAMS: an array of ZAMS masses in solar masses. The Spera functions are valid for MZAMS> 7 M_sun
@@ -147,28 +158,28 @@ def get_Mco(self, Z, MZAMS):
core_masses[high_metal_idx] = self.get_Mco_high_metal(Z[high_metal_idx], MZAMS[high_metal_idx])
return core_masses
-
+
def M_rem_very_low_metal_low_mass(self, Z, Mco):
"""
C1 of Spera, valid for Z <= 5.0e-4 and Mco <= 5.0
-
+
Parameters:
Z: an array of metallicities reported as metal_mass/total_mass
Mco: an arrray of core masses in M_sun
"""
-
+
p = -2.333 + 0.1559*Mco + 0.2700*Mco**2 #C2 of Spera
#need to return p or 1.27, whichever is greater
final = np.zeros(len(p))
-
+
p_max_idx = np.where(p >= 1.27)
final[p_max_idx] = p[p_max_idx]
-
+
p_min_idx = np.where(p < 1.27)
final[p_min_idx] = 1.27
@@ -206,7 +217,7 @@ def M_rem_very_low_metal_high_mass(self, Z, Mco):
m = -6.476e2*Z + 1.911 #C3 of Spera
q = 2.300e3*Z + 11.67 #C3 of Spera
-
+
f = m*Mco + q #C2 of Spera
#need to return either p or f, whichever is less
@@ -217,7 +228,7 @@ def M_rem_very_low_metal_high_mass(self, Z, Mco):
f_min_idx = np.where(f < p)
final[f_min_idx] = f[f_min_idx]
-
+
return final
def M_rem_low_metal_low_mass(self, Z, Mco):
@@ -241,15 +252,15 @@ def M_rem_low_metal_low_mass(self, Z, Mco):
#need to return h or 1.27, whichever is greater
final = np.zeros(len(h))
-
+
h_max_idx = np.where(h >= 1.27)
final[h_max_idx] = h[h_max_idx]
-
+
h_min_idx = np.where(h < 1.27)
final[h_min_idx] = 1.27
return final
-
+
def M_rem_low_metal_med_mass(self, Z, Mco):
"""
@@ -305,9 +316,9 @@ def M_rem_low_metal_high_mass(self, Z, Mco):
f_max_idx = np.where(f > h)
final[f_max_idx] = f[f_max_idx]
-
+
return final
-
+
def M_rem_med_metal_low_mass(self, Z, Mco):
"""
@@ -330,10 +341,10 @@ def M_rem_med_metal_low_mass(self, Z, Mco):
#need to return h or 1.27, whichever is greater
final = np.zeros(len(h))
-
+
h_max_idx = np.where(h >= 1.27)
final[h_max_idx] = h[h_max_idx]
-
+
h_min_idx = np.where(h < 1.27)
final[h_min_idx] = 1.27
@@ -384,7 +395,7 @@ def M_rem_med_metal_high_mass_1(self, Z, Mco):
q = -1.296e4*Z + 26.98
f = m*Mco + q #C5 of Spera
-
+
#need to return either h or f, whichever is greater
final = np.zeros(len(h))
@@ -393,7 +404,7 @@ def M_rem_med_metal_high_mass_1(self, Z, Mco):
f_max_idx = np.where(f > h)
final[f_max_idx] = f[f_max_idx]
-
+
return final
def M_rem_med_metal_high_mass_2(self, Z, Mco):
@@ -420,7 +431,7 @@ def M_rem_med_metal_high_mass_2(self, Z, Mco):
q = 1.061
f = m*Mco + q #C5 of Spera
-
+
#need to return either h or f, whichever is greater
final = np.zeros(len(h))
@@ -429,7 +440,7 @@ def M_rem_med_metal_high_mass_2(self, Z, Mco):
f_max_idx = np.where(f > h)
final[f_max_idx] = f[f_max_idx]
-
+
return final
@@ -454,10 +465,10 @@ def M_rem_high_metal_low_mass(self, Z, Mco):
#need to return h or 1.27, whichever is greater
final = np.zeros(len(h))
-
+
h_max_idx = np.where(h >= 1.27)
final[h_max_idx] = h[h_max_idx]
-
+
h_min_idx = np.where(h < 1.27)
final[h_min_idx] = 1.27
@@ -508,7 +519,7 @@ def M_rem_high_metal_high_mass(self, Z, Mco):
q = 1.061
f = m*Mco + q #C5 of Spera
-
+
#need to return either h or f, whichever is greater
final = np.zeros(len(h))
@@ -517,15 +528,15 @@ def M_rem_high_metal_high_mass(self, Z, Mco):
f_max_idx = np.where(f > h)
final[f_max_idx] = f[f_max_idx]
-
+
return final
def generate_death_mass(self, mass_array, metallicity_array):
"""
- The top-level function that assigns the remnant type
- and mass based on the stellar initial mass.
-
+ The top-level function that assigns the remnant type
+ and mass based on the stellar initial mass.
+
Parameters
----------
mass_array: array of floats
@@ -536,20 +547,20 @@ def generate_death_mass(self, mass_array, metallicity_array):
Notes
------
The output typecode tells what compact object formed:
-
+
* WD: typecode = 101
* NS: typecode = 102
* BH: typecode = 103
- A typecode of value -1 means you're outside the range of
- validity for applying the ifmr formula.
- A remnant mass of -99 means you're outside the range of
+ A typecode of value -1 means you're outside the range of
validity for applying the ifmr formula.
- Range of validity: MZAMS > 0.5
-
+ A remnant mass of -99 means you're outside the range of
+ validity for applying the ifmr formula.
+ Range of validity: MZAMS > 0.5
+
Returns
-------
output_arr: 2-element array
- output_array[0] contains the remnant mass, and
+ output_array[0] contains the remnant mass, and
output_array[1] contains the typecode
"""
@@ -574,10 +585,10 @@ def generate_death_mass(self, mass_array, metallicity_array):
Kal_idx = np.where(core_mass < 0)
rem_mass_array[Kal_idx] = self.Kalirai_mass(mass_array[Kal_idx])
-
+
##### very low metallicity Z < 5.0e-4
-
+
#remnant masses of stars with Z < 5.0e-4 and Mco < 5.0
very_low_metal_low_mass_idx = np.where((Z_array < 5.0e-4) & (core_mass < 5.0) & (core_mass >= 0))
rem_mass_array[very_low_metal_low_mass_idx] = self.M_rem_very_low_metal_low_mass(Z_array[very_low_metal_low_mass_idx], core_mass[very_low_metal_low_mass_idx])
@@ -591,7 +602,7 @@ def generate_death_mass(self, mass_array, metallicity_array):
rem_mass_array[very_low_metal_high_mass_idx] = self.M_rem_very_low_metal_high_mass(Z_array[very_low_metal_high_mass_idx], core_mass[very_low_metal_high_mass_idx])
#### low metallicity 5.0e-4 <= Z < 1.0e-3
-
+
#remnant masses of stars with 5.0e-4 <= Z < 1.0e-3 and Mco < 5.0
low_metal_low_mass_idx = np.where((Z_array >= 5.0e-4) & (Z_array < 1.0e-3) & (core_mass < 5.0) & (core_mass >= 0))
rem_mass_array[low_metal_low_mass_idx] = self.M_rem_low_metal_low_mass(Z_array[low_metal_low_mass_idx], core_mass[low_metal_low_mass_idx])
@@ -603,9 +614,9 @@ def generate_death_mass(self, mass_array, metallicity_array):
#remnant masses of stars with 5.0e-4 <= Z < 1.0e-3 and Mco > 10.0
low_metal_high_mass_idx = np.where((Z_array >= 5.0e-4) & (Z_array < 1.0e-3) & (core_mass > 10.0))
rem_mass_array[low_metal_high_mass_idx] = self.M_rem_low_metal_high_mass(Z_array[low_metal_high_mass_idx], core_mass[low_metal_high_mass_idx])
-
+
#### medium metallicity 1.0e-3 <= Z <= 4.0e-3
-
+
#remnant masses of stars with 1.0e-3 <= Z <= 4.0e-3 and Mco < 5.0
med_metal_low_mass_idx = np.where((Z_array >= 1.0e-3) & (Z_array <= 4.0e-3) & (core_mass < 5.0) & (core_mass >= 0))
rem_mass_array[med_metal_low_mass_idx] = self.M_rem_med_metal_low_mass(Z_array[med_metal_low_mass_idx],core_mass[med_metal_low_mass_idx])
@@ -621,9 +632,9 @@ def generate_death_mass(self, mass_array, metallicity_array):
#remnant masses of stars with 2.0e-3 <= Z <= 4.0e-3 and Mco > 10.0
med_metal_high_mass_idx_2 = np.where((Z_array >= 2.0e-3) & (Z_array <= 4.0e-3) & (core_mass > 10.0))
rem_mass_array[med_metal_high_mass_idx_2] = self.M_rem_med_metal_high_mass_2(Z_array[med_metal_high_mass_idx_2], core_mass[med_metal_high_mass_idx_2])
-
+
#### high metallicity Z > 4.0e-3
-
+
#remnant masses of stars with Z > 4.0e-3 and Mco < 5.0
high_metal_low_mass_idx = np.where((Z_array > 4.0e-3) & (core_mass < 5.0) & (core_mass >= 0))
rem_mass_array[high_metal_low_mass_idx] = self.M_rem_high_metal_low_mass(Z_array[high_metal_low_mass_idx], core_mass[high_metal_low_mass_idx])
@@ -635,16 +646,16 @@ def generate_death_mass(self, mass_array, metallicity_array):
#remnant masses of stars with Z > 4.0e-3 and MZAMS > 10.0
high_metal_high_mass_idx = np.where((Z_array > 4.0e-3) & (core_mass > 10.0))
rem_mass_array[high_metal_high_mass_idx] = self.M_rem_high_metal_high_mass(Z_array[high_metal_high_mass_idx], core_mass[high_metal_high_mass_idx])
-
+
#assign object types based on remnant mass
bad_idx = np.where(rem_mass_array < 0) #outside the range of validity for the ifmr
WD_idx = np.where((rem_mass_array <= 1.4) & (rem_mass_array >= 0 )) #based on the Chandresekhar limit
- NS_idx = np.where((rem_mass_array > 1.4) & (rem_mass_array <= 3.0)) #based on figures 15-17 of Spera
+ NS_idx = np.where((rem_mass_array > 1.4) & (rem_mass_array <= 3.0)) #based on figures 15-17 of Spera
BH_idx = np.where(rem_mass_array > 3.0) #based on figures 15-17 of Spera
output_array[0][bad_idx] = rem_mass_array[bad_idx]
output_array[1][bad_idx] = -1
-
+
output_array[0][WD_idx] = rem_mass_array[WD_idx]
output_array[1][WD_idx] = codes['WD']
@@ -655,74 +666,76 @@ def generate_death_mass(self, mass_array, metallicity_array):
output_array[1][BH_idx] = codes['BH']
return output_array
-
+
class IFMR_Raithel18(IFMR):
"""
- This IFMR comes from Raithel et al. 2018
- https://ui.adsabs.harvard.edu/abs/2018ApJ...856...35R/abstract
- The IFMR is a combination of the
- WD IFMR from
+ The IFMR is a combination of the WD IFMR from
`Kalirai et al. (2008) `_
and the NS/BH IFMR from
`Raithel et al. (2018) `_.
+ Note that the NS masses are NOT assigned based on the above results.
+ We do take the NS/BH formation ratio and the BH masses.
+ NS masses are assigned based on random draws from a Gaussian (see NS_mass function).
- See Lam et al. (submitted) for more details.
+ See
+ `Lam et al. (2020) `_
+ and Rose et al. (submitted) for more details.
"""
def BH_mass_core_low(self, MZAMS):
- """
- Eqn (1)
- Paper: 15 < MZAMS < 40
- Us extending: 15 < MZAMS < 42.22
+ """
+ Eqn (1)
+ Paper: 15 < MZAMS < 40
+ Us extending: 15 < MZAMS < 42.22
"""
return -2.024 + 0.4130*MZAMS
def BH_mass_all_low(self, MZAMS):
- """
- Eqn (2)
- Paper: 15 < MZAMS < 40
- Us extending: 15 < MZAMS < 42.22
+ """
+ Eqn (2)
+ Paper: 15 < MZAMS < 40
+ Us extending: 15 < MZAMS < 42.22
"""
return 16.28 + 0.00694 * (MZAMS - 21.872) - 0.05973 * (MZAMS - 21.872)**2 + 0.003112 * (MZAMS - 21.872)**3
def BH_mass_high(self, MZAMS):
- """
- Eqn (3)
- Paper: 45 < MZAMS < 120
- Us extending: 42.22 < MZAMS < 120
+ """
+ Eqn (3)
+ Paper: 45 < MZAMS < 120
+ Us extending: 42.22 < MZAMS < 120
"""
return 5.795 + 1.007 * 10**9 * MZAMS**-4.926
def BH_mass_low(self, MZAMS, f_ej):
- """
- Eqn (4)
- Paper: 15 < MZAMS < 40
- Us extending: 15 < MZAMS < 42.22
+ """
+ Eqn (4)
+ Paper: 15 < MZAMS < 40
+ Us extending: 15 < MZAMS < 42.22
"""
return f_ej * self.BH_mass_core_low(MZAMS) + (1 - f_ej) * self.BH_mass_all_low(MZAMS)
def NS_mass(self, MZAMS):
- """
- Paper: 9 < MZAMS 120
- Drawing the mass from gaussian created using observational data
- Done by Emily Ramey and Sergiy Vasylyev at University of Caifornia, Berkeley
- sample oF NS from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020),
- J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018),
- J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017),
+ """
+ Drawing the NS mass from a Gaussian distrobuton based on observational data.
+
+ Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of Caifornia, Berkeley using a
+ sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020),
+ J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018),
+ J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017),
J1757+1854 Cameron et al. (2018), J0030+0451 Riley et al. (2019), J1301+0833 Romani et al. (2016)
- The gaussian distribution was created using this data and a Bayesian MCMC method adapted from
- Kiziltan et al. (2010)
-
+ The Gaussian distribution was fit using this data and a Bayesian MCMC method adapted from
+ Kiziltan et al. (2010).
+
"""
- return np.random.normal(loc=1.36, scale=0.09, size=len(MZAMS))
+ return self.rng.normal(loc=1.36, scale=0.09, size=len(MZAMS))
def generate_death_mass(self, mass_array):
"""
- The top-level function that assigns the remnant type
- and mass based on the stellar initial mass.
-
+ The top-level function that assigns the remnant type
+ and mass based on the stellar initial mass.
+
Parameters
----------
mass_array: array of floats
@@ -733,15 +746,15 @@ def generate_death_mass(self, mass_array):
Notes
------
The output typecode tells what compact object formed:
-
+
* WD: typecode = 101
* NS: typecode = 102
* BH: typecode = 103
- A typecode of value -1 means you're outside the range of
- validity for applying the ifmr formula.
+ A typecode of value -1 means you're outside the range of
+ validity for applying the ifmr formula.
- A remnant mass of -99 means you're outside the range of
+ A remnant mass of -99 means you're outside the range of
validity for applying the ifmr formula.
Range of validity: 0.5 < MZAMS < 120
@@ -749,7 +762,7 @@ def generate_death_mass(self, mass_array):
Returns
-------
output_arr: 2-element array
- output_array[0] contains the remnant mass, and
+ output_array[0] contains the remnant mass, and
output_array[1] contains the typecode
"""
@@ -758,10 +771,10 @@ def generate_death_mass(self, mass_array):
output_array = np.zeros((2, len(mass_array)))
#Random array to get probabilities for what type of object will form
- random_array = np.random.randint(1, 1001, size = len(mass_array))
+ random_array = self.rng.integers(1, 1001, size = len(mass_array))
codes = {'WD': 101, 'NS': 102, 'BH': 103}
-
+
"""
The id_arrays are to separate all the different formation regimes
"""
@@ -788,7 +801,7 @@ def generate_death_mass(self, mass_array):
id_array4_BH = np.where((mass_array >= 17.8) & (mass_array < 18.5) & (random_array > 833))
output_array[0][id_array4_BH]= self.BH_mass_low(mass_array[id_array4_BH], 0.9)
output_array[1][id_array4_BH] = codes['BH']
-
+
id_array4_NS = np.where((mass_array >= 17.8) & (mass_array < 18.5) & (random_array <= 833))
output_array[0][id_array4_NS] = self.NS_mass(mass_array[id_array4_NS])
output_array[1][id_array4_NS] = codes['NS']
@@ -796,7 +809,7 @@ def generate_death_mass(self, mass_array):
id_array5_BH = np.where((mass_array >= 18.5) & (mass_array < 21.7) & (random_array > 500))
output_array[0][id_array5_BH] = self.BH_mass_low(mass_array[id_array5_BH], 0.9)
output_array[1][id_array5_BH] = codes['BH']
-
+
id_array5_NS = np.where((mass_array >= 18.5) & (mass_array < 21.7) & (random_array <= 500))
output_array[0][id_array5_NS] = self.NS_mass(mass_array[id_array5_NS])
output_array[1][id_array5_NS] = codes['NS']
@@ -808,7 +821,7 @@ def generate_death_mass(self, mass_array):
id_array7_BH = np.where((mass_array >= 25.2) & (mass_array < 27.5) & (random_array > 652))
output_array[0][id_array7_BH] = self.BH_mass_low(mass_array[id_array7_BH], 0.9)
output_array[1][id_array7_BH] = codes['BH']
-
+
id_array7_NS = np.where((mass_array >= 25.2) & (mass_array < 27.5) & (random_array <= 652))
output_array[0][id_array7_NS] = self.NS_mass(mass_array[id_array7_NS])
output_array[1][id_array7_NS] = codes['NS']
@@ -824,9 +837,228 @@ def generate_death_mass(self, mass_array):
id_array10_BH = np.where((mass_array >= 60) & (mass_array < 120) & (random_array > 400))
output_array[0][id_array10_BH] = self.BH_mass_high(mass_array[id_array10_BH])
output_array[1][id_array10_BH] = codes['BH']
-
+
id_array10_NS = np.where((mass_array >= 60) & (mass_array < 120) & (random_array <= 400))
output_array[0][id_array10_NS] = self.NS_mass(mass_array[id_array10_NS])
output_array[1][id_array10_NS] = codes['NS']
return(output_array)
+
+class IFMR_N20_Sukhbold(IFMR):
+ """
+ The BH/NS IFMR for zero metallicity progenitors comes from
+ `Sukhbold & Woosley (2014) `_.
+ The BH/NS IFMR for solar metallicity progenitors comes from
+ `Sukhbold et al. (2016) `_.
+ The PPISN models are from
+ `Woosley (2017) `_
+ and
+ `Woosley et al. (2020) `_.
+ The WD IFMR is from
+ `Kalirai et al. (2008) `_.
+ Note that the NS masses are NOT assigned based on the above results.
+ We do take the NS/BH formation ratio and the BH masses.
+ NS masses are assigned based on random draws from a Gaussian (see NS_mass function).
+
+ See Rose et al. (submitted) for more details.
+ """
+ # Linear fits to Sukhbold simulations.
+
+ def zero_BH_mass(self, MZAMS):
+ #zero_coeff = [0.46522639, -3.29170817]
+ #func = np.poly1d(zero_coeff)
+ #result = func(MZAMS)
+ return 0.46522639*MZAMS + -3.29170817
+
+ def solar_BH_mass(self, MZAMS):
+ #solar_coeff = [-0.27079245, 24.74320755]
+ #func = np.poly1d(solar_coeff)
+ return -0.27079245*MZAMS + 24.74320755
+
+ # Solar metallicity (what Sam is using)
+ Zsun = 0.014
+
+ def NS_mass(self, MZAMS):
+ """
+ Drawing the NS mass from a Gaussian distrobuton based on observational data.
+
+ Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of Caifornia, Berkeley using a
+ sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020),
+ J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018),
+ J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017),
+ J1757+1854 Cameron et al. (2018), J0030+0451 Riley et al. (2019), J1301+0833 Romani et al. (2016)
+ The Gaussian distribution was fit using this data and a Bayesian MCMC method adapted from
+ Kiziltan et al. (2010).
+
+ """
+ if isinstance(MZAMS, np.ndarray):
+ return self.rng.normal(loc=1.36, scale=0.09, size=len(MZAMS))
+ else:
+ return self.rng.normal(loc=1.36, scale=0.09, size=1)[0]
+
+
+ def BH_mass_low(self, MZAMS):
+ """
+ 9 < MZAMS < 40 Msun
+ """
+ mBH = self.zero_BH_mass(MZAMS)
+
+ return mBH
+
+
+ def BH_mass_high(self, MZAMS, Z):
+ """
+ 39.6 Msun < MZAMS < 120 Msun
+ """
+ # Solar metallicity (what Sam is using)
+ Zsun = 0.014
+
+ zfrac = np.atleast_1d(Z/Zsun)
+ # super-solar Z gives identical results as solar Z
+ above_idx = np.where(zfrac > 1)
+ if len(above_idx) > 1:
+ zfrac[above_idx] = 1.0
+
+ bad_idx = np.where(zfrac < 0)
+ if len(bad_idx) > 1:
+ raise ValueError('Z must be non-negative')
+
+ # Linearly interpolate
+ mBH = (1 - zfrac) * self.zero_BH_mass(MZAMS) + zfrac*self.solar_BH_mass(MZAMS)
+
+ return mBH
+
+
+ def prob_BH_high(self, Z):
+ """
+ Probability of BH formation for 60 < Mzams < 120 Msun
+ """
+ # Solar metallicity (what Sam is using)
+ Zsun = 0.014
+
+ Z = np.atleast_1d(Z)
+ # Convert from [Fe/H] to Z
+ zfrac = Z / Zsun
+
+ # super-solar Z gives identical results as solar Z
+ zfrac[zfrac > 1] = 1.0
+ zfrac[zfrac < 0] = np.nan
+
+ pBH = 1 - 0.8*zfrac
+
+ return pBH
+
+
+ def generate_death_mass(self, mass_array, metallicity_array):
+ """
+ The top-level function that assigns the remnant type
+ and mass based on the stellar initial mass.
+
+ Parameters
+ ----------
+ mass_array: array of floats
+ Array of initial stellar masses. Units are
+ M_sun.
+ metallicity_array: array of floats
+ Array of metallicities in terms of [Fe/H]
+ Notes
+ ------
+ The output typecode tells what compact object formed:
+
+ * WD: typecode = 101
+ * NS: typecode = 102
+ * BH: typecode = 103
+ A typecode of value -1 means you're outside the range of
+ validity for applying the ifmr formula.
+ A remnant mass of -99 means you're outside the range of
+ validity for applying the ifmr formula.
+ Range of validity: MZAMS > 0.5
+
+ Returns
+ -------
+ output_arr: 2-element array
+ output_array[0] contains the remnant mass, and
+ output_array[1] contains the typecode
+ """
+ #output_array[0] holds the remnant mass
+ #output_array[1] holds the remnant type
+ mass_array = np.atleast_1d(mass_array)
+ metallicity_array = np.atleast_1d(metallicity_array)
+
+ output_array = np.zeros((2, len(mass_array)))
+
+ codes = {'WD': 101, 'NS': 102, 'BH': 103}
+
+
+ # Array to store the remnant masses
+ # rem_mass_array = np.zeros(len(mass_array))
+
+ # Convert from [Fe/H] to Z
+ # FIXME: if have Fe/H = nan that makes Z = 0. Is that the behavior we want?
+ Z_array = np.zeros((len(metallicity_array)))
+ metal_idx = ~np.isnan(metallicity_array)
+ Z_array[metal_idx] = self.get_Z(metallicity_array[metal_idx])
+
+ # Random array to get probabilities for what type of object will form
+ random_array = self.rng.integers(1, 101, size=len(mass_array))
+
+ id_array0 = (mass_array < 0.5) | (mass_array >= 120)
+ output_array[0][id_array0] = -99
+ output_array[1][id_array0] = -1
+
+ id_array1 = (mass_array >= 0.5) & (mass_array < 9)
+ output_array[0][id_array1] = self.Kalirai_mass(mass_array[id_array1])
+ output_array[1][id_array1]= codes['WD']
+
+ id_array2 = (mass_array >= 9) & (mass_array < 15)
+ output_array[0][id_array2] = self.NS_mass(mass_array[id_array2])
+ output_array[1][id_array2] = codes['NS']
+
+ id_array3_BH = (mass_array >= 15) & (mass_array < 21.8) & (random_array > 75)
+ output_array[0][id_array3_BH] = self.BH_mass_low(mass_array[id_array3_BH])
+ output_array[1][id_array3_BH] = codes['BH']
+
+ id_array3_NS = (mass_array >= 15) & (mass_array < 21.8) & (random_array <= 75)
+ output_array[0][id_array3_NS] = self.NS_mass(mass_array[id_array3_NS])
+ output_array[1][id_array3_NS] = codes['NS']
+
+ id_array4 = (mass_array >= 21.8) & (mass_array < 25.2)
+ output_array[0][id_array4] = self.BH_mass_low(mass_array[id_array4])
+ output_array[1][id_array4] = codes['BH']
+
+ id_array5 = (mass_array >= 25.2) & (mass_array < 27.4)
+ output_array[0][id_array5] = self.NS_mass(mass_array[id_array5])
+ output_array[1][id_array5] = codes['NS']
+
+ id_array6 = (mass_array >= 27.4) & (mass_array < 39.6)
+ output_array[0][id_array6] = self.BH_mass_low(mass_array[id_array6])
+ output_array[1][id_array6] = codes['BH']
+
+ id_array7 = (mass_array >= 39.6) & (mass_array < 60)
+ output_array[0][id_array7] = self.BH_mass_high(mass_array[id_array7],
+ Z_array[id_array7])
+ output_array[1][id_array7] = codes['BH']
+
+ BH_or_NS = np.where((mass_array >= 60) & (mass_array < 120))[0]
+ pBH = self.prob_BH_high(Z_array[BH_or_NS])
+ is_BH = random_array[BH_or_NS] > 100 * pBH
+
+ id_array8 = BH_or_NS[is_BH]
+ id_array9 = BH_or_NS[~is_BH]
+
+ # Assign BH masses and types for BH-forming indices
+ output_array[0][id_array8] = self.BH_mass_high(mass_array[id_array8], Z_array[id_array8])
+ output_array[1][id_array8] = codes['BH']
+
+ # Assign NS masses and types for NS-forming indices
+ output_array[0][id_array9] = self.NS_mass(mass_array[id_array9])
+ output_array[1][id_array9] = codes['NS']
+
+ #this is where sam's janky fix for unphysical BH massses goes
+ #any BH with mass less then 3 M_sun is reassigned as a NS
+ #and given a mass from the NS mass dist instead
+ id_array10 = (output_array[1] == codes['BH']) & (output_array[0] < 3.0)
+ output_array[0][id_array10] = self.NS_mass(mass_array[id_array10])
+ output_array[1][id_array10] = codes['NS']
+
+ return output_array
diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py
index b5bb7572..df6a8887 100755
--- a/spisea/imf/imf.py
+++ b/spisea/imf/imf.py
@@ -4,7 +4,7 @@
# Original code was taken from libimf package written by Jan Pflamm-Altenburg
# and has been modified only marginally. The libimf code was licensed under
# a GNU General Public License.
-#
+#
# When I use this code, I should cite Pflamm-Altenburg & Kroupa 2006
#
# Unfortunately, the code was almost completely un-commented, so all
@@ -22,47 +22,82 @@
class IMF(object):
"""
- The IMF base class. The mass sampling and multiplicity
- implementation is here.
-
- Notes
- -----
- Code author: J. Lu.
-
- Original code was taken from libimf package written by Jan Pflamm-Altenburg
- (`Pflamm-Altenburg & Kroupa 2006 `_)
- and has been modified only marginally, though more convinient and general purpose
- functions have been added. The libimf code was licensed under
- a GNU General Public License.
+ The IMF base class. The mass sampling and multiplicity
+ implementation is here.
Parameters
----------
massLimits : 2 element array; optional
- Define the minimum and maximum stellar masses in the IMF, in
- solar masses. First element is taken as the min, second element
+ Define the minimum and maximum stellar masses in the IMF, in
+ solar masses. First element is taken as the min, second element
the max (e.g. `massLimits` = [min_mass, max_mass]).
multiplicity : Multiplicity object or None
- If None, no multiplicity is assumed. Otherwise, use
+ If None, no multiplicity is assumed. Otherwise, use
multiplicity object to create multiple star systems.
+
+ seed : int, optional
+ Seed for the random generator numpy.random.default_rng(seed).
+ All random functions in the class will use this generator, by default None.
+ Behavior:
+ ::
+
+ imf = IMF(..., seed=42)
+ result1 = imf.generate_cluster()
+ result2 = imf.generate_cluster()
+ imf = IMF(..., seed=42)
+ result3 = imf.generate_cluster()
+ result4 = imf.generate_cluster()
+
+ result1==result3, result2==result4, but result1≠result2, result3≠result4.
+ This is the same behavior as
+ ::
+
+ rng = np.random.default_rng(seed=42)
+ result1 = rng.random(1)
+ result2 = rng.random(1)
+ rng = np.random.default_rng(seed=42)
+ result3 = rng.random(1)
+ result4 = rng.random(1)
+
+ If identical output is desired over each run, the random state can be reset before running the function, e.g.
+ ::
+
+ imf.rng = np.random.default_rng(seed=42)
+ result1 = imf.generate_cluster()
+ imf.rng = np.random.default_rng(seed=42)
+ result2 = imf.generate_cluster()
+
+ In this case, result1==result2
+
+ Notes
+ -----
+ Code author: J. Lu.
+
+ Original code was taken from libimf package written by Jan Pflamm-Altenburg
+ (`Pflamm-Altenburg & Kroupa 2006 `_)
+ and has been modified only marginally, though more convinient and general purpose
+ functions have been added. The libimf code was licensed under
+ a GNU General Public License.
+
"""
- def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None):
+ def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None):
self._multi_props = multiplicity
- self._mass_limits = massLimits
+ self._mass_limits = np.atleast_1d(massLimits)
+ self.rng = np.random.default_rng(seed)
- if multiplicity == None:
- self.make_multiples = False
- else:
+ if multiplicity:
self.make_multiples = True
-
+ else:
+ self.make_multiples = False
+
return
-
- def generate_cluster(self, totalMass, seed=None):
+ def generate_cluster(self, totalMass):
"""
Generate a cluster of stellar systems with the specified IMF.
-
+
Randomly sample from an IMF with specified mass
limits until the desired total mass is reached. The maximum
stellar mass is not allowed to exceed the total cluster mass.
@@ -79,24 +114,23 @@ def generate_cluster(self, totalMass, seed=None):
totalMass : float
The total mass of the cluster (including companions) in solar masses.
- seed: int
- If set to non-None, all random sampling will be seeded with the
- specified seed, forcing identical output.
- Default None
-
Returns
-------
masses : numpy float array
- List of primary star masses.
+ Array of primary star masses.
isMultiple : numpy boolean array
- List of booleans with True for each primary star that is in a multiple
+ Array of booleans with True for each primary star that is in a multiple
system and False for each single star.
- companionMasses : numpy float array
- List of
-
+ companionMasses : numpy masked array
+ Masked array of companion masses. Each row corresponds to a primary star, and each column corresponds to a companion. The mask is True for entries that are not valid companions (e.g. for single stars or for companions that are below the minimum mass limit).
+
+ systemMasses : numpy float array
+ Array of total system masses (primary + companions) for each primary star.
+
"""
+ initial_mass_limit = self._mass_limits[-1]
if (self._mass_limits[-1] > totalMass):
log.info('sample_imf: Setting maximum allowed mass to %d' %
@@ -113,7 +147,8 @@ def generate_cluster(self, totalMass, seed=None):
# Generate output arrays.
masses = np.array([], dtype=float)
isMultiple = np.array([], dtype=bool)
- compMasses = np.array([], dtype=np.object)
+ # compMasses = {} # Hashmap for index -> compMasses for faster lookup
+ compMasses = []
systemMasses = np.array([], dtype=float)
# Loop through and add stars to the cluster until we get to
@@ -121,51 +156,45 @@ def generate_cluster(self, totalMass, seed=None):
totalMassTally = 0
loopCnt = 0
- # Set the random seed, if desired
- if seed:
- np.random.seed(seed=seed)
-
+ # start_while = time.time()
while totalMassTally < totalMass:
# Generate a random number array.
- uniX = np.random.rand(int(newStarCount))
-
+ uniX = self.rng.random(int(newStarCount))
# Convert into the IMF from the inverted CDF
newMasses = self.dice_star_cl(uniX)
-
+
# Testing for Nans produced in masses
test = np.isnan(newMasses)
if np.sum(test) > 0:
raise ValueError('Nan detected in cluster mass')
-
+
# Dealing with multiplicity
- if self._multi_props != None:
- newCompMasses = np.empty((len(newMasses),), dtype=np.object)
- newCompMasses.fill([])
-
+ if self._multi_props:
+ # newCompMasses = np.empty((len(newMasses),), dtype=object)
+ # newCompMasses.fill([])
# Determine the multiplicity of every star
MF = self._multi_props.multiplicity_fraction(newMasses)
CSF = self._multi_props.companion_star_fraction(newMasses)
-
- newIsMultiple = np.random.rand(int(newStarCount)) < MF
- # Copy over the primary masses. Eventually add the companions.
- newSystemMasses = newMasses.copy()
+ newIsMultiple = self.rng.random(int(newStarCount)) < MF
# Function to calculate multiple systems more efficiently
- newCompMasses, newSystemMasses, newIsMultiple = self.calc_multi(newMasses, newCompMasses,
- newSystemMasses, newIsMultiple,
- CSF, MF)
-
+ # start_calc = time.time()
+ newCompMasses, newSystemMasses, newIsMultiple = self.calc_multi(newMasses, newIsMultiple, CSF, MF)
+ # end_calc = time.time()
+ # print('Time taken for calc_multi: ', end_calc - start_calc)
newTotalMassTally = newSystemMasses.sum()
isMultiple = np.append(isMultiple, newIsMultiple)
systemMasses = np.append(systemMasses, newSystemMasses)
- compMasses = np.append(compMasses, newCompMasses)
+ compMasses.append(newCompMasses)
+
else:
newTotalMassTally = newMasses.sum()
-
+ # end_while = time.time()
+ # print('Time taken for while loop: ', end_while - start_while)
# Append to our primary masses array
masses = np.append(masses, newMasses)
-
+
if (loopCnt >= 0):
log.info('sample_imf: Loop %d added %.2e Msun to previous total of %.2e Msun' %
(loopCnt, newTotalMassTally, totalMassTally))
@@ -173,9 +202,28 @@ def generate_cluster(self, totalMass, seed=None):
totalMassTally += newTotalMassTally
newStarCount = mean_number * 0.1 # increase by 20% each pass
loopCnt += 1
-
+
# Make a running sum of the system masses
if self._multi_props:
+ # Concatenate the companion masses
+ if len(compMasses) > 1:
+ max_cols = max(compMass.shape[1] for compMass in compMasses)
+
+ # Pad each array to have the same number of columns
+ padded_arrays = [
+ np.ma.masked_all((compMass.shape[0], max_cols)) for compMass in compMasses
+ ]
+
+ for i, compMass in enumerate(compMasses):
+ padded_arrays[i][:, :compMass.shape[1]] = compMass
+
+ # Vertically stack the padded arrays
+ compMasses = np.ma.vstack(padded_arrays)
+
+ else:
+ compMasses = compMasses[0]
+
+ # Make a running sum of the system masses
massCumSum = systemMasses.cumsum()
else:
massCumSum = masses.cumsum()
@@ -194,78 +242,58 @@ def generate_cluster(self, totalMass, seed=None):
isMultiple = np.zeros(len(masses), dtype=bool)
systemMasses = masses
+ self._mass_limits[-1] = initial_mass_limit
+
return (masses, isMultiple, compMasses, systemMasses)
-
- def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, MF):
+
+ def calc_multi(self, newMasses, newIsMultiple, CSF, MF):
"""
Helper function to calculate multiples more efficiently.
We will use array operations as much as possible
"""
- # Identify multiple systems, calculate number of companions for
- # each
- idx = np.where(newIsMultiple == True)[0]
- n_comp_arr = 1 + np.random.poisson((CSF[idx] / MF[idx]) - 1)
- if self._multi_props.companion_max == True:
- too_many = np.where(n_comp_arr > self._multi_props.CSF_max)[0]
- n_comp_arr[too_many] = self._multi_props.CSF_max
- primary = newMasses[idx]
+ # Copy over the primary masses. Eventually add the companions.
+ newSystemMasses = newMasses.copy()
+
+ # Identify multiple systems, calculate number of companions for each
+ multiple_idx = np.where(newIsMultiple)[0]
+ comp_nums = 1 + self.rng.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1)
+ if self._multi_props.companion_max:
+ too_many = np.where(comp_nums > self._multi_props.CSF_max)[0]
+ comp_nums[too_many] = self._multi_props.CSF_max
+ primary = newMasses[multiple_idx]
# We will deal with each number of multiple system independently. This is
# so we can put in uniform arrays in _multi_props.random_q.
- num = np.unique(n_comp_arr)
- for ii in num:
- tmp = np.where(n_comp_arr == ii)[0]
-
- if ii == 1:
- # Single companion case
- q_values = self._multi_props.random_q(np.random.rand(len(tmp)))
-
- # Calculate mass of companion
- m_comp = q_values * primary[tmp]
-
- # Only keep companions that are more than the minimum mass. Update
- # compMasses, newSystemMasses, and newIsMultiple appropriately
- good = np.where(m_comp >= self._mass_limits[0])[0]
- for jj in good:
- compMasses[idx[tmp[jj]]] = np.transpose([m_comp[jj]])
- newSystemMasses[idx[tmp[jj]]] += compMasses[idx[tmp[jj]]]
-
- bad = np.where(m_comp < self._mass_limits[0])[0]
- newIsMultiple[idx[tmp[bad]]] = False
- else:
- # Multple companion case
- q_values = self._multi_props.random_q(np.random.rand(len(tmp), ii))
-
- # Calculate masses of companions
- m_comp = np.multiply(q_values, np.transpose([primary[tmp]]))
+ comp_unique = np.unique(comp_nums)
+ comp_indices = [np.where(comp_nums == i)[0] for i in comp_unique]
+ compMasses = np.zeros((len(newMasses), max(comp_unique)))
- # Update compMasses, newSystemMasses, and newIsMultiple appropriately
- for jj in range(len(tmp)):
- m_comp_tmp = m_comp[jj]
- compMasses[idx[tmp[jj]]] = m_comp_tmp[m_comp_tmp >= self._mass_limits[0]]
- newSystemMasses[idx[tmp[jj]]] += compMasses[idx[tmp[jj]]].sum()
+ for comp_num, comp_index in zip(comp_unique, comp_indices):
+ # Calculate masses of companions
+ q_values = self._multi_props.random_q(self.rng.random((len(comp_index), comp_num)))
+ m_comp = np.multiply(q_values, np.transpose([primary[comp_index]]))
+ compMasses[multiple_idx[comp_index], :comp_num] = m_comp
- # Double check for the case when we drop all companions.
- # This happens a lot near the minimum allowed mass.
- if len(compMasses[idx[tmp[jj]]]) == 0:
- newIsMultiple[idx[tmp[jj]]] = False
+ # Mask out companions that are less than the minimum mass
+ compMasses = np.ma.MaskedArray(compMasses, mask=compMasses < self._mass_limits[0])
+ newSystemMasses[multiple_idx] += compMasses[multiple_idx].sum(axis=1)
+ newIsMultiple = np.any(~compMasses.mask, axis=1)
return compMasses, newSystemMasses, newIsMultiple
-
-
+
class IMF_broken_powerlaw(IMF):
"""
Initialize a multi-part power-law with N parts. Each part of the
power-law is described with a probability density function:
- P(m) \propto m ** power[n]
+ P(m) ∝ m ** power[n]
for mass_limits[n] < m <= mass_limits[n+1].
Parameters
----------
mass_limits : numpy array
- Array of length (N + 1) with lower and upper limits of
+ Array of length (N + 1) with lower and upper limits of
the power-law segments.
powers : numpy array
@@ -273,28 +301,23 @@ class IMF_broken_powerlaw(IMF):
power-law segment.
multiplicity : Multiplicity object or None
- If None, no multiplicity is assumed. Otherwise, use
+ If None, no multiplicity is assumed. Otherwise, use
multiplicity object to create multiple star systems.
"""
- def __init__(self, mass_limits, powers, multiplicity=None):
+ def __init__(self, mass_limits, powers, multiplicity=None, seed=None):
+ super().__init__(massLimits=mass_limits, multiplicity=multiplicity, seed=seed)
+ powers = np.atleast_1d(powers)
if len(mass_limits) != len(powers) + 1:
msg = 'Incorrect specification of multi-part powerlaw.\n'
msg += ' len(massLimts) != len(powers)+1\n'
- msg += ' len(massLimits) = \n' + len(massLimits)
- msg += ' len(powers) = \n' + len(powers)
-
- raise RuntimeException(msg)
+ msg += ' len(massLimits) = \n' + str(len(mass_limits))
+ msg += ' len(powers) = \n' + str(len(powers))
- self._mass_limits = np.atleast_1d(mass_limits)
+ raise RuntimeError(msg)
+ mass_limits = np.atleast_1d(mass_limits)
self._m_limits_low = mass_limits[0:-1]
self._m_limits_high = mass_limits[1:]
- self._powers = powers
- self._multi_props = multiplicity
-
- if multiplicity == None:
- self.make_multiples = False
- else:
- self.make_multiples = True
+ self._powers = np.atleast_1d(powers)
# Calculate the coeffs to make the function continuous
nterms = len(self._powers)
@@ -323,7 +346,7 @@ def xi(self, m):
xi - probability of measuring that mass.
"""
returnFloat = type(m) == float
-
+
m = np.atleast_1d(m)
# Temporary arrays
@@ -340,7 +363,7 @@ def xi(self, m):
# Maybe we are all done?
if len(idx) == 0:
break
-
+
m_tmp = m[idx]
aux_tmp = aux[idx]
@@ -353,7 +376,7 @@ def xi(self, m):
z *= delta(m - self._m_limits_high[i])
xi = self.k * z * y
-
+
if returnFloat:
return xi[0]
else:
@@ -381,7 +404,7 @@ def m_xi(self, m):
# Maybe we are all done?
if len(idx) == 0:
break
-
+
m_tmp = m[idx]
aux_tmp = aux[idx]
@@ -394,7 +417,7 @@ def m_xi(self, m):
z *= delta(m - self._m_limits_high[i])
mxi = self.k * z * y
-
+
if returnFloat:
return mxi[0]
else:
@@ -402,23 +425,23 @@ def m_xi(self, m):
def getProbabilityBetween(self, massLo, massHi):
- """Return the integrated probability between some low and high
+ """Return the integrated probability between some low and high
mass value.
"""
return self.int_xi(massLo, massHi)
def int_xi(self, massLo, massHi):
- """Return the integrated probability between some low and high
+ """Return the integrated probability between some low and high
mass value.
"""
return self.prim_xi(massHi) - self.prim_xi(massLo)
-
+
def getMassBetween(self, massLo, massHi):
- """Return the integrated mass between some low and high
+ """Return the integrated mass between some low and high
mass value.
"""
return self.int_mxi(massLo, massHi)
-
+
def int_mxi(self, massLo, massHi):
"""Return the integrated total mass between some low and high stellar
mass value. Be sure to normalize the IMF instance beforehand.
@@ -440,7 +463,7 @@ def prim_xi(self, a):
t3 = prim_power(self._m_limits_low, self._powers)
y1 = (t1 * (t2 - t3)).sum()
- t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high)
+ t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high)
t1 *= self.coeffs
t2 = prim_power(a[i], self._powers)
t3 = prim_power(self._m_limits_low, self._powers)
@@ -458,7 +481,7 @@ def prim_mxi(self, a):
Helper function
"""
returnFloat = type(a) == float
-
+
a = np.atleast_1d(a)
val = np.zeros(len(a), dtype=float)
@@ -467,8 +490,8 @@ def prim_mxi(self, a):
t2 = prim_power(self._m_limits_high, self._powers+1)
t3 = prim_power(self._m_limits_low, self._powers+1)
y1 = (t1 * (t2 - t3)).sum()
-
- t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high)
+
+ t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high)
t1 *= self.coeffs
t2 = prim_power(a[i], self._powers+1)
t3 = prim_power(self._m_limits_low, self._powers+1)
@@ -488,7 +511,7 @@ def normalize(self, Mcl, Mmin=None, Mmax=None):
"""
self.k = 1.0
self.Mcl = Mcl
-
+
if Mmax == None:
Mmax = self._m_limits_high[-1]
@@ -498,7 +521,7 @@ def normalize(self, Mcl, Mmin=None, Mmax=None):
if Mmax > Mcl:
Mmax = Mcl
-
+
if Mmax > self._m_limits_high[-1]:
Mmax = self._m_limits_high[-1]
@@ -507,7 +530,7 @@ def normalize(self, Mcl, Mmin=None, Mmax=None):
self.norm_Mmin = Mmin
self.norm_Mmax = Mmax
-
+
self.k = Mcl / self.int_mxi(self.norm_Mmin, self.norm_Mmax)
self.lamda = self.int_xi_cl(self._m_limits_low[0], self._mass_limits)
@@ -526,7 +549,7 @@ def norm_cl_wk04(self, Mcl, Mmax=None, Mmin=None):
if Mmax > Mcl:
Mmax = Mcl
-
+
if Mmax > self._m_limits_high[-1]:
Mmax = self._m_limits_high[-1]
@@ -594,21 +617,21 @@ def dice_star_cl(self, r):
returnFloat = type(r) == float
r = np.atleast_1d(r) # Make sure it is an array
- x = r * self.lamda[-1]
- y = np.zeros(len(r), dtype=float)
- z = np.ones(len(r), dtype=float)
+ x = r * self.lamda[-1]
+ y = np.zeros_like(r)
+ z = np.ones_like(r)
# Loop through the different parts of the power law.
for i in range(self.nterms): #-----For i = 0 --> n, where n is the number of intervals
aux = x - self.lamda[i] #---Should this be i - 1?
-
+
# Only continue for those entries that are in later segments
- idx = np.where(aux >= 0)[0]
+ idx = aux >= 0
# Maybe we are all done?
- if len(idx) == 0:
+ if sum(idx) == 0:
break
-
+
x_tmp = x[idx]
aux_tmp = aux[idx]
@@ -691,7 +714,7 @@ def __init__(self, multiplicity=None):
multiplicity=multiplicity)
##################################################
-#
+#
# Generic functions -- see if we can move these up.
#
##################################################
@@ -711,11 +734,11 @@ def prim_power(m, power):
power = np.repeat(power, len(m))
z = 1.0 + power
- val = (m**z) / z
-
- val[power == -1] = np.log(m[power == -1])
+ val = np.empty_like(m)
+ valid_idx = power != -1
+ val[valid_idx] = (m[valid_idx]**z[valid_idx]) / z[valid_idx]
+ val[~valid_idx] = np.log(m[~valid_idx])
-
if returnFloat:
return val[0]
else:
@@ -723,7 +746,7 @@ def prim_power(m, power):
def inv_prim_power(x, power):
"""
- returns ((1+power) * x)**(1.0 / (1 + power)) and handles the case
+ returns ((1+power) * x)**(1.0 / (1 + power)) and handles the case
when power == -1.
"""
returnFloat = (type(x) == float) and (type(power) == float)
@@ -737,22 +760,19 @@ def inv_prim_power(x, power):
power = np.repeat(power, len(x))
if x.shape != power.shape:
- pdb.set_trace()
-
+ raise ValueError('spisea.imf.inv_prim_power: Dimension mismatch, x and power must have the same shape')
+
z = 1.0 + power
- val = (z * x)**(1.0 / z)
-
- #--------------BUG CHECK---------------------#
- # This line doesn't make sense if x is an N-element array and
- # power is just a 1-element array, which it appears to be for
- # imf.generate_cluster
- val[power == -1] = np.exp(x[power == -1])
- #-----------------------------------------------#
+ val = np.empty_like(x)
+ valid_idx = power != -1
+ val[valid_idx] = (z[valid_idx] * x[valid_idx])**(1.0 / z[valid_idx])
+ val[~valid_idx] = np.exp(x[~valid_idx])
+
if returnFloat:
return val[0]
else:
return val
-
+
def log_normal(m, mean_logm, sigma_logm):
returnFloat = (type(m) == float) and (type(mean_logm) == float) and \
@@ -764,7 +784,7 @@ def log_normal(m, mean_logm, sigma_logm):
z = np.log10(m) - mean_logm
val = np.exp(-z**2 / (2.0 * sigma_logm**2)) / m
-
+
if returnFloat:
return val[0]
else:
@@ -780,7 +800,7 @@ def prim_log_normal(m, mean_logm, sigma_logm):
mu = (np.log10(m) - mean_logm) / (1.4142135623731 * sigma_logm)
val = 2.88586244942136 * sigma_logm * error(mu)
-
+
if returnFloat:
return val[0]
else:
@@ -793,10 +813,10 @@ def inv_prim_log_normal(x, mean_logm, sigma_logm):
m = np.atleast_1d(m)
mean_logm = np.atleat_1d(mean_logm)
sigma_logm = np.atleat_1d(sigma_logm)
-
+
mu = inv_error(0.346516861952484 * x / sigma_logm)
val = 10.0**(1.4142135623731 * sigma_logm * mu + mean_logm)
-
+
if returnFloat:
return val[0]
else:
@@ -812,7 +832,7 @@ def mlog_normal(x, mean_logm, sigma_logm):
z = np.log10(m) - mean_logm
val = np.exp(-z**2 / (2.0 * sigma_logm**2))
-
+
if returnFloat:
return val[0]
else:
@@ -833,12 +853,12 @@ def prim_mlog_normal(x, mean_logm, sigma_logm):
val = error(eta)
val *= 2.88586244942136 * sigma_logm * np.exp(2.30258509299405 * t1)
-
+
if returnFloat:
return val[0]
else:
return val
-
+
def theta_closed(x):
"""
@@ -888,7 +908,7 @@ def delta(x):
def gamma_closed(m, left, right):
"""
-
+
"""
return theta_closed(m - left) * theta_closed(right - m)
@@ -896,7 +916,7 @@ def gamma_closed(m, left, right):
def error(x):
x2 = x**2
ax2 = 0.140012288686666 * x2
-
+
val = np.sqrt(1.0 - np.exp(-x2*(1.27323954473516+ax2)/(1+ax2)))
if x >=0:
@@ -909,11 +929,10 @@ def inv_error(x):
lnx2 = np.log(1.0 - x2)
aux = 4.54688497944829 + (lnx2 / 2.0)
y = -aux + np.sqrt(aux**2 - (lnx2 / 0.140012288686666))
-
+
val = np.sqrt(y)
if x>=0:
return y
else:
return -y
-
diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py
index 0d28ced6..685d5917 100755
--- a/spisea/imf/multiplicity.py
+++ b/spisea/imf/multiplicity.py
@@ -1,6 +1,7 @@
import numpy as np
import astropy.modeling
from random import choice
+from scipy.stats import truncnorm
defaultMF_amp = 0.44
defaultMF_power = 0.51
@@ -249,29 +250,34 @@ def log_semimajoraxis(self, mass):
Parameters
----------
- mass : float
- Mass of primary star
+ mass : array-like
+ Mass array of primary star
Returns
-------
- log_semimajoraxis : float
+ log_semimajoraxis : array-like
Log of the semimajor axis/separation between the stars in units of AU
"""
a_mean_func = astropy.modeling.powerlaws.BrokenPowerLaw1D(amplitude=self.a_amp, x_break=self.a_break, alpha_1=self.a_slope1, alpha_2=self.a_slope2)
log_a_mean = np.log10(a_mean_func(mass)) #mean log(a)
log_a_std_func = astropy.modeling.models.Linear1D(slope=self.a_std_slope, intercept=self.a_std_intercept)
log_a_std = log_a_std_func(np.log10(mass)) #sigma_log(a)
- if mass >= 2.9:
- log_a_std = log_a_std_func(np.log10(2.9)) #sigma_log(a)
- if log_a_std < 0.1:
- log_a_std = 0.1
-
- log_semimajoraxis = np.random.normal(log_a_mean, log_a_std)
- while 10**log_semimajoraxis > 2000 or log_semimajoraxis < -2: #AU
- log_semimajoraxis = np.random.normal(log_a_mean, log_a_std)
-
+
+ large_mass_idx = mass >= 2.9
+ log_a_std[large_mass_idx] = log_a_std_func(np.log10(2.9)) #sigma_log(a)
+ log_a_std = np.clip(log_a_std, 0.1, None)
+
+ # Trunc normal distribution between -2 and 2000 AU
+ log_a_lower = np.log10(0.01)
+ log_a_upper = np.log10(2000)
+
+ # Convert bounds to standard normal space
+ a_lower_std = (log_a_lower - log_a_mean) / log_a_std
+ a_upper_std = (log_a_upper - log_a_mean) / log_a_std
+
+ log_semimajoraxis = truncnorm.rvs(a_lower_std, a_upper_std, loc=log_a_mean, scale=log_a_std)
return log_semimajoraxis
-
+
def random_e(self, x):
"""
Generate random eccentricity from the inverse of the CDF where the PDF is f(e) = 2e from Duchene and Kraus 2013
diff --git a/spisea/imf/tests/__init__.py b/spisea/imf/tests/__init__.py
deleted file mode 100755
index 8b137891..00000000
--- a/spisea/imf/tests/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/spisea/reddening.py b/spisea/reddening.py
index 16cc6be9..0d25e5ae 100755
--- a/spisea/reddening.py
+++ b/spisea/reddening.py
@@ -7,8 +7,11 @@
import pylab as py
import numpy as np
from scipy import interpolate
+from astropy.table import Table
import pysynphot
from scipy.linalg import solve_banded
+import ast
+import os
import pdb
@@ -21,30 +24,47 @@ def get_red_law(str):
----------
str: str
Reddening law name and additional params (comma-separated).
- Name must match
+ Name must match
"""
# Parse the string, extracting redlaw name and other params
tmp = str.split(',')
name = tmp[0]
- params = ()
- if len(tmp) > 1:
- for ii in range(len(tmp) - 1):
- params = params + (float(tmp[ii+1]),)
+
+ # How we split this up changes for the broken power law EL
+ # versus the other ELs (since we have arrays for broken power law EL)
+ if name == 'broken_pl':
+ # resplit the str, given that arrays are in it
+ tmp = str.split('[')
+ tmp2 = str.split(']')
+ param1 = ast.literal_eval('[{0}'.format(tmp[1][:-1])) # Funny formatting to remove ,
+ param2 = ast.literal_eval('{0}]'.format(tmp2[1][1:])) # Funny formatting to remove ,
+ param3 = float(str.split(',')[-1])
+
+ params = (param1, param2, param3)
+ else:
+ params = ()
+ if len(tmp) > 1:
+ for ii in range(len(tmp) - 1):
+ params = params + (float(tmp[ii+1]),)
# Define dictionary connecting redlaw names to the redlaw classes
- name_dict = {'N09':RedLawNishiyama09,
+ name_dict = {'pl': RedLawPowerLaw,
+ 'broken_pl': RedLawBrokenPowerLaw,
+ 'RL85': RedLawRiekeLebofsky,
'C89': RedLawCardelli,
+ 'I05': RedLawIndebetouw05,
'RZ07': RedLawRomanZuniga07,
- 'RL85': RedLawRiekeLebofsky,
+ 'F09': RedLawFitzpatrick09,
+ 'N09':RedLawNishiyama09,
+ 'S10': RedLawSchoedel10,
+ 'F11': RedLawFritz11,
'D16': RedLawDamineli16,
'DM16': RedLawDeMarchi16,
- 'F09': RedLawFitzpatrick09,
'S16': RedLawSchlafly16,
- 'pl': RedLawPowerLaw,
- 'F11': RedLawFritz11,
- 'H18': RedLawHosek18,
'H18b': RedLawHosek18b,
- 'NL18': RedLawNoguerasLara18}
+ 'NL18': RedLawNoguerasLara18,
+ 'NL20': RedLawNoguerasLara20
+ }
# Make reddening law object, including params if necessary.
# This is not great coding, but I really strugged to generalize this...
@@ -66,15 +86,28 @@ def get_red_law(str):
class RedLawNishiyama09(pysynphot.reddening.CustomRedLaw):
"""
- Defines extinction law from `Nishiyama et al. 2009
- `_
- toward the Galactic Center. This is the default extinction law.
- The law is defined between 0.5 -- 8 microns.
+ The extinction law towards the Galactic Center
+ from `Nishiyama et al. 2009
+ `_,
+ combined with the Av / AKs value from `Nishiyama et al. 2008
+ `_.
+ This law is defined between 0.5 - 8.0 microns.
+
+ This law is constructed in 3 segments:
+
+ * 0.5 -- 1.24 microns: a linear interpolation in log(1/lambda) vs log(A/AKs) space
+ between the Av/AKs and AJ/AKs values
+ * 1.25 -- 2.14 microns: a power law with index of 2.0
+ * 2.14 -- 8.0 microns: a spline interpolation between the observed extinction values
+
+ A_lambda / A_Ks = 1 when lambda = 2.14 microns
+
+ This is the default extinction law.
"""
def __init__(self):
# Fetch the extinction curve, pre-interpolate across 3-8 microns
wave = np.arange(0.5, 8.0, 0.001)
-
+
# This will eventually be scaled by AKs when you
# call reddening(). Right now, calc for AKs=1
wave_vals, Alambda_scaled = RedLawNishiyama09._derive_nishiyama09(wave)
@@ -82,7 +115,7 @@ def __init__(self):
# Convert wavelength to angstrom
wave_vals *= 10 ** 4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave_vals,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave_vals,
waveunits='angstrom',
Avscaled=Alambda_scaled,
name='Nishiyama09',
@@ -91,11 +124,14 @@ def __init__(self):
# Set the upper/lower wavelength limits of law (in angstroms)
self.low_lim = min(wave_vals)
self.high_lim = max(wave_vals)
+
+ # Other info
+ self.scale_lambda = 2.14
self.name = 'N09'
-
+
@staticmethod
def _derive_nishiyama09(wavelength):
- """
+ """
Calculate the N09 extinction law as defined in the paper:
a A_lambda/AKs = power law of exponent -2.0 between JHK. Then
use a *linear* interpolation in 1/lambda space to go from J to the V-band observation,
@@ -111,12 +147,15 @@ def _derive_nishiyama09(wavelength):
"""
#-----Define power law extinction law between JHK----#
jhk_idx = np.where( (wavelength >= 1.25) & (wavelength <= 2.14) )
-
+ #jhk_idx = np.where( (wavelength >= 1.25) & (wavelength <= 2.3) )
+
alpha = 2.0
wave_jhk = wavelength[jhk_idx]
+ idx_scale = np.where(abs(wave_jhk - 2.14) == min(abs(wave_jhk - 2.14)) )
A_jhk = wave_jhk**(-1.0*alpha)
A_Ks_jhk = A_jhk / A_jhk[-1]
+ #A_Ks_jhk = A_jhk / A_jhk[idx_scale]
#----Now do a linear interpolation (in log(1/lambda) vs log(A/AKs) space) between 1.25 microns and 0.551 microns---#
jv_idx = np.where( (wavelength < 1.25) & (wavelength > 0.551) )
@@ -133,10 +172,15 @@ def _derive_nishiyama09(wavelength):
long_idx = np.where(wavelength > 2.14)
wave = np.array([0.551, 1.25, 1.63, 2.14, 3.545, 4.442, 5.675, 7.760])
A_AKs = np.array([16.13, 3.02, 1.73, 1.00, 0.500, 0.390, 0.360, 0.430])
-
- spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)
+ interp_idx = np.where(wave > 2.1)
+ #long_idx = np.where(wavelength > 2.3)
+ #wave = np.array([0.551, 1.25, 1.63, 2.14, wave_jhk[-1], 3.545, 4.442, 5.675, 7.760])
+ #A_AKs = np.array([16.13, 3.02, 1.73, 1.00, A_Ks_jhk[-1], 0.500, 0.390, 0.360, 0.430])
+ #interp_idx = np.where(wave > 2.2)
+
+ spline_interp = interpolate.splrep(wave[interp_idx], A_AKs[interp_idx], k=3, s=0)
A_AKs_long = interpolate.splev(wavelength[long_idx], spline_interp)
-
+
# Stitch together sections for the final law
wave_vals = np.concatenate((wavelength[jv_idx[0]], wavelength[jhk_idx[0]]))
A_AKs_vjhk = np.concatenate((A_Ks_jv, A_Ks_jhk))
@@ -148,8 +192,8 @@ def _derive_nishiyama09(wavelength):
return wave_vals, A_AKs_final
def Nishiyama09(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -169,7 +213,7 @@ def Nishiyama09(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -185,33 +229,65 @@ def Nishiyama09(self, wavelength, AKs):
A_at_wave = np.array(A_AKs_at_wave) * AKs
return A_at_wave
-
+
+ def plot_Nishiyama09(self):
+ """
+ Plot Nishiayama+09 law vs. the observed measurements
+ from their Table 1.
+ """
+ # Extract the law
+ wave = self.wave # angstroms
+ law = self.obscuration
+
+ # Convert wave to microns
+ wave *= 10**-4
+
+ # Measured extinction values from Nishiyama+09, Table 1
+ wave_obs = np.array([0.551, 1.25, 1.63, 2.14, 3.545, 4.442, 5.675, 7.760])
+ A_AKs = np.array([16.13, 3.02, 1.73, 1.00, 0.500, 0.390, 0.360, 0.430])
+ A_AKs_err = np.array([0.21, 0.04, 0.03, 0.0, 0.01, 0.01, 0.01, 0.01])
+
+ # Make plot
+ py.figure(figsize=(10,10))
+ py.plot(wave, law, 'r-', label='EL Function')
+ py.errorbar(wave_obs, A_AKs, yerr=A_AKs_err, fmt='k.', ms=10,
+ label='Measured')
+ py.xlabel('Wavelength (microns)')
+ py.ylabel(r'Extinction (A$_{\lambda}$)')
+ py.title('Nishiyama+09 EL')
+ py.gca().set_xscale('log')
+ py.gca().set_yscale('log')
+ py.legend()
+ py.savefig('nishiyama09_el.png')
+
+ return
+
class RedLawCardelli(pysynphot.reddening.CustomRedLaw):
- """
- Defines the extinction law from
- `Cardelli et al. 1989 `_.
+ r"""
+ Defines the extinction law from
+ `Cardelli et al. 1989 `_.
The law is defined from 0.3 - 3 microns, and in terms
of :math:`A_{\lambda} / A_{Ks}`, where Ks is 2.174 microns.
Parameters
----------
Rv : float
- Ratio of absolute to selective extinction, :math:`A(V) / E(B-V)`.
+ Ratio of absolute to selective extinction, :math:`A(V) / E(B-V)`.
The standard value for the diffuse ISM is 3.1.
"""
def __init__(self, Rv):
# Fetch the extinction curve, pre-interpolate across 0.3-3 microns
wave = np.arange(0.3, 3.0, 0.001)
-
+
# This will eventually be scaled by AKs when you
- # call reddening(). Produces A_lambda for AKs = 1, which will be
+ # call reddening(). Produces A_lambda for AKs = 1, which will be
# scaled later. Expects wavelength in microns
Alambda_scaled = RedLawCardelli._derive_cardelli(wave, Rv)
# Convert wavelength to angstrom
wave *= 10 ** 4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
Avscaled=Alambda_scaled,
name='Cardelli89',
@@ -220,6 +296,9 @@ def __init__(self, Rv):
# Set the upper/lower wavelength limits of law (in angstroms)
self.low_lim = min(wave)
self.high_lim = max(wave)
+
+ # other info
+ self.scale_lambda = 2.174
self.name = 'C89,{0}'.format(Rv)
@staticmethod
@@ -238,7 +317,7 @@ def _derive_cardelli(wavelength, Rv):
if (np.max(x) > 8.0):
print( 'wavelength is shorter than applicable range for Cardelli law')
return None
-
+
# Set up some arrays for coefficients that we will need
a = np.zeros(len(x), dtype=float)
b = np.zeros(len(x), dtype=float)
@@ -287,14 +366,14 @@ def _derive_cardelli(wavelength, Rv):
k_ind = np.where(abs(x-0.46) == min(abs(x-0.46)))
Aks_Av = a[k_ind] + b[k_ind]/Rv # Aks / Av
Av_Aks = 1.0 / Aks_Av # Av / Aks
-
+
output = extinction * Av_Aks # (A(lamb) / Av) * (Av / Aks) = (A(lamb) / Aks)
return output
def Cardelli89(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -314,7 +393,7 @@ def Cardelli89(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -330,18 +409,22 @@ def Cardelli89(self, wavelength, AKs):
A_at_wave = np.array(A_AKs_at_wave) * AKs
return A_at_wave
-
+
class RedLawRomanZuniga07(pysynphot.reddening.CustomRedLaw):
"""
Defines extinction law from `Roman-Zuniga et al. 2007
`_
- for the dense cloud core Barnard 59. It is defined between 1.0 - 8.0
+ for the dense cloud core Barnard 59. The law is a cubic spline fit
+ to the values of A_lambda / A_Ks derived using the color-color
+ diagrams slopes in their Table 1. It is defined between 1.0 - 8.0
microns.
+
+ A_lambda / A_Ks = 1 when lambda = 2.164 microns
"""
def __init__(self):
# Fetch the extinction curve, pre-interpolate across 1-8 microns
wave = np.arange(1.0, 8.0, 0.01)
-
+
# This will eventually be scaled by AKs when you
# call reddening(). Right now, calc for AKs=1
Alambda_scaled = RedLawRomanZuniga07._derive_romanzuniga07(wave)
@@ -349,7 +432,7 @@ def __init__(self):
# Convert wavelength to angstrom
wave *= 10**4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
Avscaled=Alambda_scaled,
name='RomanZuniga07',
@@ -362,11 +445,14 @@ def __init__(self):
@staticmethod
def _derive_romanzuniga07(wavelength):
+ """
+ Measurements taken from C-C column in Table 1 of RZ07
+ """
filters = ['J', 'H', 'Ks', '[3.6]', '[4.5]', '[5.8]', '[8.0]']
wave = np.array([1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760])
A_AKs = np.array([2.299, 1.550, 1.000, 0.618, 0.525, 0.462, 0.455])
A_AKs_err = np.array([0.530, 0.080, 0.000, 0.077, 0.063, 0.055, 0.059])
-
+
# Interpolate over the curve
spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)
A_AKs_at_wave = interpolate.splev(wavelength, spline_interp)
@@ -374,8 +460,8 @@ def _derive_romanzuniga07(wavelength):
return A_AKs_at_wave
def RomanZuniga07(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -395,7 +481,7 @@ def RomanZuniga07(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -411,17 +497,48 @@ def RomanZuniga07(self, wavelength, AKs):
A_at_wave = np.array(A_AKs_at_wave) * AKs
return A_at_wave
-
+
+ def plot_RomanZuniga07(self):
+ """
+ Plot law against measured values from Roman-Zuniga+07
+ """
+ wave = self.wave # Angstroms
+ law = self.obscuration
+
+ # Change wavelengths to microns
+ wave *= 10**-4
+
+ # Get the observed values
+ wave_obs = np.array([1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760])
+ A_AKs = np.array([2.299, 1.550, 1.000, 0.618, 0.525, 0.462, 0.455])
+ A_AKs_err = np.array([0.530, 0.080, 0.000, 0.077, 0.063, 0.055, 0.059])
+
+ # Make plot
+ py.figure(figsize=(10,10))
+ py.plot(wave, law, 'r-', label='EL Function')
+ py.errorbar(wave_obs, A_AKs, yerr=A_AKs_err, fmt='k.', ms=10,
+ label='Measured')
+ py.xlabel('Wavelength (microns)')
+ py.ylabel(r'Extinction (A$_{\lambda}$)')
+ py.title('Roman-Zuniga+07 EL')
+ py.gca().set_xscale('log')
+ py.gca().set_yscale('log')
+ py.legend()
+ py.savefig('romanzuniga07_el.png')
+ return
+
class RedLawRiekeLebofsky(pysynphot.reddening.CustomRedLaw):
"""
Defines the extinction law from `Rieke & Lebofsky 1985
`_
- for the Galactic Center. The law is defined between 1.0 - 13 microns.
+ for the Galactic Center. The law is defined between 1.0 - 5.0 microns.
+
+ A_lambda / A_Ks = 1 when lambda = 2.2 microns
"""
def __init__(self):
- # Fetch the extinction curve, pre-interpolate across 0.365-13 microns
- wave = np.arange(0.365, 13.0, 0.001)
-
+ # Define the wavelength range of the extinction law
+ wave = np.arange(1.0, 5.0, 0.001)
+
# This will eventually be scaled by AKs when you
# call reddening(). Right now, calc for AKs=1
Alambda_scaled = RedLawRiekeLebofsky._derive_RiekeLebofsky(wave)
@@ -429,7 +546,7 @@ def __init__(self):
# Convert wavelength to angstrom
wave *= 10 ** 4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
Avscaled=Alambda_scaled,
name='RiekeLebofsky',
@@ -438,27 +555,30 @@ def __init__(self):
# Set the upper/lower wavelength limits of law (in angstroms)
self.low_lim = min(wave)
self.high_lim = max(wave)
+
+ # Other info
+ self.scale_lambda = 2.2
self.name = 'RL85'
@staticmethod
def _derive_RiekeLebofsky(wavelength):
"""
- Calculate the resulting extinction for an array of wavelengths.
- The extinction is normalized with A_Ks.
+ Calculate the resulting extinction for an array of wavelengths,
+ using the values calculated in RL85. Extinction is originally
+ in A_lambda / A_v, but is re-normalized to A_lambda / A_Ks
+
+ Data pulled from Rieke+Lebofsky 1985, Table 3. Note that their
+ Table contains values from 0.3 - 13 microns, but only 1 - 5 microns
+ is measured directly.
- Data pulled from Rieke+Lebofsky 1985, Table 3
+ Wavelengths for filters I - M are from Rieke+89, table 4.
"""
- filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M',
- '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]',
+ # Arrays with the values from the paper
+ filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M',
+ '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]',
'[11.0]', '[11.5]', '[12.0]', '[12.5]', '[13.0]']
- #wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.25, 1.635, 2.2,
- # 3.77, 4.68, 4.75, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,
- # 11.5, 12.0, 12.5, 13.0])
-
- # Wavelengths from Nishiyama+09 plot of RL+85 law...slightly different than standard,
- # drop N filter
- wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.17, 1.57, 2.12,
- 3.40, 4.75, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,
+ wave = np.array([0.365, 0.445, 0.551, 0.658, 0.9, 1.25, 1.60, 2.2,
+ 3.50, 4.8, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,
11.5, 12.0, 12.5, 13.0])
A_Av = np.array([1.531, 1.324, 1.00, 0.748, 0.482, 0.282, 0.175, 0.112,
0.058, 0.023, 0.02, 0.043, 0.074, 0.087, 0.083,
@@ -469,16 +589,24 @@ def _derive_RiekeLebofsky(wavelength):
Av_Ak = 1.0 / Ak_Av
A_Ak = A_Av * Av_Ak
-
- # Interpolate over the curve
- spline_interp = interpolate.splrep(wave, A_Ak, k=3, s=0)
+
+ # Only interpolate between I and M
+ idx1 = np.where(np.array(filters) == 'I')[0][0]
+ idx2 = np.where(np.array(filters) == 'M')[0][0]
+
+ wave_interp = wave[idx1:idx2+1]
+ A_Ak_interp = A_Ak[idx1:idx2+1]
+ assert len(wave_interp) == 6
+
+ # Interpolate over the curve over desired wavelength range
+ spline_interp = interpolate.splrep(wave_interp, A_Ak_interp, k=3, s=0)
A_Ak_at_wave = interpolate.splev(wavelength, spline_interp)
return A_Ak_at_wave
def RiekeLebofsky85(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -498,7 +626,7 @@ def RiekeLebofsky85(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -515,26 +643,80 @@ def RiekeLebofsky85(self, wavelength, AKs):
return A_at_wave
+ def plot_RiekeLebofsky85(self):
+ """
+ Plot law against measured values from RL+85, from their
+ Table 3
+ """
+ wave = self.wave # Angstroms
+ law = self.obscuration
+
+ # Change wavelengths to microns
+ wave *= 10**-4
+
+ # Get the observed values from their Table 3. Note only JHKLM is
+ # measured directly by RL85, other values come from elsewhere.
+ # Wavelengths are from Rieke+89, Table 4
+ filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M',
+ '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]',
+ '[11.0]', '[11.5]', '[12.0]', '[12.5]', '[13.0]']
+ wave_obs = np.array([0.365, 0.445, 0.551, 0.658, 0.9, 1.25, 1.60, 2.2,
+ 3.50, 4.8, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,
+ 11.5, 12.0, 12.5, 13.0])
+ A_Av = np.array([1.531, 1.324, 1.00, 0.748, 0.482, 0.282, 0.175, 0.112,
+ 0.058, 0.023, 0.02, 0.043, 0.074, 0.087, 0.083,
+ 0.074, 0.060, 0.047, 0.037, 0.030, 0.027])
+
+ # Want to change this from A/Av to A/AK
+ k_ind = np.where(np.array(filters) == 'K')
+ Ak_Av = A_Av[k_ind]
+ Av_Ak = 1.0 / Ak_Av
+
+ A_Ak = A_Av * Av_Ak
+
+ # RL law is only defined between I and M. Restrict to this range
+ idx1 = np.where(np.array(filters) == 'I')[0][0]
+ idx2 = np.where(np.array(filters) == 'M')[0][0]
+
+ wave_obs_f = wave_obs[idx1:idx2+1]
+ A_Ak_f = A_Ak[idx1:idx2+1]
+
+ # Make plot
+ py.figure(figsize=(10,10))
+ py.plot(wave, law, 'r-', label='EL Function')
+ py.plot(wave_obs_f, A_Ak_f, 'k.', ms=10,
+ label='Measured')
+ py.xlabel('Wavelength (microns)')
+ py.ylabel(r'Extinction (A$_{\lambda}$)')
+ py.title('Rieke+Lebofsky+85 EL')
+ py.gca().set_xscale('log')
+ py.gca().set_yscale('log')
+ py.legend()
+ py.savefig('rl85_el.png')
+
+ return
+
class RedLawDamineli16(pysynphot.reddening.CustomRedLaw):
"""
Defines the extinction law of `Damineli et al. 2016
`_,
derived for the Wd1 cluster. The law is derived between
- 0.5 - 8.0 microns.
+ 0.4 - 4.8 microns.
+
+ Law is scaled such that A_lambda / A_Ks = 1 at 2.156 microns
"""
def __init__(self):
- # Fetch the extinction curve, pre-interpolate across 1-8 microns
- wave = np.arange(0.3, 8.0, 0.001)
-
+ # Fetch the extinction curve, pre-interpolate across 0.4-4.8 microns
+ wave = np.arange(0.4, 4.8, 0.001)
+
# This will eventually be scaled by AKs when you
# call reddening(). Right now, calc for AKs=1
Alambda_scaled = RedLawDamineli16._derive_Damineli16(wave)
- #Alambda_scaled = RedLawDamineli16.derive_Damineli16_old(wave, 1.0)
# Convert wavelength to angstrom
wave *= 10 ** 4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
Avscaled=Alambda_scaled,
name='Damineli16',
@@ -543,8 +725,11 @@ def __init__(self):
# Set the upper/lower wavelength limits of law (in angstroms)
self.low_lim = min(wave)
self.high_lim = max(wave)
+
+ self.scale_lambda = 2.159
self.name = 'D16'
-
+
+ return
@staticmethod
def _derive_Damineli16(wavelength):
@@ -563,13 +748,13 @@ def _derive_Damineli16(wavelength):
log_A_AKs = -0.015 + 2.33*x + 0.522*x**2. - 3.001*x**3. + 2.034*x**4.
# Now to convert this back to linear space
- A_AKs_at_wave = 10**log_A_AKs
+ A_AKs_at_wave = 10**log_A_AKs
return A_AKs_at_wave
def Damineli16(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -589,7 +774,7 @@ def Damineli16(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -606,6 +791,37 @@ def Damineli16(self, wavelength, AKs):
return A_at_wave
+ def plot_Damineli16(self):
+ """
+ Plot law against measured values from Damineli+16
+ from their Table 1
+ """
+ wave = self.wave # Angstroms
+ law = self.obscuration
+
+ # Change wavelengths to microns
+ wave *= 10**-4
+
+ # Get the observed values from their Table 1 (Wd1 + RC)
+ wave_obs = np.array([0.442, 0.537, 0.664, 0.805, 0.878, 1.021,
+ 1.244, 1.651, 2.159, 3.295, 4.4809])
+ A_AKs = np.array([21.43, 14.95, 11.25, 8.72, 7.23, 5.10, 3.23, 1.77,
+ 1.0, 0.39, 0.26])
+
+ # Make plot
+ py.figure(figsize=(10,10))
+ py.plot(wave, law, 'r-', label='EL Function')
+ py.errorbar(wave_obs, A_AKs, fmt='k.', ms=10,
+ label='Measured')
+ py.xlabel('Wavelength (microns)')
+ py.ylabel(r'Extinction (A$_{\lambda}$)')
+ py.title('Damineli+16 EL')
+ py.gca().set_xscale('log')
+ py.gca().set_yscale('log')
+ py.legend()
+ py.savefig('damineli16_el.png')
+ return
+
class RedLawDeMarchi16(pysynphot.reddening.CustomRedLaw):
"""
Defines extinction law from `De Marchi et al. 2016
@@ -615,7 +831,7 @@ class RedLawDeMarchi16(pysynphot.reddening.CustomRedLaw):
def __init__(self):
# Fetch the extinction curve, pre-interpolate across 1-8 microns
wave = np.arange(0.3, 8.0, 0.001)
-
+
# This will eventually be scaled by AK when you
# call reddening(). Right now, calc for AKs=1
Alambda_scaled = RedLawDeMarchi16._derive_DeMarchi16(wave)
@@ -623,7 +839,7 @@ def __init__(self):
# Convert wavelength to angstrom
wave *= 10 ** 4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
Avscaled=Alambda_scaled,
name='DeMarchi16',
@@ -675,8 +891,8 @@ def _derive_DeMarchi16(wavelength):
return A_AK_at_wave
def DeMarchi16(self, wavelength, AK):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -696,7 +912,7 @@ def DeMarchi16(self, wavelength, AK):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -712,30 +928,32 @@ def DeMarchi16(self, wavelength, AK):
A_at_wave = np.array(A_AKs_at_wave) * AK
return A_at_wave
-
+
class RedLawFitzpatrick09(pysynphot.reddening.CustomRedLaw):
"""
- Defines the extinction law from
+ Defines the extinction law from
`Fitzpatrick et al. 2009 `_.
- The law is defined between 0.3 -- 3 microns.
+ The law is defined between 0.5 -- 3 microns.
The extinction law is as defined in their equation 5, and has two
free parameters: :math:`\alpha` and R(V). Averaged over 14 sight-lines,
- the authors generally find either :math:`alpha` ~ 2.5, R(V) ~ 3, or
- :math:`alpha` ~ 1.8, R(V) ~ 5 (their Figure 6).
+ the authors generally find either :math:`alpha` ~ 2.5, R(V) ~ 3, or
+ :math:`alpha` ~ 1.8, R(V) ~ 5 (their Figure 6).
+
+ A_lambda / A_K = 1 at lambda = 2.18
Parameters
----------
alpha : float
- alpha parameter for extinction law.
+ alpha parameter for extinction law.
RV : float
- R(V) parameter for extinction law.
+ R(V) parameter for extinction law.
"""
def __init__(self, alpha, RV):
# Fetch the extinction curve, pre-interpolate across 1-8 microns
- wave = np.arange(0.7, 3.0, 0.001)
-
+ wave = np.arange(0.5, 3.0, 0.001)
+
# This will eventually be scaled by AK when you
# call reddening(). Right now, calc for AKs=1
Alambda_scaled = RedLawFitzpatrick09._derive_Fitzpatrick09(wave, alpha, RV)
@@ -743,7 +961,7 @@ def __init__(self, alpha, RV):
# Convert wavelength to angstrom
wave *= 10 ** 4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
Avscaled=Alambda_scaled,
name='Fitzpatrick09',
@@ -752,6 +970,8 @@ def __init__(self, alpha, RV):
# Set the upper/lower wavelength limits of law (in angstroms)
self.low_lim = min(wave)
self.high_lim = max(wave)
+
+ self.scale_lambda = 2.18
self.name = 'F09,{0},{1}'.format(alpha, RV)
@staticmethod
@@ -775,25 +995,26 @@ def _derive_Fitzpatrick09(wavelength, alpha, RV):
"""
alpha = float(alpha)
RV = float(RV)
-
+
# First we'll calculate k(lambda - V) = E(lambda - V) / E(B - V),
# directly from equation 5
k = (0.349 + 2.087*RV) * (1.0 / (1.0 + (wavelength / 0.507)**alpha)) - RV
# We'll calculate Alam/Av from K + Rv
- Alam_Av = (k / RV) + 1.
-
+ Alam_Av = (k / RV) + 1.
+
# Finally, to get A_lambda/Aks we need to divide Alam_Av by AKs_Av.
- # We'll assume central wavelength of 2.14 for Ks
- idx = np.where(abs(wavelength - 2.14) == min(abs(wavelength - 2.14)))
+ # We'll assume a wavelength of 2.18 for Ks, since it is the wavelength
+ # they report for K-band
+ idx = np.where(abs(wavelength - 2.18) == min(abs(wavelength - 2.18)))
A_AKs_at_wave = Alam_Av / Alam_Av[idx]
return A_AKs_at_wave
def Fitzpatrick09(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -813,7 +1034,7 @@ def Fitzpatrick09(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -832,9 +1053,11 @@ def Fitzpatrick09(self, wavelength, AKs):
class RedLawSchlafly16(pysynphot.reddening.CustomRedLaw):
"""
- Defines the extinction law from `Schlafly et al. 2016
+ Defines the extinction law from `Schlafly et al. 2016
`_.
- The law is defined between 0.5 - 8 microns.
+ The law is defined between 0.5 - 4.8 microns.
+
+ The law is scaled such that A_lambda / A_Ks = 1 when lambda = 2.151
Parameters
----------
@@ -844,9 +1067,9 @@ class RedLawSchlafly16(pysynphot.reddening.CustomRedLaw):
Free parameter in extinction law (see Schlafly+16, Eqn 6)
"""
def __init__(self, AH_AKs, x):
- # Fetch the extinction curve, pre-interpolate across 1-8 microns
+ # Fetch the extinction curve, pre-interpolate across 0.5-4.8 microns
wave = np.arange(0.5, 4.8, 0.001)
-
+
# This will eventually be scaled by AK when you
# call reddening(). Right now, calc for AKs=1
Alambda_scaled = RedLawSchlafly16._derive_Schlafly16(wave, AH_AKs, x)
@@ -854,7 +1077,7 @@ def __init__(self, AH_AKs, x):
# Convert wavelength to angstrom
wave *= 10 ** 4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
Avscaled=Alambda_scaled,
name='Schlafly16',
@@ -868,7 +1091,7 @@ def __init__(self, AH_AKs, x):
@staticmethod
def _derive_Schlafly16(wavelength, AH_AKs, x):
"""
- Calculate Schalfly+16 extinction law according to
+ Calculate Schalfly+16 extinction law according to
code provided in appendix of the paper. AH_AKs sets the
gray component while x sets the shape of the law in an
Rv-like way
@@ -879,20 +1102,20 @@ def _derive_Schlafly16(wavelength, AH_AKs, x):
# Evaluate function for desired wavelengths (in angstroms)
law = law_func(wavelength*10**4)
-
+
# Now normalize to A_lambda/AKs, rather than A_lambda/A(5420)
- idx = np.where( abs(wavelength - 2.14) == min(abs(wavelength - 2.14)) )
+ idx = np.where( abs(wavelength - 2.151) == min(abs(wavelength - 2.151)) )
law_out = law / law[idx]
-
+
return law_out
@staticmethod
def _Schlafly_appendix(x, rhk):
- """
+ """
Schlafly+16 extinction law as defined in paper appendix. We've modified
- the wrapper slightly so that the user has control of rhk and x. Here is
+ the wrapper slightly so that the user has control of rhk and x. Here is
the comments from that code:
-
+
Returns the extinction curve, A(lambda)/A(5420 A), according to
Schlafly+2016, for the parameter "x," which controls the overall shape of
the extinction curve in an R(V)-like way. The extinction curve returned
@@ -914,7 +1137,7 @@ def _Schlafly_appendix(x, rhk):
lam: anchor wavelengths (angstroms), default to Schlafly+2016
Returns: the extinction curve E, so the extinction alam = A(lam)/A(5420 A)
- is given by:
+ is given by:
A = extcurve(x)
alam = A(lam)
"""
@@ -939,8 +1162,8 @@ def _Schlafly_appendix(x, rhk):
return CubicSpline(lam, anchors/cs0(5420.), yp='3d=0')
def Schlafly16(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -960,7 +1183,7 @@ def Schlafly16(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -976,14 +1199,132 @@ def Schlafly16(self, wavelength, AKs):
A_at_wave = np.array(A_AKs_at_wave) * AKs
return A_at_wave
-
-class RedLawPowerLaw(pysynphot.reddening.CustomRedLaw):
+
+class RedLawIndebetouw05(pysynphot.reddening.CustomRedLaw):
"""
- Extinction object that is a power-law extinction law:
+ Defines the extinction law from `Indebetouw et al. 2005
+ `_.
+ The law is defined between 1.25 - 8 microns using Equation 4
+ in their paper.
+
+ Note that A_lambda / A_K = 1 when lambda = 2.164 microns.
+ """
+ def __init__(self):
+ # Get A_lambda / A_K values from Indebetouw+05
+ wave = np.arange(1.25, 8.0, 0.001) # microns
+ Alambda_scaled = RedLawIndebetouw05._derive_Indebetouw05(wave)
+
+ # Convert wavelength to angstrom
+ wave *= 10 ** 4
+
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ waveunits='angstrom',
+ Avscaled=Alambda_scaled,
+ name='Indebetouw05',
+ litref='Indebetouw+ 2005')
+
+ # Set the upper/lower wavelength limits of law (in angstroms)
+ self.low_lim = min(wave)
+ self.high_lim = max(wave)
+
+ # Other info
+ self.scale_lambda = 2.164
+ self.name = 'I05'
+
+ @staticmethod
+ def _derive_Indebetouw05(wave):
+ """
+ Calculate Indebetouw+05 extinction law using equation 4 of their
+ paper.
+ """
+ log_Alambda_AK = 0.61 - 2.22*np.log10(wave) + 1.21 * np.log10(wave)**2.
+
+ # Return output in terms of A_lambda / A_K
+ Alambda_AK = 10**log_Alambda_AK
+ return Alambda_AK
+
+ def Indebetouw05(self, wavelength, AKs):
+ """
+ Return the extinction at a given wavelength assuming the
+ extinction law and an overall extinction at AK (2.164 microns)
+
+ Parameters
+ ----------
+ wavelength : float or array
+ Wavelength to return extinction for, in microns
+ AKs : float
+ Total extinction in AKs, in mags
+ """
+ # If input entry is a single float, turn it into an array
+ try:
+ len(wavelength)
+ except:
+ wavelength = [wavelength]
+
+ # Return error if any wavelength is beyond interpolation range of
+ # extinction law
+ if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
+ return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
+
+ # Extract wave and A/AKs from law, turning wave into micron units
+ wave = self.wave * (10**-4)
+ law = self.obscuration
+
+ # Find the value of the law at the closest points
+ # to wavelength
+ A_AKs_at_wave = []
+ for ii in wavelength:
+ idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )
+ A_AKs_at_wave.append(law[idx][0])
+
+ # Now multiply by AKs (since law assumes AKs = 1)
+ A_at_wave = np.array(A_AKs_at_wave) * AKs
+
+ return A_at_wave
+
+ def plot_Indebetouw05(self):
+ """
+ Plot Indebetouw+05 extinciton curve versus their
+ actual measured values (their Table 1).
+ This is similar to their Figure 6.
+
+ Saves plot as indebetouw05_el.png in cwd
+ """
+ # Get the extinction law
+ wave = self.wave # Angstroms
+ law = self.obscuration # A_lambda / AK
+
+ # Convert wave to microns for plot
+ wave *= 10**-4
+
+ # Their average measurements across sight lines
+ # from Table 1
+ wave_arr = [1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760]
+ law_obs_arr = [2.50, 1.55, 1.0, 0.56, 0.43, 0.43, 0.43]
+ law_obs_err_arr = [0.15, 0.08, 0.0, 0.06, 0.08, 0.10, 0.10]
+
+ # Make plot
+ py.figure(figsize=(10,10))
+ py.plot(wave, law, 'r-', label='EL Function')
+ py.errorbar(wave_arr, law_obs_arr, yerr=law_obs_err_arr, fmt='k.', ms=10,
+ label='Measured')
+ py.xlabel('Wavelength (microns)')
+ py.ylabel(r'Extinction (A$_{\lambda}$)')
+ py.title('Indebetouw+05 EL')
+ py.gca().set_xscale('log')
+ py.gca().set_yscale('log')
+ py.legend()
+ py.savefig('indebetouw05_el.png')
+
+ return
+
+class RedLawPowerLaw(pysynphot.reddening.CustomRedLaw):
+ r"""
+ Extinction object that is a power-law extinction law:
:math:`A_{\lambda} \propto \lambda^{\alpha}`.
- For example, to create an extinction law between
- 0.8 and 3 microns where :math:`\alpha = 2.21`,
+ For example, to create an extinction law between
+ 0.8 and 3 microns where :math:`\alpha = 2.21`,
where :math:`A_{\lambda} / A_{Ks} = 1` at 2.12 microns:
>>> from spisea import reddening
@@ -1008,7 +1349,7 @@ class RedLawPowerLaw(pysynphot.reddening.CustomRedLaw):
def __init__(self, alpha, K_wave, wave_min=0.5, wave_max=5.0):
# Fetch the extinction curve, pre-interpolate across wave_min to wave_max
wave = np.arange(wave_min, wave_max, 0.001)
-
+
# This will eventually be scaled by AK when you
# call reddening(). Right now, calc for AKs=1
Alambda_scaled = RedLawPowerLaw._derive_powerlaw(wave, alpha, K_wave)
@@ -1016,7 +1357,7 @@ def __init__(self, alpha, K_wave, wave_min=0.5, wave_max=5.0):
# Convert wavelength to angstrom
wave *= 10 ** 4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
Avscaled=Alambda_scaled,
name='Power law')
@@ -1038,8 +1379,8 @@ def _derive_powerlaw(wavelength, alpha, K_wave):
in microns
alpha: float
- -1.0 * (power law exponent)
-
+ -1.0 * (power law exponent)
+
K_wave: float
Desired K-band wavelength, in microns
"""
@@ -1053,8 +1394,8 @@ def _derive_powerlaw(wavelength, alpha, K_wave):
return A_AKs_at_wave
def powerlaw(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -1074,7 +1415,7 @@ def powerlaw(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -1092,23 +1433,23 @@ def powerlaw(self, wavelength, AKs):
return A_at_wave
class RedLawBrokenPowerLaw(pysynphot.reddening.CustomRedLaw):
- """
- Extinction object that is a broken power-law extinction law:
+ r"""
+ Extinction object that is a broken power-law extinction law:
:math:`A_{\lambda} \propto \lambda^{\alpha[n]}`
- for:
+ for:
:math: `\lambda_{limits}[n] < \lambda <= \lambda_{limits}[n+1]`
- Note: lambda_limits must be continuous in wavelength and K_wave must be
- within one of the section defined by the lambda_limits array.
+ Note: lambda_limits must be continuous in wavelength and K_wave must be
+ within one of the section defined by the lambda_limits array.
Extinction law is only defined over lambda_limits
-
+
Units of lambda_limits array is microns.
Parameters
----------
lambda_limits : numpy array
- Array of length (N + 1) with lower and upper wavelength limits of
+ Array of length (N + 1) with lower and upper wavelength limits of
the power-law segments. Units are microns.
alpha_vals : numpy array
@@ -1139,7 +1480,7 @@ def __init__(self, lambda_limits, alpha_vals, K_wave):
# Convert wavelength to angstrom
wave *= 10 ** 4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
Avscaled=Alambda_scaled,
name='Broken Power law')
@@ -1161,8 +1502,8 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave):
in microns
alpha: float
- -1.0 * (power law exponent)
-
+ -1.0 * (power law exponent)
+
K_wave: float
Desired K-band wavelength, in microns
"""
@@ -1188,14 +1529,14 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave):
#print('wave_connect = {0}'.format(wave_connect))
#print('alph_num = {0}'.format(alpha_vals[jj]))
#print('alpha_den = {0}'.format(alpha_vals[jj+1]))
-
+
coeff *= val
-
+
law[idx] = coeff * (wave[idx]**(-1.0 * alpha))
# Let's make sure we didn't miss updating any parts of the law
assert np.sum(np.isnan(law)) == 0
-
+
# We'll identify K-band as 2.14 microns
idx = np.where(abs(wave - K_wave) == min(abs(wave - K_wave)))
A_AKs_at_wave = law / law[idx]
@@ -1203,8 +1544,8 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave):
return A_AKs_at_wave
def broken_powerlaw(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -1224,7 +1565,7 @@ def broken_powerlaw(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -1243,73 +1584,156 @@ def broken_powerlaw(self, wavelength, AKs):
class RedLawFritz11(pysynphot.reddening.CustomRedLaw):
"""
- Defines extinction law from `Fritz et al. 2011
+ Defines extinction law from `Fritz et al. 2011
`_
- for the Galactic Center. The law is defined from 1.0 -- 19 microns.
+ for the Galactic Center. The law is defined from 1.0 -- 26 microns.
+
+ By default, law is scaled such that A_lambda / A_2.166 microns = 1.
+ According to Fritz+11, A_2.166 microns = 2.62 +/- 0.11 mag is the total
+ extinction towards the inner 14"x20" of the MW.
+
+ Parameters:
+ -----------
+ scale_labmda: float
+ Wavelength at which extinction law is scaled (A_lambda / A_scale_lambda = 1),
+ in microns. Default is 2.166 microns
+
"""
- def __init__(self):
- # Fetch the extinction curve, pre-interpolate across 3-8 microns
- wave = np.arange(1.0, 19, 0.001)
-
- # This will eventually be scaled by AKs when you
- # call reddening(). Right now, calc for AKs=1
- Alambda_scaled = RedLawFritz11._derive_Fritz11(wave)
+ def __init__(self, scale_lambda=2.166):
+ # Read in the interpolated extinction curve from Fritz+11,
+ # based on their Table 8. Wavelengths in microns, extinction in mags
+ wave, ext, ext_err = RedLawFritz11._read_Fritz11()
- # Convert wavelength to angstrom
- wave *= 10 ** 4
+ # Convert wave to angstromgs
+ wave *= 10**4
+
+ # Rescale extinction law such that A_lambda / A_2.166 microns = 1
+ idx = np.where( abs(wave - (scale_lambda*10**4)) ==
+ min(abs(wave - (scale_lambda*10**4))) )
+ ext_scale = ext / ext[idx]
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ # Make custom reddening law
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
- Avscaled=Alambda_scaled,
- name='Fritz09',
+ Avscaled=ext_scale,
+ name='Fritz11',
litref='Fritz+2011')
-
+
# Set the upper/lower wavelength limits of law (in angstroms)
self.low_lim = min(wave)
self.high_lim = max(wave)
+
+ # Other useful variables
+ self.scale_lambda = scale_lambda
self.name = 'F11'
-
+
+ return
+
@staticmethod
- def _derive_Fritz11(wavelength):
+ def _read_Fritz11():
"""
- Calculate the resulting extinction for an array of wavelengths.
- The extinction is normalized to A_Ks = 1
+ Return the interpolated extinction curve from Fritz+11,
+ as defined in their Table 8.
- Data pulled from Fritz+11, Table 2
+ Output:
+ ------
+ wave: array
+ Wavelength in microns
- Parameters
- ----------
- wavelength : float
- Wavelength range to derive extinction law over, in microns
+ ext: array
+ Extinction in mags
+
+ ext_err: array
+ Extinction error, in mags
"""
- # Extinction law definition
- wave = np.array([1.282, 1.736, 2.166, 2.625, 2.758, 2.873, 3.039, 3.297, 3.74, 3.819, 3.907, 4.052,
- 4.376, 5.128, 5.908, 6.772, 7.459, 7.502, 8.76, 12.371, 19.062])
- A_AKs = np.array([7.91, 4.30, 2.49, 1.83, 1.51, 1.84, 2.07, 1.66, 1.19, 1.19, 1.09, 1.01, 1.09, 0.99,
- 1.04, 0.84, 0.81, 0.79, 2.04, 1.34, 1.34])
+ # Read in file with Table 8 info (published with Fritz+11 paper)
+ inpath = os.path.dirname(os.path.abspath(__file__))
+ infile = os.path.join(inpath, 'el_files', 'fritz11_EL_table8.fits')
+ t = Table.read(infile, format='fits')
+ wave = t['lambda']
+ ext = t['A']
+ ext_err = t['e_A']
- # Interpolate over the curve
- spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)
- A_at_wave = interpolate.splev(wavelength, spline_interp)
+ return wave, ext, ext_err
- # We'll call 2.14 microns the K-band
- idx = np.where( abs(wavelength - 2.14) == min(abs(wavelength - 2.14)) )
- A_AKs_at_wave = A_at_wave / A_at_wave[idx]
+ @staticmethod
+ def _read_Fritz11_obs():
+ """
+ Return the Fritz+11 observed values, from their Table 2
- return A_AKs_at_wave
+ Output:
+ -------
+ wave: array
+ Wavelength in microns
- def Fritz11(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
- extinction law and an overall `AKs` value.
+ ext: array
+ Extinction in mags
+
+ ext_err: array
+ Extinction error, in mags
+ """
+ # Read in file with Table 8 info (published with Fritz+11 paper)
+ sep = '/'
+ inpath = sep.join(__file__.split('/')[:-1])
+ infile = '{0}/el_files/fritz11_EL_table2.txt'.format(inpath)
+
+ t = Table.read(infile, format='ascii')
+ wave = t['wave']
+ ext = t['ext']
+ ext_err = t['ext_err']
+
+ return wave, ext, ext_err
+
+ def plot_Fritz11(self):
+ """
+ Plot Fritz+11 interpolated extinciton curve (their Fig 8)
+ versus their actual measured values (their Table 2).
+ This is similar to their Figure 8.
+
+ Saves plot as fritz11_el.png in cwd
+ """
+ # Read in the Fritz+11 table 8
+ wave, ext, ext_err = RedLawFritz11._read_Fritz11()
+
+ # Read in Fritz+11 measurements (Table 2)
+ wave_obs, ext_obs, ext_obs_err = RedLawFritz11._read_Fritz11_obs()
+
+ # Now plot the scaled extinction law, scaled to the Fritz+11
+ # extinction at 2.166 microns. Remember that this produces
+ # throughput = 10^-0.4*Alambda
+ ext_scaled = self.reddening(2.62)
+
+ # Make plot
+ py.figure(figsize=(10,10))
+ py.plot(wave, ext, 'r-', label='Interpolated EL')
+ py.fill_between(wave, ext+ext_err, ext-ext_err, color='red',
+ alpha=0.3)
+ py.errorbar(wave_obs, ext_obs, yerr=ext_obs_err, fmt='k.', ms=10,
+ label='Measured')
+ py.plot(ext_scaled.wave*10**-4, np.log10(ext_scaled.throughput)/-0.4, 'b-', label='Scaled EL')
+ py.xlabel('Wavelength (microns)')
+ py.ylabel(r'Extinction (A$_{\lambda}$)')
+ py.title('Fritz+11 EL')
+ py.gca().set_xscale('log')
+ py.gca().set_yscale('log')
+ py.legend()
+ py.savefig('fritz11_el.png')
+
+ return
+
+ def Fritz11(self, wavelength, A_scale_lambda):
+ """
+ Return the extinction at a given wavelength assuming the
+ extinction law and a total extinction at the scale_lambda
+ (the wavelength where the extinction law = 1)
Parameters
----------
wavelength : float or array
Wavelength to return extinction for, in microns
- AKs : float
- Total extinction in AKs, in mags
+ A_scale_lambda : float
+ Total extinction at scale_lambda, in mags
"""
# If input entry is a single float, turn it into an array
try:
@@ -1321,60 +1745,161 @@ def Fritz11(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
- # Extract wave and A/AKs from law, turning wave into micron units
+
+ # Extract wave and A/A_scale_lambda from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
# Find the value of the law at the closest points
# to wavelength
- A_AKs_at_wave = []
+ A_Ascale_at_wave = []
for ii in wavelength:
idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )
- A_AKs_at_wave.append(law[idx][0])
+ A_Ascale_at_wave.append(law[idx][0])
- # Now multiply by AKs (since law assumes AKs = 1)
- A_at_wave = np.array(A_AKs_at_wave) * AKs
+ # Now multiply by A_scale_lambda (since law assumes A_scale_lambda = 1)
+ A_at_wave = np.array(A_Ascale_at_wave) * A_scale_lambda
return A_at_wave
-
-class RedLawHosek18(pysynphot.reddening.CustomRedLaw):
- """
- Defines extinction law from `Hosek et al. 2018
- `_
- for the Arches Cluster and Wd1. The law is defined between
- 0.7 - 3.54 microns.
- WARNING: DEPRECATED! This law has revised to RedLawHosek18b, which
- should be used instead
+#==============================================#
+# This redlaw is now depreciated: use Hosek18b
+# (from Hosek+19, appendix B) instead
+#==============================================#
+#class RedLawHosek18(pysynphot.reddening.CustomRedLaw):
+# """
+# Defines extinction law from `Hosek et al. 2018
+# `_
+# for the Arches Cluster and Wd1. The law is defined between
+# 0.7 - 3.54 microns.
+#
+# WARNING: DEPRECATED! This law has revised to RedLawHosek18b, which
+# should be used instead
+# """
+# def __init__(self):
+# # Fetch the extinction curve, pre-interpolate across 3-8 microns
+# wave = np.arange(0.7, 3.545, 0.001)
+#
+# # This will eventually be scaled by AKs when you
+# # call reddening(). Right now, calc for AKs=1
+# Alambda_scaled = RedLawHosek18._derive_Hosek18(wave)
+#
+# # Convert wavelength to angstrom
+# wave *= 10 ** 4
+#
+# pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+# waveunits='angstrom',
+# Avscaled=Alambda_scaled,
+# name='Hosek+18',
+# litref='Hosek+ 2018')
+#
+# # Set the upper/lower wavelength limits of law (in angstroms)
+# self.low_lim = min(wave)
+# self.high_lim = max(wave)
+# self.name = 'H18'
+#
+# @staticmethod
+# def _derive_Hosek18(wavelength):
+# """
+# Derive the Hosek+18 extinction law, using the data from Table 4.
+#
+# Calculate the resulting extinction for an array of wavelengths.
+# The extinction is normalized with A_Ks.
+#
+# Data pulled from Hosek+18, Table 4
+#
+# Parameters
+# ----------
+# wavelength : float
+# Wavelength range to define extinction law over, in microns
+# """
+# # Extinction law definition
+# wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545])
+# A_AKs = np.array([9.66, 6.29, 3.56, 2.33, 1.0, 0.50])
+#
+#
+# # Following Hosek+18, Interpolate over the curve with cubic spline interpolation
+# spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)
+# A_AKs_at_wave = interpolate.splev(wavelength, spline_interp)
+#
+# # This curve already assumes A_Ks = 1.0, so we can go straight to
+# # output
+# return A_AKs_at_wave
+#
+# def Hosek18(self, wavelength, AKs):
+# """
+# Return the extinction at a given wavelength assuming the
+# extinction law and an overall `AKs` value.
+#
+# Parameters
+# ----------
+# wavelength : float or array
+# Wavelength to return extinction for, in microns
+# AKs : float
+# Total extinction in AKs, in mags
+# """
+# # If input entry is a single float, turn it into an array
+# try:
+# len(wavelength)
+# except:
+# wavelength = [wavelength]
+#
+# # Return error if any wavelength is beyond interpolation range of
+# # extinction law
+# if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
+# return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
+#
+# # Extract wave and A/AKs from law, turning wave into micron units
+# wave = self.wave * (10**-4)
+# law = self.obscuration
+#
+# # Find the value of the law at the closest points
+# # to wavelength
+# A_AKs_at_wave = []
+# for ii in wavelength:
+# idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )
+# A_AKs_at_wave.append(law[idx][0])
+#
+# # Now multiply by AKs (since law assumes AKs = 1)
+# A_at_wave = np.array(A_AKs_at_wave) * AKs
+#
+# return A_at_wave
+#=====================================================#
+
+class RedLawHosek18b(pysynphot.reddening.CustomRedLaw):
+ """
+ Defines extinction law from `Hosek et al. 2019
+ `_
+ for the Arches cluster and Wd1.
+ The law is derived between 0.7 - 3.54 microns
"""
def __init__(self):
# Fetch the extinction curve, pre-interpolate across 3-8 microns
wave = np.arange(0.7, 3.545, 0.001)
-
+
# This will eventually be scaled by AKs when you
# call reddening(). Right now, calc for AKs=1
- Alambda_scaled = RedLawHosek18._derive_Hosek18(wave)
+ Alambda_scaled = RedLawHosek18b._derive_Hosek18b(wave)
# Convert wavelength to angstrom
wave *= 10 ** 4
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
+ pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
waveunits='angstrom',
Avscaled=Alambda_scaled,
- name='Hosek+18',
- litref='Hosek+ 2018')
+ name='Hosek+18b',
+ litref='Hosek+ 2018b')
# Set the upper/lower wavelength limits of law (in angstroms)
self.low_lim = min(wave)
self.high_lim = max(wave)
- self.name = 'H18'
-
+ self.name = 'H18b'
+
@staticmethod
- def _derive_Hosek18(wavelength):
- """
- Derive the Hosek+18 extinction law, using the data from Table 4.
-
+ def _derive_Hosek18b(wavelength):
+ """
+ Derive the Hosek+18 extinction law, using the data from Table 4.
+
Calculate the resulting extinction for an array of wavelengths.
The extinction is normalized with A_Ks.
@@ -1387,20 +1912,19 @@ def _derive_Hosek18(wavelength):
"""
# Extinction law definition
wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545])
- A_AKs = np.array([9.66, 6.29, 3.56, 2.33, 1.0, 0.50])
-
+ A_AKs = np.array([7.943, 5.715, 3.142, 2.04, 1.0, 0.50])
# Following Hosek+18, Interpolate over the curve with cubic spline interpolation
spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)
A_AKs_at_wave = interpolate.splev(wavelength, spline_interp)
# This curve already assumes A_Ks = 1.0, so we can go straight to
- # output
+ # output
return A_AKs_at_wave
- def Hosek18(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ def Hosek18b(self, wavelength, AKs):
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -1420,7 +1944,7 @@ def Hosek18(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -1437,73 +1961,51 @@ def Hosek18(self, wavelength, AKs):
return A_at_wave
-class RedLawHosek18b(pysynphot.reddening.CustomRedLaw):
+class RedLawSchoedel10(RedLawBrokenPowerLaw):
"""
- Defines extinction law from `Hosek et al. 2019
- `_
- for the Arches cluster and Wd1. This should be used over RedLawHosek18b.
- The law is derived between 0.7 - 3.54 microns
- """
- def __init__(self):
- # Fetch the extinction curve, pre-interpolate across 3-8 microns
- wave = np.arange(0.7, 3.545, 0.001)
-
- # This will eventually be scaled by AKs when you
- # call reddening(). Right now, calc for AKs=1
- Alambda_scaled = RedLawHosek18b._derive_Hosek18b(wave)
+ Defines extinction law from `Schoedel et al. 2010
+ `_
+ for the Galactic Center. It is defined between 1.5 - 3.8 microns.
- # Convert wavelength to angstrom
- wave *= 10 ** 4
+ Power law indices:
- pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave,
- waveunits='angstrom',
- Avscaled=Alambda_scaled,
- name='Hosek+18b',
- litref='Hosek+ 2018b')
+ * 1.677 - 2.168 microns ---> alpha = 2.21 +/- 0.24
+ * 2.168 - 3.636 microns ---> alpha = 1.34 +/- 0.29
- # Set the upper/lower wavelength limits of law (in angstroms)
- self.low_lim = min(wave)
- self.high_lim = max(wave)
- self.name = 'H18b'
-
- @staticmethod
- def _derive_Hosek18b(wavelength):
- """
- Derive the Hosek+18 extinction law, using the data from Table 4.
-
- Calculate the resulting extinction for an array of wavelengths.
- The extinction is normalized with A_Ks.
+ Wavelengths come from effective wavelengths of observations (some buffer
+ is added to either side of these values).
- Data pulled from Hosek+18, Table 4
+ Reddening law is scaled such that A_lambda / A_Ks = 1 at
+ lambda = 2.168 microns.
+ """
+ def __init__(self):
+ lambda_limits = [3.8, 2.168, 1.5]
+ alpha_vals = [1.34, 2.21]
+ K_wave = 2.168
+ RedLawBrokenPowerLaw.__init__(self, lambda_limits, alpha_vals, K_wave)
- Parameters
- ----------
- wavelength : float
- Wavelength range to define extinction law over, in microns
- """
- # Extinction law definition
- wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545])
- A_AKs = np.array([7.943, 5.715, 3.142, 2.04, 1.0, 0.50])
-
- # Following Hosek+18, Interpolate over the curve with cubic spline interpolation
- spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)
- A_AKs_at_wave = interpolate.splev(wavelength, spline_interp)
+ # Set the upper/lower wavelength limits of law (in angstroms)
+ self.low_lim = np.min(lambda_limits)*10**4
+ self.high_lim = np.max(lambda_limits)*10**4
- # This curve already assumes A_Ks = 1.0, so we can go straight to
- # output
- return A_AKs_at_wave
+ # Other useful variables
+ self.scale_lambda = K_wave
+ self.name = 'S10'
- def Hosek18b(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
- extinction law and an overall `AKs` value.
+ return
+
+ def Schoedel10(self, wavelength, AKs):
+ """
+ Return the extinction at a given wavelength assuming the
+ extinction law and a total extinction at scale_lambda
+ (the wavelength where the extinction law = 1)
Parameters
----------
wavelength : float or array
Wavelength to return extinction for, in microns
AKs : float
- Total extinction in AKs, in mags
+ Total extinction at scale_lambda, in mags
"""
# If input entry is a single float, turn it into an array
try:
@@ -1515,7 +2017,7 @@ def Hosek18b(self, wavelength, AKs):
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
-
+
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
law = self.obscuration
@@ -1532,25 +2034,37 @@ def Hosek18b(self, wavelength, AKs):
return A_at_wave
+
class RedLawNoguerasLara18(RedLawPowerLaw):
"""
- Defines extinction law from `Nogueras-Lara et al. 2018
+ Defines extinction law from `Nogueras-Lara et al. 2018
`_
- for the Galactic Center. It is defined between 0.8 - 2.5 microns.
+ for the Galactic Center. It is defined between 1.0 - 3.0 microns.
+ Measurements were made in JHK, with effective wavelengths
+ of 1.2685, 1.6506, and 2.1629 microns, respectively.
+
+ This extinction law is a single power law with exponent
+ of alpha = 2.3 +/- 0.08.
+
+ Reddening law is scaled such that A_lambda / A_Ks = 1 at
+ lambda = 2.163 microns (the observed K-band)
"""
def __init__(self):
- wave_min = 0.8
- wave_max = 2.8
- RedLawPowerLaw.__init__(self, 2.30, 2.15, wave_min=wave_min, wave_max=wave_max)
-
+ wave_min = 1.0
+ wave_max = 3.0
+ K_wave = 2.163
+ RedLawPowerLaw.__init__(self, 2.30, K_wave, wave_min=wave_min, wave_max=wave_max)
+
# Set the upper/lower wavelength limits of law (in angstroms)
self.low_lim = wave_min*10**4
self.high_lim = wave_max*10**4
+
+ self.scale_lambda = 2.163
self.name = 'NL18'
def NoguerasLara18(self, wavelength, AKs):
- """
- Return the extinction at a given wavelength assuming the
+ """
+ Return the extinction at a given wavelength assuming the
extinction law and an overall `AKs` value.
Parameters
@@ -1569,7 +2083,82 @@ def NoguerasLara18(self, wavelength, AKs):
# Return error if any wavelength is beyond interpolation range of
# extinction law
if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
- return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
+ return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
+
+ # Extract wave and A/AKs from law, turning wave into micron units
+ wave = self.wave * (10**-4)
+ law = self.obscuration
+
+ # Find the value of the law at the closest points
+ # to wavelength
+ A_AKs_at_wave = []
+ for ii in wavelength:
+ idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )
+ A_AKs_at_wave.append(law[idx][0])
+
+ # Now multiply by AKs (since law assumes AKs = 1)
+ A_at_wave = np.array(A_AKs_at_wave) * AKs
+
+ return A_at_wave
+
+class RedLawNoguerasLara20(RedLawBrokenPowerLaw):
+ """
+ Defines extinction law from `Nogueras-Lara et al. 2020
+ `_
+ for the Galactic Center. It is defined between 1.0 -- 3 microns.
+ Measurements were made in JHK, with effective wavelengths
+ of 1.2685, 1.6506, and 2.1629 microns, respectively
+
+ Measured power law indices:
+
+ * 1.2685 - 1.6505 microns ---> alpha = 2.44 +/- 0.05
+ * 1.6505 - 2.1629 microns ---> alpha = 2.23 +/- 0.05
+
+ Wavelengths come from effective wavelengths of observations (some buffer
+ is added to either side of these values).
+
+ Reddening law is scaled such that A_lambda / A_Ks = 1 at
+ lambda = 2.163 microns (the observed K-band)
+ """
+ def __init__(self):
+ lambda_limits = [3.0, 1.6505, 1.0]
+ alpha_vals = [2.44, 2.23]
+ K_wave = 2.163
+ RedLawBrokenPowerLaw.__init__(self, lambda_limits, alpha_vals, K_wave)
+
+ # Set the upper/lower wavelength limits of law (in angstroms)
+ self.low_lim = np.min(lambda_limits)*10**4
+ self.high_lim = np.max(lambda_limits)*10**4
+
+ # Other useful variables
+ self.scale_lambda = K_wave
+ self.name = 'NL20'
+
+ return
+
+ def NoguerasLara20(self, wavelength, AKs):
+ """
+ Return the extinction at a given wavelength assuming the
+ extinction law and a total extinction at scale_lambda
+ (the wavelength where the extinction law = 1)
+
+ Parameters
+ ----------
+ wavelength : float or array
+ Wavelength to return extinction for, in microns
+ AKs : float
+ Total extinction at scale_lambda, in mags
+ """
+ # If input entry is a single float, turn it into an array
+ try:
+ len(wavelength)
+ except:
+ wavelength = [wavelength]
+
+ # Return error if any wavelength is beyond interpolation range of
+ # extinction law
+ if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):
+ return ValueError('{0}: wavelength values beyond interpolation range'.format(self))
# Extract wave and A/AKs from law, turning wave into micron units
wave = self.wave * (10**-4)
@@ -1585,7 +2174,7 @@ def NoguerasLara18(self, wavelength, AKs):
# Now multiply by AKs (since law assumes AKs = 1)
A_at_wave = np.array(A_AKs_at_wave) * AKs
- return A_at_wave
+ return A_at_wave
#---------------------------#
# Cubic spline function from Schalfly+16 appendix
diff --git a/spisea/synthetic.py b/spisea/synthetic.py
index 704074d0..4dec9340 100755
--- a/spisea/synthetic.py
+++ b/spisea/synthetic.py
@@ -1,33 +1,21 @@
+import os
+import time
+import math
+import scipy
+import inspect
+import warnings
import numpy as np
import pylab as plt
-from spisea import reddening
-from spisea import evolution
+import matplotlib.pyplot as plt
+from spisea import reddening, evolution, filters
from spisea import atmospheres as atm
-from spisea import filters
-from spisea.imf import imf, multiplicity
-from scipy import interpolate
-from scipy import stats
-from scipy.special import erf
+from scipy.spatial import cKDTree as KDTree
+from spisea.imf import multiplicity
from pysynphot import spectrum
from pysynphot import ObsBandpass
from pysynphot import observation as obs
-import pysynphot
from astropy import constants, units
-from astropy.table import Table, Column, MaskedColumn
-import pickle
-import time, datetime
-import math
-import os, glob
-import tempfile
-import scipy
-import matplotlib
-import matplotlib.pyplot as plt
-import time
-import warnings
-import pdb
-from scipy.spatial import cKDTree as KDTree
-import inspect
-import astropy.modeling
+from astropy.table import Table, Column
default_evo_model = evolution.MISTv1()
default_red_law = reddening.RedLawNishiyama09()
@@ -37,7 +25,7 @@
def Vega():
# Use Vega as our zeropoint... assume V=0.03 mag and all colors = 0.0
# These parameters are defined in Girardi+02
- vega = atm.get_kurucz_atmosphere(temperature=9550,
+ vega = atm.get_kurucz_atmosphere(temperature=9550,
gravity=3.95,
metallicity=-0.5)
@@ -47,22 +35,39 @@ def Vega():
# This is (R/d)**2 as reported by Girardi et al. 2002, page 198, col 1.
# and is used to convert to flux observed at Earth.
- vega *= 6.247e-17
-
+ vega *= 6.247e-17
+
return vega
vega = Vega()
+class Interpolator(object):
+ def __init__(self, xp, yp):
+ """Wrapper for np.interp to allow for pickling in multiprocessing.
+
+ Parameters
+ ----------
+ xp: array-like
+ x data points for interpolation
+ yp: array-like
+ y data points for interpolation
+ """
+ self.xp = xp
+ self.yp = yp
+
+ def __call__(self, x):
+ return np.interp(x, self.xp, self.yp, left=np.nan, right=np.nan)
+
class Cluster(object):
"""
Base class to create a cluster with user-specified isochrone,
- imf, ifmr, and total mass.
+ imf, ifmr, and total mass.
Parameters
-----------
iso: isochrone object
SPISEA isochrone object
-
+
imf: imf object
SPISEA IMF object
@@ -75,9 +80,9 @@ class Cluster(object):
no compact remnants are produced.
seed: int
- If set to non-None, all random sampling will be seeded with the
- specified seed, forcing identical output.
- Default None
+ Seed for the random number generator numpy.random.default_rng(seed).
+ All random functions in the class will use this generator,
+ unless a different generator is passed in as an argument to the function, by default None.
vebose: boolean
True for verbose output.
@@ -90,17 +95,18 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=False,
self.ifmr = ifmr
self.cluster_mass = cluster_mass
self.seed = seed
-
+ self.rng = np.random.default_rng(self.seed)
+
return
-
+
class ResolvedCluster(Cluster):
"""
Cluster sub-class that produces a *resolved* stellar cluster.
- A table is output with the synthetic photometry and intrinsic
- properties of the individual stars (or stellar systems, if
+ A table is output with the synthetic photometry and intrinsic
+ properties of the individual stars (or stellar systems, if
mutliplicity is used in the IMF object).
- If multiplicity is used, than a second table is produced that
+ If multiplicity is used, than a second table is produced that
contains the properties of the companion stars independent of their
primary stars.
@@ -108,41 +114,80 @@ class ResolvedCluster(Cluster):
-----------
iso: isochrone object
SPISEA isochrone object
-
+
imf: imf object
SPISEA IMF object
cluster_mass: float
Total initial mass of the cluster, in M_sun
- ifmr: ifmr object or None
+ ifmr: ifmr object, optional
If ifmr object is defined, will create compact remnants
produced by the cluster at the given isochrone age. Otherwise,
no compact remnants are produced.
+ By default None.
- seed: int
- If set to non-None, all random sampling will be seeded with the
- specified seed, forcing identical output.
- Default None
+ keep_low_mass_stars: boolean, optional
+ If True, the cluster will not cut out stars below the isochrone grid
+ on initial mass. They are assigned a current mass equal to their initial
+ mass, a phase of 98, and no other evolutionary properties or photometry.
+ If False, stars below the isochrone initial mass limit are cut out.
+ By default False.
- vebose: boolean
+ seed: int, optional
+ Seed for the random number generator numpy.random.default_rng(seed).
+ All random functions in the class will use this generator,
+ unless a different generator is passed in as an argument to the function, by default None.
+
+ vebose: boolean, optional
True for verbose output.
+
+ Attributes
+ ----------
+ star_systems: astropy.table.Table
+ Table containing the properties of the primary stars (or stellar systems, if multiplicity is used). The columns include:
+ mass: primary mass
+ isMultiple: boolean for whether the star is in a multiple system
+ systemMass: total initial mass of the stellar system (primary + companions)
+ Teff: effective temperature of the star
+ L: luminosity of the star in L_sun
+ logg: surface gravity of the star in cgs
+ isWR: boolean for whether the star is a Wolf-Rayet star
+ mass_current: current mass of the star
+ phase: evolutionary phase of the star, as defined by the isochrone model
+ metallicity: metallicity of the star
+ filter columns: magnitude of the star in each filter defined by the isochrone model
+
+ companions: astropy.table.Table (only if multiplicity is used in the IMF object)
+ Table containing the properties of the companion stars. The columns include:
+ system_idx: index of the stellar system this companion belongs to, which can be used to match to the star_systems table
+ mass: initial mass of the companion star
+ Teff: effective temperature of the companion star
+ L: luminosity of the companion star in L_sun
+ logg: surface gravity of the companion star in cgs
+ isWR: boolean for whether the companion star is a Wolf-Rayet star
+ mass_current: current mass of the companion star
+ phase: evolutionary phase of the companion star, as defined by the isochrone model
+ metallicity: metallicity of the companion star
+ filter columns: magnitude of the companion star in each filter defined by the isochrone model
+ If multiplicity properties are defined in the IMF object, additional columns for those properties (e.g., log_a, e, i, Omega, omega) are included.
"""
def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True,
- seed=None):
+ seed=None, keep_low_mass_stars=False):
Cluster.__init__(self, iso, imf, cluster_mass, ifmr=ifmr, verbose=verbose,
seed=seed)
-
# Provide a user warning is random seed is set
- if seed is not None:
+ if seed is not None and verbose:
print('WARNING: random seed set to %i' % seed)
+ imf.rng = self.rng
- t1 = time.time()
- #####
+ #####
# Sample the IMF to build up our cluster mass.
#####
- mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass,
- seed=seed)
+ # start0 = time.time()
+ mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass)
+ # end0 = time.time()
+ # print('IMF sampling took {0:f} s.'.format(end0 - start0))
# Figure out the filters we will make.
self.filt_names = self.set_filter_names()
@@ -154,31 +199,46 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True,
interp_keys = ['Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase'] + self.filt_names
self.iso_interps = {}
for ikey in interp_keys:
- self.iso_interps[ikey] = interpolate.interp1d(self.iso.points['mass'], self.iso.points[ikey],
- kind='linear', bounds_error=False, fill_value=np.nan)
-
- #####
+ # self.iso_interps[ikey] = interpolate.interp1d(self.iso.points['mass'], self.iso.points[ikey],
+ # kind='linear', bounds_error=False, fill_value=np.nan)
+ self.iso_interps[ikey] = Interpolator(self.iso.points['mass'], self.iso.points[ikey])
+
+ #####
# Make a table to contain all the information about each stellar system.
#####
+ # start1 = time.time()
star_systems = self._make_star_systems_table(mass, isMulti, sysMass)
-
+ # end1 = time.time()
+ # print('Star systems table took {0:f} s.'.format(end1 - start1))
+
# Trim out bad systems; specifically, stars with masses outside those provided
# by the model isochrone (except for compact objects).
- star_systems, compMass = self._remove_bad_systems(star_systems, compMass)
+ star_systems, compMass = self._remove_bad_systems(star_systems, compMass, keep_low_mass_stars)
- #####
+ #####
# Make a table to contain all the information about companions.
#####
if self.imf.make_multiples:
- companions = self._make_companions_table(star_systems, compMass)
-
+ # start3 = time.time()
+ star_systems, companions = self._make_companions_table_new(star_systems, compMass)
+ # end3 = time.time()
+ # print('Companion table new took {0:f} s.'.format(end3 - start3))
+ self.companions = companions
+
+ # compMass = [
+ # [value for value, mask in zip(row, row_mask) if not mask]
+ # for row, row_mask in zip(compMass.data, compMass.mask)
+ # ]
+ # start3 = time.time()
+ # star_systems, companions = self._make_companions_table(star_systems, compMass)
+ # end3 = time.time()
+ # print('Companion table took {0:f} s.'.format(end3-start3))
+ # self.companions = companions
+
#####
# Save our arrays to the object
#####
self.star_systems = star_systems
-
- if self.imf.make_multiples:
- self.companions = companions
return
@@ -187,13 +247,13 @@ def set_filter_names(self):
Set filter column names
"""
filt_names = []
-
+
for col_name in self.iso.points.colnames:
if 'm_' in col_name:
filt_names.append(col_name)
return filt_names
-
+
def _make_star_systems_table(self, mass, isMulti, sysMass):
"""
Make a star_systems table and get synthetic photometry for each primary star.
@@ -202,28 +262,20 @@ def _make_star_systems_table(self, mass, isMulti, sysMass):
names=['mass', 'isMultiple', 'systemMass'])
N_systems = len(star_systems)
- # Add columns for the Teff, L, logg, isWR, mass_current, phase for the primary stars.
- star_systems.add_column( Column(np.zeros(N_systems, dtype=float), name='Teff') )
- star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='L') )
- star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='logg') )
- star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='isWR') )
- star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='mass_current') )
- star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='phase') )
- star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='metallicity') )
+ # Use our pre-built interpolators to fetch values from the isochrone for each star.
+ for key in ['Teff', 'L', 'logg', 'mass_current']:
+ star_systems.add_column(Column(self.iso_interps[key](star_systems['mass']), name=key))
+
+ # Treat out-of-range mass as isWR=True
+ star_systems.add_column(Column(~(self.iso_interps['isWR'](star_systems['mass']) < 0.5), name='isWR'))
+ star_systems.add_column(Column(np.round(self.iso_interps['phase'](star_systems['mass'])), name='phase'))
+
+ star_systems['metallicity'] = np.ones(N_systems) * self.iso.metallicity
# Add the filter columns to the table. They are empty so far.
# Keep track of the filter names in : filt_names
for filt in self.filt_names:
- star_systems.add_column( Column(np.empty(N_systems, dtype=float), name=filt) )
-
- # Use our pre-built interpolators to fetch values from the isochrone for each star.
- star_systems['Teff'] = self.iso_interps['Teff'](star_systems['mass'])
- star_systems['L'] = self.iso_interps['L'](star_systems['mass'])
- star_systems['logg'] = self.iso_interps['logg'](star_systems['mass'])
- star_systems['isWR'] = np.round(self.iso_interps['isWR'](star_systems['mass']))
- star_systems['mass_current'] = self.iso_interps['mass_current'](star_systems['mass'])
- star_systems['phase'] = np.round(self.iso_interps['phase'](star_systems['mass']))
- star_systems['metallicity'] = np.ones(N_systems)*self.iso.metallicity
+ star_systems.add_column(Column(self.iso_interps[filt](star_systems['mass']), name=filt))
# For a very small fraction of stars, the star phase falls on integers in-between
# the ones we have definition for, as a result of the interpolation. For these
@@ -233,29 +285,22 @@ def _make_star_systems_table(self, mass, isMulti, sysMass):
# effect is so small
# Convert nan_to_num to avoid errors on greater than, less than comparisons
star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99)
- bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99))
- # Print warning, if desired
- verbose=False
- if verbose:
- for ii in range(len(bad[0])):
- print('WARNING: changing phase {0} to 5'.format(star_systems['phase'][bad[0][ii]]))
+ bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) &
+ (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99))
star_systems['phase'][bad] = 5
-
- for filt in self.filt_names:
- star_systems[filt] = self.iso_interps[filt](star_systems['mass'])
#####
# Make Remnants
# Note: Some models already have WDs in them. If they do, then they shouldn't
# be handled by this code here (because their Teff > 0).
- #
+ #
# Remnants have flux = 0 in all bands if they are generated here.
- #####
+ #####
if self.ifmr != None:
# Identify compact objects as those with Teff = 0 or with phase > 100.
highest_mass_iso = self.iso.points['mass'].max()
idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso))[0]
-
+
# Calculate remnant mass and ID for compact objects; update remnant_id and
# remnant_mass arrays accordingly
if 'metallicity_array' in inspect.getfullargspec(self.ifmr.generate_death_mass).args:
@@ -266,7 +311,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass):
# Drop remnants where it is not relevant (e.g. not a compact object or
# outside mass range IFMR is defined for)
- good = np.where(r_id_tmp > 0)
+ good = r_id_tmp > 0
idx_rem_good = idx_rem[good]
star_systems['mass_current'][idx_rem_good] = r_mass_tmp[good]
@@ -277,13 +322,111 @@ def _make_star_systems_table(self, mass, isMulti, sysMass):
star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan)
return star_systems
-
+
+
+ def _make_companions_table_new(self, star_systems, compMass):
+ """Make companions table for resolved clusters with multiplicity.
+
+ Parameters
+ ----------
+ star_systems : astropy.table.Table
+ Table containing the properties of the primary stars.
+ compMass : numpy.ma.MaskedArray
+ Masked array containing the masses of the companions.
+
+ Returns
+ -------
+ companions : astropy.table.Table
+ """
+ N_systems = len(star_systems)
+ N_companions = np.sum(~compMass.mask, axis=1)
+ N_comp_tot = np.sum(N_companions)
+ star_systems.add_column(Column(N_companions, name='N_companions'))
+ system_index = np.repeat(np.arange(N_systems), N_companions)
+ companions = Table([system_index], names=['system_idx'])
+ companions.add_column(np.zeros(N_comp_tot, dtype=float), name='mass')
+
+ if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK):
+ companions.add_column(Column(self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx']]), name='log_a'))
+ companions.add_column(Column(self.imf._multi_props.random_e(self.rng.random(N_comp_tot)), name='e'))
+ companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters(
+ self.rng.random(N_comp_tot),
+ self.rng.random(N_comp_tot),
+ self.rng.random(N_comp_tot)
+ )
+
+ companions['mass'] = compMass.compressed()
+ for key in ['Teff', 'L', 'logg', 'mass_current']:
+ companions[key] = self.iso_interps[key](companions['mass'])
+
+ for key in ['isWR', 'phase']:
+ companions[key] = np.round(self.iso_interps[key](companions['mass']))
+
+ companions['metallicity'] = np.ones(N_comp_tot) * self.iso.metallicity
+
+ # For a very small fraction of stars, the star phase falls on integers in-between
+ # the ones we have definition for, as a result of the interpolation. For these
+ # stars, round phase down to nearest defined phase (e.g., if phase is 71,
+ # then round it down to 5, rather than up to 101).
+ # Convert nan_to_num to avoid errors on greater than, less than comparisons
+ companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99)
+ companions['phase'][
+ (companions_phase_non_nan > 5) &
+ (companions_phase_non_nan < 101) &
+ (companions_phase_non_nan != 9) &
+ (companions_phase_non_nan != -99)
+ ] = 5
+
+ # Update primary fluxes to include the flux of companions.
+ for filt in self.filt_names:
+ companions[filt] = self.iso_interps[filt](companions['mass'])
+ primary_flux = 10**(-star_systems[filt] / 2.5)
+ # Sum the flux of all companions in each system
+ companions_flux = np.bincount(companions['system_idx'], weights=10**(-companions[filt] / 2.5), minlength=N_systems)
+ combined_flux = np.nansum(np.vstack((primary_flux, companions_flux)), axis=0)
+ combined_flux[combined_flux == 0] = np.nan
+ star_systems[filt] = -2.5 * np.log10(combined_flux)
+
+ #####
+ # Make Remnants with flux = 0 in all bands.
+ #####
+ if self.ifmr:
+ # Identify compact objects as those with Teff = 0 or with masses above the max iso mass
+ highest_mass_iso = self.iso.points['mass'].max()
+ remnant_idx = np.where(np.isnan(companions['Teff']) & (companions['mass'] > highest_mass_iso))[0]
+ self.remnant_idx_new = remnant_idx
+ # Calculate remnant mass and ID for compact objects; update remnant_id and remnant_mass arrays accordingly
+ if 'metallicity_array' in inspect.getfullargspec(self.ifmr.generate_death_mass).args:
+ remnant_mass, remnant_code = self.ifmr.generate_death_mass(mass_array=companions['mass'][remnant_idx], metallicity_array=companions['metallicity'][remnant_idx])
+ else:
+ remnant_mass, remnant_code = self.ifmr.generate_death_mass(mass_array=companions['mass'][remnant_idx])
+
+ # Drop remnants where it is not relevant (e.g. not a compact object or outside mass range IFMR is defined for)
+ remnant_valid = remnant_code > 0
+ remnant_valid_idx = remnant_idx[remnant_valid]
+ self.remnant_mass_new = remnant_mass
+ self.remnant_valid_idx_new = remnant_valid_idx
+ companions['mass_current'][remnant_valid_idx] = remnant_mass[remnant_valid]
+ companions['phase'][remnant_valid_idx] = remnant_code[remnant_valid]
+ # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux.
+ for filt in self.filt_names:
+ companions[filt][remnant_valid_idx] = np.full(len(remnant_idx[remnant_valid]), np.nan)
+
+ companions_teff_non_nan = np.nan_to_num(companions['Teff'], nan=-99)
+ if self.verbose and sum(companions_teff_non_nan > 0) != N_comp_tot:
+ print(f'Found {N_comp_tot - sum(companions_teff_non_nan > 0):d} companions out of stellar mass range')
+
+ assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive"
+
+ return star_systems, companions
+
+
def _make_companions_table(self, star_systems, compMass):
N_systems = len(star_systems)
-
+
#####
- # MULTIPLICITY
+ # MULTIPLICITY
# Make a second table containing all the companion-star masses.
# This table will be much longer... here are the arrays:
# sysIndex - the index of the system this star belongs too
@@ -307,25 +450,29 @@ def _make_companions_table(self, star_systems, compMass):
companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name='metallicity') )
for filt in self.filt_names:
companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name=filt) )
-
+
if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK):
companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='log_a') )
companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='e') )
companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='i', description = 'degrees') )
companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='Omega') )
companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='omega') )
-
+
for ii in range(len(companions)):
companions['log_a'][ii] = self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx'][ii]])
-
- companions['e'] = self.imf._multi_props.random_e(np.random.rand(N_comp_tot))
- companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters(np.random.rand(N_comp_tot),np.random.rand(N_comp_tot),np.random.rand(N_comp_tot))
+
+ companions['e'] = self.imf._multi_props.random_e(self.rng.random(N_comp_tot))
+ companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters(
+ self.rng.random(N_comp_tot),
+ self.rng.random(N_comp_tot),
+ self.rng.random(N_comp_tot)
+ )
# Make an array that maps system index (ii), companion index (cc) to
# the place in the 1D companions array.
N_comp_max = N_companions.max()
-
+
comp_index = np.zeros((N_systems, N_comp_max), dtype=int)
kk = 0
for ii in range(N_systems):
@@ -341,9 +488,10 @@ def _make_companions_table(self, star_systems, compMass):
idx = np.where(N_companions >= cc)[0]
# Get the location in the companions array for each system and
- # the cc'th companion.
+ # the cc'th companion.
cdx = comp_index[idx, cc-1]
-
+
+ # companions['mass'][cdx] = compMass[idx, cc-1]
companions['mass'][cdx] = [compMass[ii][cc-1] for ii in idx]
comp_mass = companions['mass'][cdx]
@@ -361,13 +509,16 @@ def _make_companions_table(self, star_systems, compMass):
# stars, round phase down to nearest defined phase (e.g., if phase is 71,
# then round it down to 5, rather than up to 101).
# Convert nan_to_num to avoid errors on greater than, less than comparisons
- star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99)
- bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99))
+ companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99)
+ bad = np.where( (companions_phase_non_nan > 5) &
+ (companions_phase_non_nan < 101) &
+ (companions_phase_non_nan != 9) &
+ (companions_phase_non_nan != -99))[0]
# Print warning, if desired
verbose=False
if verbose:
- for ii in range(len(bad[0])):
- print('WARNING: changing phase {0} to 5'.format(companions['phase'][bad[0][ii]]))
+ for ii in range(len(bad)):
+ print('WARNING: changing phase {0} to 5'.format(companions['phase'][bad[ii]]))
companions['phase'][bad] = 5
for filt in self.filt_names:
@@ -387,15 +538,15 @@ def _make_companions_table(self, star_systems, compMass):
# If *both* objects are dark, then keep the magnitude
# as np.nan. Otherwise, add fluxes together
- good = np.where( (f1 != 0) | (f2 != 0) )
- bad = np.where( (f1 == 0) & (f2 == 0) )
-
+ good = np.where( (f1 != 0) | (f2 != 0) )[0]
+ bad = np.where( (f1 == 0) & (f2 == 0) )[0]
+
star_systems[filt][idx[good]] = -2.5 * np.log10(f1[good] + f2[good])
star_systems[filt][idx[bad]] = np.nan
#####
# Make Remnants with flux = 0 in all bands.
- #####
+ #####
if self.ifmr != None:
# Identify compact objects as those with Teff = 0 or with masses above the max iso mass
highest_mass_iso = self.iso.points['mass'].max()
@@ -405,19 +556,20 @@ def _make_companions_table(self, star_systems, compMass):
# Calculate remnant mass and ID for compact objects; update remnant_id and
# remnant_mass arrays accordingly
if 'metallicity_array' in inspect.getfullargspec(self.ifmr.generate_death_mass).args:
- r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=star_systems['mass'][cdx_rem],
- metallicity_array=star_systems['metallicity'][cdx_rem])
+ r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem],
+ metallicity_array=companions['metallicity'][cdx_rem])
else:
- r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=star_systems['mass'][cdx_rem])
+ r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem])
+
# Drop remnants where it is not relevant (e.g. not a compact object or
# outside mass range IFMR is defined for)
- good = np.where(r_id_tmp > 0)
+ good = np.where(r_id_tmp > 0)[0]
cdx_rem_good = cdx_rem[good]
companions['mass_current'][cdx_rem_good] = r_mass_tmp[good]
companions['phase'][cdx_rem_good] = r_id_tmp[good]
-
+
# Give remnants a magnitude of nan, so they can be filtered out later when calculating flux.
for filt in self.filt_names:
companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan)
@@ -430,19 +582,25 @@ def _make_companions_table(self, star_systems, compMass):
if len(idx) != N_comp_tot and self.verbose:
print( 'Found {0:d} companions out of stellar mass range'.format(N_comp_tot - len(idx)))
+ # For low-mass stars and substellar objects below isochrone, assume no mass loss and set phase to 98
+ low_mass_idxs = (companions['mass'] 0
+ if len(idx) > 0:
+ assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive"
- return companions
+ return star_systems, companions
-
- def _remove_bad_systems(self, star_systems, compMass):
+
+ def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars):
"""
Helper function to remove stars with masses outside the isochrone
- mass range from the cluster. These stars are identified by having
+ mass range from the cluster. These stars are identified by having
a Teff = 0, as set up by _make_star_systems_table_interp.
- If self.ifmr == None, then both high and low-mass bad systems are
- removed. If self.ifmr != None, then we will save the high mass systems
+ If self.ifmr == None, then both high and low-mass bad systems are
+ removed. If self.ifmr != None, then we will save the high mass systems
since they will be plugged into an ifmr later.
"""
N_systems = len(star_systems)
@@ -451,23 +609,50 @@ def _remove_bad_systems(self, star_systems, compMass):
# Convert nan_to_num to avoid errors on greater than, less than comparisons
star_systems_teff_non_nan = np.nan_to_num(star_systems['Teff'], nan=-99)
star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99)
- if self.ifmr == None:
+ if (self.ifmr == None) and (not keep_low_mass_stars):
+ print('Remove low mass stars below grid and compact objects')
# Keep only those stars with Teff assigned.
- idx = np.where(star_systems_teff_non_nan > 0)[0]
+ idx = star_systems_teff_non_nan > 0
+ elif not keep_low_mass_stars:
+ print('Remove low mass stars, keep compact objects')
+ # Keep stars (with Teff) and any other compact objects (with phase info).
+ idx = (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0)
+ elif self.ifmr == None:
+ print('Remove compact objects, keep low mass stars below grid')
+ # Keep stars (with Teff) and objects below mass grid
+ idx = (star_systems_teff_non_nan > 0) | (star_systems['mass'] < np.min(self.iso.points['mass']))
else:
- # Keep stars (with Teff) and any other compact objects (with phase info).
- idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) )[0]
-
- if len(idx) != N_systems and self.verbose:
- print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx)))
+ print('Keep low mass stars below grid and compact objects')
+ # Keep all
+ idx = (star_systems_teff_non_nan > 0) | \
+ (star_systems_phase_non_nan >= 0) | \
+ (star_systems['mass'] < np.min(self.iso.points['mass']))
+
+ n_out_of_range = N_systems - sum(idx)
+ if self.verbose and n_out_of_range > 0:
+ print( 'Found {0:d} stars out of mass range'.format(n_out_of_range))
+
+ if keep_low_mass_stars:
+ lm_idx = star_systems['mass']0:
+ self.verbose = True
+ print('Missing photometry for',len(comp_filters),'filter - recomputing these columns:',comp_filters)
+
+ print('Loading stellar spectra')
+ # Initialize output for stellar spectra
+ self.spec_list = []
+ # For each isochrone point, extract the synthetic photometry.
+ for ii in range(len(self.points['Teff'])):
+ # Loop is currently taking about 0.11 s per iteration
+ gravity = float( self.points['logg'][ii] )
+ L = float( (self.points['L'][ii]*units.W).cgs / (units.erg / units.s)) # in erg/s
+ T = float( self.points['Teff'][ii] ) # in Kelvin
+ R = float( (self.points['R'][ii]*units.m).to('pc') / units.pc) # in pc
+ phase = int(self.points['phase'][ii])
+ # Get the atmosphere model now. Wavelength is in Angstroms
+ # This is the time-intensive call... everything else is negligable.
+ # If source is a star, pull from star atmospheres. If it is a WD,
+ # pull from WD atmospheres
+ if phase == 101:
+ star = wd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity,
+ verbose=False)
+ else:
+ star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity,
+ rebin=rebin)
+ # Trim wavelength range down to appropriate range
+ star = spectrum.trimSpectrum(star, wave_range[0], wave_range[1])
+ # Convert into flux observed at Earth (unreddened)
+ star *= (R / self.points.meta["DISTANCE"])**2 # in erg s^-1 cm^-2 A^-1
+ # Redden the spectrum. This doesn't take much time at all.
+ red = red_law.reddening(AKs).resample(star.wave)
+ star *= red
+ # Save the final spectrum to our spec_list for later use.
+ self.spec_list.append(star)
+
+ self.make_photometry(rebin=rebin, vega=vega, comp_filters=comp_filters)
+
+ # Drop filters in the saved file that we don't actually want here
+ all_filters = ['m_'+get_filter_col_name(f) for f in filters]
+ drop_columns = [col for col in self.points.columns if (col[:2]=='m_' and
+ (col not in all_filters))]
+ self.points.remove_columns(drop_columns)
return
- def make_photometry(self, rebin=True, vega=vega):
- """
+ def make_photometry(self, rebin=True, vega=vega, comp_filters=None):
+ """
Make synthetic photometry for the specified filters. This function
udpates the self.points table to include new columns with the
photometry.
-
- """
- startTime = time.time()
- meta = self.points.meta
+ """
+ if self.verbose:
+ startTime = time.time()
- print( 'Making photometry for isochrone: log(t) = %.2f AKs = %.2f dist = %d' % \
- (meta['LOGAGE'], meta['AKS'], meta['DISTANCE']))
- print( ' Starting at: ', datetime.datetime.now(), ' Usually takes ~5 minutes')
+ # print('Making photometry for isochrone: log(t) = %.2f AKs = %.2f dist = %d' % \
+ # (meta['LOGAGE'], meta['AKS'], meta['DISTANCE']))
+ # print( 'Starting at: ', datetime.datetime.now(), ' Usually takes ~5 minutes')
npoints = len(self.points)
verbose_fmt = 'M = {0:7.3f} Msun T = {1:5.0f} K m_{2:s} = {3:4.2f}'
+ #Calculate all filters, or select filters
+ if comp_filters is None:
+ comp_filters = self.filters
+
# Loop through the filters, get filter info, make photometry for
# all stars in this filter.
- for ii in self.filters:
- prt_fmt = 'Starting filter: {0:s} Elapsed time: {1:.2f} seconds'
- print( prt_fmt.format(ii, time.time() - startTime))
-
+ for ii in comp_filters:
+ if self.verbose:
+ prt_fmt = 'Starting filter: {0:s} Elapsed time: {1:.2f} seconds'
+ print( prt_fmt.format(ii, time.time() - startTime))
+
filt = get_filter_info(ii, rebin=rebin, vega=vega)
filt_name = get_filter_col_name(ii)
@@ -1044,21 +1345,23 @@ def make_photometry(self, rebin=True, vega=vega):
col_name = 'm_' + filt_name
mag_col = Column(np.zeros(npoints, dtype=float), name=col_name)
self.points.add_column(mag_col)
-
+
# Loop through each star in the isochrone and do the filter integration
- print('Starting synthetic photometry')
+ if self.verbose:
+ print('Starting synthetic photometry')
for ss in range(npoints):
star = self.spec_list[ss] # These are already extincted, observed spectra.
star_mag = mag_in_filter(star, filt)
-
+
self.points[col_name][ss] = star_mag
-
- if (self.verbose and (ss % 100) == 0):
+
+ if self.verbose and (ss % 100) == 0:
print( verbose_fmt.format(self.points['mass'][ss], self.points['Teff'][ss],
filt_name, star_mag))
- endTime = time.time()
- print( ' Time taken: {0:.2f} seconds'.format(endTime - startTime))
+ if self.verbose:
+ endTime = time.time()
+ print( 'Time taken: {0:.2f} seconds'.format(endTime - startTime))
if self.save_file != None:
with warnings.catch_warnings():
@@ -1067,30 +1370,6 @@ def make_photometry(self, rebin=True, vega=vega):
return
- def check_save_file(self, evo_model, atm_func, red_law):
- """
- Check to see if save_file exists, as saved by the save_file
- and save_file_legacy objects. If the filename exists, check the
- meta-data as well.
-
- returns a boolean: True is file exists, false otherwise
- """
- out_bool = False
-
- if os.path.exists(self.save_file) | os.path.exists(self.save_file_legacy):
- try:
- tmp = Table.read(self.save_file)
- except:
- tmp = Table.read(self.save_file_legacy)
-
-
- # See if the meta-data matches: evo model, atm_func, redlaw
- if ( (tmp.meta['EVOMODEL'] == type(evo_model).__name__) &
- (tmp.meta['ATMFUNC'] == atm_func.__name__) &
- (tmp.meta['REDLAW'] == red_law.name) ):
- out_bool = True
-
- return out_bool
def plot_CMD(self, mag1, mag2, savefile=None):
"""
@@ -1103,7 +1382,7 @@ def plot_CMD(self, mag1, mag2, savefile=None):
mag2 : string
The name of the second magnitude column to be plotted.
savefile : string (default None)
- If a savefile is specified, then the plot will be saved to that file.
+ If a savefile is specified, then the plot will be saved to that file.
"""
plt.clf()
plt.plot(self.points[mag1] - self.points[mag2], self.points[mag1],
@@ -1111,7 +1390,7 @@ def plot_CMD(self, mag1, mag2, savefile=None):
plt.gca().invert_yaxis()
plt.xlabel(mag1 + ' - ' + mag2 + ' (mag)')
plt.ylabel(mag1 + ' (mag)')
-
+
fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}'
plt.title(fmt_title.format(self.points.meta['LOGAGE'],
self.points.meta['DISTANCE']/1e3,
@@ -1119,34 +1398,34 @@ def plot_CMD(self, mag1, mag2, savefile=None):
if savefile != None:
plt.savefig(savefile)
-
+
return
def plot_mass_magnitude(self, mag, savefile=None):
"""
Make a standard mass-luminosity relation plot for this isochrone.
-
+
Parameters
----------
mag : string
The name of the magnitude column to be plotted.
savefile : string (default None)
- If a savefile is specified, then the plot will be saved to that file.
+ If a savefile is specified, then the plot will be saved to that file.
"""
plt.clf()
plt.semilogx(self.points['mass'], self.points[mag], 'k.')
plt.gca().invert_yaxis()
plt.xlabel(r'Mass (M$_\odot$)')
plt.ylabel(mag + ' (mag)')
-
+
fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}'
plt.title(fmt_title.format(self.points.meta['LOGAGE'],
self.points.meta['DISTANCE']/1e3,
self.points.meta['AKS']))
-
+
if savefile != None:
plt.savefig(savefile)
-
+
return
#===================================================#
@@ -1168,7 +1447,7 @@ def __init__(self, logAge, distance, evo_model=default_evo_model,
Functions on this object:
apply_reddening (Apply reddening with defined redlaw and AKs)
make_photometry (make synthetic photometry for spectra)
-
+
Parameters
----------
logAge : float
@@ -1206,13 +1485,13 @@ def __init__(self, logAge, distance, evo_model=default_evo_model,
spectrum. This is very useful to save computation time down the
road.
"""
- t1 = time.time()
+ t1 = time.time()
c = constants
# Get solar metallicity models for a population at a specific age.
# Takes about 0.1 seconds.
- evol = evo_model.isochrone(age=10**logAge) # solar metallicity
-
+ evol = evo_model.isochrone(age=10**logAge) # solar metallicity
+
# Eliminate cases where log g is less than 0
idx = np.where(evol['logg'] > 0)
evol = evol[idx]
@@ -1223,8 +1502,8 @@ def __init__(self, logAge, distance, evo_model=default_evo_model,
evol = evol[idx]
if max_mass != None:
idx = np.where(evol['mass'] <= max_mass)
- evol = evol[idx]
-
+ evol = evol[idx]
+
# Trim down the table by selecting every Nth point where
# N = mass sampling factor.
evol = evol[::mass_sampling]
@@ -1259,30 +1538,31 @@ def __init__(self, logAge, distance, evo_model=default_evo_model,
# Get the atmosphere model now. Wavelength is in Angstroms
# This is the time-intensive call... everything else is negligable.
star = atm_func(temperature=T, gravity=gravity)
-
+
# Trim wavelength range down to JHKL range (0.5 - 5.2 microns)
star = spectrum.trimSpectrum(star, wave_range[0], wave_range[1])
# Convert into flux observed at Earth (unreddened)
star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1
-
- # Save the final spectrum to our spec_list for later use.
+
+ # Save the final spectrum to our spec_list for later use.
self.spec_list.append(star)
# Append all the meta data to the summary table.
-
+
tab.meta['ATMFUNC'] = atm_func.__name__
tab.meta['EVOMODEL'] = type(evo_model).__name__
+ tab.meta['EVOMODELVERSION'] = evo_model.model_version_name
tab.meta['LOGAGE'] = logAge
tab.meta['DISTANCE'] = distance
tab.meta['WAVEMIN'] = wave_range[0]
tab.meta['WAVEMAX'] = wave_range[1]
self.points = tab
-
+
t2 = time.time()
print('Isochrone generation took {0:f} s.'.format(t2-t1))
-
+
return
def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max=None):
@@ -1294,7 +1574,7 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max=
----------
AKs: float
Total extinction in AKs
-
+
extinction_law: SPISEA extinction object
Extinction law to be used on the spectra
@@ -1304,13 +1584,13 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max=
dAKs_max: float or None
If not none, defines the maximum |dAKs| a star can
- have in gaussian distribution case
+ have in gaussian distribution case
dist: string, 'uniform' or 'gaussian'
Distribution to draw differential reddening from. If uniform,
dAKs will cut off at Aks +/- dAKs. Otherwise, will draw
from Gaussian of width AKs +/- dAks
-
+
"""
self.AKs = np.ones(len(self.spec_list))
# Apply reddening to each object in the spec list
@@ -1321,25 +1601,25 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max=
# extinction law
if dAKs != 0:
if dist == 'gaussian':
- AKs_act = np.random.normal(loc=AKs, scale=dAKs)
+ AKs_act = self.rng.normal(loc=AKs, scale=dAKs)
# Apply dAKs_max if desired. Redo if diff > dAKs_max
if dAKs_max != None:
diff = abs(AKs_act - AKs)
while diff > dAKs_max:
print('While loop active')
- AKs_act = np.random.normal(loc=AKs, scale=dAKs)
+ AKs_act = self.rng.normal(loc=AKs, scale=dAKs)
diff = abs(AKs_act - AKs)
elif dist == 'uniform':
low = AKs - dAKs
high = AKs + dAKs
- AKs_act = np.random.uniform(low=low, high=high)
+ AKs_act = self.rng.uniform(low=low, high=high)
else:
print('dist {0} undefined'.format(dist))
return
else:
AKs_act = AKs
- red = extinction_law.reddening(AKs_act).resample(star.wave)
+ red = extinction_law.reddening(AKs_act).resample(star.wave)
star *= red
# Update the spectrum in spec list
@@ -1352,7 +1632,7 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max=
return
def make_photometry(self, filters, rebin=True):
- """
+ """
Make synthetic photometry for the specified filters. This function
udpates the self.points table to include new columns with the
photometry.
@@ -1362,11 +1642,11 @@ def make_photometry(self, filters, rebin=True):
filters : dictionary
A dictionary containing the filter name (for the output columns)
and the filter specification string that can be processed by pysynphot.
-
+
rebin: boolean
- True to rebin filter function (only used if non-zero transmission points are
+ True to rebin filter function (only used if non-zero transmission points are
larger than 1500 points)
-
+
"""
npoints = len(self.points)
@@ -1383,35 +1663,78 @@ def make_photometry(self, filters, rebin=True):
col_name = 'mag_' + filt_name
mag_col = Column(np.zeros(npoints, dtype=float), name=col_name)
self.points.add_column(mag_col)
-
+
# Loop through each star in the isochrone and do the filter integration
for ss in range(npoints):
star = self.spec_list[ss] # These are already extincted, observed spectra.
star_mag = mag_in_filter(star, filt)
-
+
self.points[col_name][ss] = star_mag
-
-
+
+
endTime = time.time()
print( ' Time taken: {0:.2f} seconds'.format(endTime - ts))
return
+def check_save_file(save_file_path, evo_model, atm_func, red_law, verbose=False):
+ """
+ Check to see if save_file exists, as saved by the save_file
+ and save_file_legacy objects. If the filename exists, check the
+ meta-data as well.
+
+ returns a boolean: True is file exists, false otherwise
+ """
+ out_bool = False
+
+ if not os.path.exists(save_file_path):
+ if verbose: print(f'Isochrone file {save_file_path} does not exist.')
+ return out_bool
+
+ tmp = Table.read(save_file_path)
+
+ # See if the meta-data matches: evo model, atm_func, redlaw
+ if ( (tmp.meta['EVOMODEL'] == type(evo_model).__name__) &
+ (tmp.meta['ATMFUNC'] == atm_func.__name__) &
+ (tmp.meta['REDLAW'] == red_law.name) ):
+ out_bool = True
+ else:
+ # If out_bool is false, print out what doesn't match
+ if verbose:
+ print(f'Isochrone file {save_file_path} exists, but meta-data does not match.')
+ if tmp.meta['EVOMODEL'] != type(evo_model).__name__:
+ print(f' EVOMODEL: {tmp.meta["EVOMODEL"]} != {type(evo_model).__name__}')
+ if tmp.meta['ATMFUNC'] != atm_func.__name__:
+ print(f' ATMFUNC: {tmp.meta["ATMFUNC"]} != {atm_func.__name__}')
+ if tmp.meta['REDLAW'] != red_law.name:
+ print(f' REDLAW: {tmp.meta["REDLAW"]} != {red_law.name}')
+
+ # Check model version if it was logged
+ if 'EVOMODELVERSION' in tmp.meta:
+ if tmp.meta['EVOMODELVERSION'] != evo_model.model_version_name:
+ out_bool=False
+ if verbose:
+ print(f'EVOMODELVERSION does not match: The recorded {tmp.meta["EVOMODELVERSION"]} does not matched the existing version {evo_model.model_version_name}')
+
+
+ return out_bool
+
+
def get_filter_info(name, vega=vega, rebin=True):
- """
+ """
Define filter functions, setting ZP according to
Vega spectrum. Input name is the SPISEA
obs_string
"""
tmp = name.split(',')
filterName = tmp[-1]
-
+
if name.startswith('nirc2'):
filt = filters.get_nirc2_filt(filterName)
elif name.startswith('2mass'):
filt = filters.get_2mass_filt(filterName)
-
+
elif name.startswith('vista'):
filt = filters.get_vista_filt(filterName)
@@ -1426,10 +1749,10 @@ def get_filter_info(name, vega=vega, rebin=True):
elif name.startswith('jg'):
filt = filters.get_Johnson_Glass_filt(filterName)
-
+
elif name.startswith('nirc1'):
filt = filters.get_nirc1_filt(filterName)
-
+
elif name.startswith('ctio_osiris'):
filt = filters.get_ctio_osiris_filt(filterName)
@@ -1441,7 +1764,7 @@ def get_filter_info(name, vega=vega, rebin=True):
elif name.startswith('ukirt'):
filt = filters.get_ukirt_filt(filterName)
-
+
elif name.startswith('keck_osiris'):
filt = filters.get_keck_osiris_filt(filterName)
@@ -1454,10 +1777,23 @@ def get_filter_info(name, vega=vega, rebin=True):
elif name.startswith('hawki'):
filt = filters.get_hawki_filt(filterName)
-
+
+ elif name.startswith('rubin'):
+ filt = filters.get_rubin_filt(filterName)
+
+ elif name.startswith('euclid'):
+ filt = filters.get_euclid_filt(filterName)
+
+ elif name.startswith('nsfcam'):
+ filt = filters.get_nsfcam_filt(filterName)
+
else:
- filt = ObsBandpass(name)
-
+ # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files
+ try:
+ filt = ObsBandpass(name)
+ except:
+ raise Exception('Filter {0} not understood. Check spelling and make sure cdbs/mtab and cdbs/comp files are up to date'.format(name))
+
# Convert to ArraySpectralElement for resampling.
filt = spectrum.ArraySpectralElement(filt.wave, filt.throughput,
waveunits=filt.waveunits,
@@ -1475,14 +1811,14 @@ def get_filter_info(name, vega=vega, rebin=True):
# Otherwise, throw an error
idx = np.where(filt.throughput > 0.001)[0]
if (min(filt.wave[idx]) < min(vega.wave)) | (max(filt.wave[idx]) > max(vega.wave)):
- raise ValueError('Vega spectrum doesnt cover filter wavelength range!')
+ raise ValueError('Vega spectrum doesnt cover filter wavelength range!')
vega_obs = obs.Observation(vega, filt, binset=filt.wave, force='taper')
#vega_flux = vega_obs.binflux.sum()
diff = np.diff(vega_obs.binwave)
diff = np.append(diff, diff[-1])
vega_flux = np.sum(vega_obs.binflux * diff)
-
+
vega_mag = 0.03
filt.flux0 = vega_flux
@@ -1490,9 +1826,10 @@ def get_filter_info(name, vega=vega, rebin=True):
return filt
+
def get_filter_col_name(obs_str):
"""
- Get standard column name for synthetic photometry based on
+ Get standard column name for synthetic photometry based on
the input string. The input string is expected to be an
appropriate SPISEA obs_string
"""
@@ -1505,11 +1842,13 @@ def get_filter_col_name(obs_str):
# Catch Gaia filter cases. Otherwise, it is HST filter
if 'dr2_rev' in tmp:
filt_name = 'gaiaDR2_{0}'.format(tmp[-1])
+ elif 'roman' in tmp:
+ filt_name = 'roman_{0}'.format(tmp[-1])
else:
filt_name = 'hst_{0}'.format(tmp[-1])
else:
filt_name = '{0}_{1}'.format(tmp[0], tmp[1])
-
+
return filt_name
def get_obs_str(col):
@@ -1519,7 +1858,7 @@ def get_obs_str(col):
"""
# Remove the trailing m_
name = col[2:]
-
+
# Define dictionary for filters
filt_list = {'hst_f127m': 'wfc3,ir,f127m', 'hst_f139m': 'wfc3,ir,f139m', 'hst_f153m': 'wfc3,ir,f153m',
'hst_f814w': 'acs,wfc1,f814w', 'hst_f125w': 'wfc3,ir,f125w', 'hst_f160w': 'wfc3,ir,f160w',
@@ -1541,7 +1880,7 @@ def get_obs_str(col):
'jwst_F187N': 'jwst,F187N',
'jwst_F200W': 'jwst,F200W',
'jwst_F210M': 'jwst,F210M',
- 'jwst_F250M': 'jwst,F250M',
+ 'jwst_F250M': 'jwst,F250M',
'jwst_F277W': 'jwst,F277W',
'jwst_F300M': 'jwst,F300M',
'jwst_F322W2': 'jwst,F322W2',
@@ -1561,10 +1900,14 @@ def get_obs_str(col):
'nirc2_FeII': 'nirc2,FeII', 'nirc2_Brgamma': 'nirc2,Brgamma',
'2mass_J': '2mass,J', '2mass_H': '2mass,H', '2mass_Ks': '2mass,Ks',
'ubv_U':'ubv,U', 'ubv_B':'ubv,B', 'ubv_V':'ubv,V', 'ubv_R':'ubv,R',
- 'ubv_I':'ubv,I',
+ 'ubv_I':'ubv,I',
'jg_J': 'jg,J', 'jg_H': 'jg,H', 'jg_K': 'jg,K',
'nirc1_K':'nirc1,K', 'nirc1_H':'nirc1,H',
'naco_J':'naco,J', 'naco_H':'naco,H', 'naco_Ks':'naco,Ks',
+ 'naco_IB_2.00': 'naco,IB_2.00', 'naco_IB_2.03':'naco,IB_2.03', 'naco_IB_2.06':'naco,IB_2.06',
+ 'naco_IB_2.24':'naco,IB_2.24', 'naco_IB_2.27':'naco,IB_2.27',
+ 'naco_IB_2.30':'naco,IB_2.30', 'naco_IB_2.33':'naco,IB_2.33',
+ 'naco_IB_2.36':'naco,IB_2.36',
'ukirt_J':'ukirt,J', 'ukirt_H':'ukirt,H', 'ukirt_K':'ukirt,K',
'ctio_osiris_H': 'ctio_osiris,H', 'ctio_osiris_K': 'ctio_osiris,K',
'ztf_g':'ztf,g', 'ztf_r':'ztf,r', 'ztf_i':'ztf,i',
@@ -1572,10 +1915,29 @@ def get_obs_str(col):
'gaiaDR2_Grp':'gaia,dr2_rev,Grp',
'hawki_J': 'hawki,J',
'hawki_H': 'hawki,H',
- 'hawki_Ks': 'hawki,Ks'}
+ 'hawki_Ks': 'hawki,Ks',
+ 'roman_f062': 'roman,wfi,f062',
+ 'roman_f087': 'roman,wfi,f087',
+ 'roman_f106': 'roman,wfi,f106',
+ 'roman_f129': 'roman,wfi,f129',
+ 'roman_f158': 'roman,wfi,f158',
+ 'roman_f146': 'roman,wfi,f146',
+ 'roman_f213': 'roman,wfi,f213',
+ 'roman_f184': 'roman,wfi,f184',
+ 'rubin_g':'rubin,g',
+ 'rubin_i':'rubin,i',
+ 'rubin_r':'rubin,r',
+ 'rubin_u':'rubin,u',
+ 'rubin_z':'rubin,z',
+ 'rubin_y':'rubin,y',
+ 'euclid_VIS':'euclid,VIS',
+ 'euclid_Y':'euclid,Y',
+ 'euclid_J':'euclid,J',
+ 'euclid_H':'euclid,H',
+ 'nsfcam_L':'nsfcam,L'}
obs_str = filt_list[name]
-
+
return obs_str
def rebin_spec(wave, specin, wavnew):
@@ -1588,7 +1950,7 @@ def rebin_spec(wave, specin, wavnew):
f = np.ones(len(wave))
filt = spectrum.ArraySpectralElement(wave, f, waveunits='angstrom')
obs_f = obs.Observation(spec, filt, binset=wavnew, force='taper')
-
+
return obs_f.binflux
def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model,
@@ -1599,7 +1961,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model,
'wfc3,ir,f153m']):
"""
Wrapper routine to generate a grid of isochrones of different ages,
- extinctions, and distances.
+ extinctions, and distances.
Parameters:
----------
@@ -1611,7 +1973,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model,
dist_arr: array
Array of distances to loop over (pc)
-
+
evo_models: SPISEA evolution object
Which evolution models to use
@@ -1628,7 +1990,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model,
Mass sampling of isochrone, relative to original mass sampling
filters: dictionary
- Which filters to do the synthetic photometry on
+ Which filters to do the synthetic photometry on
"""
print( '**************************************')
print( 'Start generating isochrones')
@@ -1673,7 +2035,7 @@ def mag_in_filter(star, filt):
diff = np.diff(star_in_filter.binwave)
diff = np.append(diff, diff[-1])
star_flux = np.sum(star_in_filter.binflux * diff)
-
+
star_mag = -2.5 * math.log10(star_flux / filt.flux0) + filt.mag0
return star_mag
@@ -1696,10 +2058,10 @@ def match_model_masses(isoMasses, starMasses):
idx = np.where(dm_frac > 0.1)[0]
indices[idx] = -1
-
+
return indices
-
+
def get_evo_model_by_string(evo_model_string):
return getattr(evolution, evo_model_string)
@@ -1710,7 +2072,7 @@ def calc_ab_vega_filter_conversion(filt_str):
AB and Vega magnitudes for a given filter:
m_AB - m_vega
- Note: this conversion is just the vega magnitude in
+ Note: this conversion is just the vega magnitude in
AB system
Parameters:
@@ -1730,14 +2092,14 @@ def calc_ab_vega_filter_conversion(filt_str):
filt_wave = filt.wave
filt_mu = c / filt_wave
s_filt = filt.throughput
-
+
# Interpolate the filter function, determine what the
# filter function is at the exact sampling of the
# vega spectrum (in freq space)
filt_interp = scipy.interpolate.interp1d(filt_mu, s_filt, kind='linear', bounds_error=False,
fill_value=0)
s_interp = filt_interp(vega_mu)
-
+
# Now for the m_ab calculation
mu_diff = np.diff(vega_mu)
numerator = np.sum(vega_flux_mu[:-1] * s_interp[:-1] * mu_diff)
@@ -1755,10 +2117,10 @@ def calc_ab_vega_filter_conversion(filt_str):
# fill_value=0)
#s_interp = filt_interp(vega.wave)
- # Calculate the numerator
+ # Calculate the numerator
#diff = np.diff(vega.wave)
#numerator2 = np.sum((vega.wave[:-1]**2. / c) * vega.flux[:-1] * s_interp[:-1] * diff)
-
+
# Now we need to intergrate the filter response for the denominator
#denominator2 = np.sum(s_interp[:-1] * diff)
@@ -1767,3 +2129,42 @@ def calc_ab_vega_filter_conversion(filt_str):
return vega_mag_ab
+def calc_st_vega_filter_conversion(filt_str):
+ """
+ Function to calculate the conversion between
+ ST and Vega magnitudes for a given filter:
+ m_ST - m_vega
+
+ Note: this conversion is just the vega magnitude in
+ ST system
+
+ Parameters:
+ -----------
+ filt_str: string
+ Filter identification string
+ """
+ # Get filter info
+ filt = get_filter_info(filt_str)
+
+ # Interpolate the filter function to be the exact same sampling as the
+ # vega spectrum
+ c = 2.997*10**18 # A / s
+ filt_interp = scipy.interpolate.interp1d(filt.wave, filt.throughput, kind='linear', bounds_error=False,
+ fill_value=0)
+ s_interp = filt_interp(vega.wave)
+
+ # Calculate the numerator
+ diff = np.diff(vega.wave)
+ numerator = np.sum(vega.flux[:-1] * s_interp[:-1] * diff)
+
+ # Now we need to intergrate the filter response for the denominator
+ denominator = np.sum(s_interp[:-1] * diff)
+ # Fλ must be in erg cm–2 sec–1 Å–1
+
+ # Calculate vega AB magnitude. This is the conversion
+ vega_mag_st = -2.5 * np.log10(numerator / denominator) - 21.1
+
+ print('For {0}, m_st - m_vega = {1}'.format(filt_str, vega_mag_st))
+
+ return vega_mag_st
+
diff --git a/spisea/tests/__init__.py b/spisea/tests/__init__.py
index 9319f894..45d1df25 100755
--- a/spisea/tests/__init__.py
+++ b/spisea/tests/__init__.py
@@ -2,20 +2,3 @@
"""
This packages contains package tests.
"""
-
-def test_evolution_merged_pisa_ekstrom():
- """Test the MergedPisaEkstromParsec() class in
- evolution.py
- """
-
- from popstar import evolution
-
- evo = evolution.MergedPisaEkstromParsec()
-
- iso1 = evo.isochrone(age=3e6)
- iso2 = evo.isochrone(age=5e6)
-
- # Check that the younger isochrone has a higher mass.
- assert iso1['mass'].max() > iso2['mass'].max()
-
- return
diff --git a/spisea/tests/test_data/companions.pkl b/spisea/tests/test_data/companions.pkl
new file mode 100644
index 00000000..0395d75d
Binary files /dev/null and b/spisea/tests/test_data/companions.pkl differ
diff --git a/spisea/tests/test_data/star_systems.pkl b/spisea/tests/test_data/star_systems.pkl
new file mode 100644
index 00000000..0cbdd246
Binary files /dev/null and b/spisea/tests/test_data/star_systems.pkl differ
diff --git a/spisea/tests/test_exceptions.py b/spisea/tests/test_exceptions.py
new file mode 100644
index 00000000..b20a132d
--- /dev/null
+++ b/spisea/tests/test_exceptions.py
@@ -0,0 +1,37 @@
+from spisea import evolution, exceptions
+import copy
+import os
+import pdb
+
+
+def test_grid_number_exception():
+ """
+ Make sure check_evo_grid_number works as expected
+ """
+ #----Case 1: installed model grid is too low (raise error)----#
+ # Get installed grid num
+ models_dir = '{0}/evolution/'.format(os.environ['SPISEA_MODELS'])
+ installed_grid = evolution.get_installed_grid_num(models_dir)
+
+ # Make sure required grid is too large
+ required_grid = installed_grid + 1
+
+ # Make sure correct error is raised, otherwise fail
+ try:
+ evolution.check_evo_grid_number(required_grid, models_dir)
+ except exceptions.ModelMismatch:
+ pass
+ else:
+ raise Exception('check_evo_grid_number failed')
+
+ # Case 2: installed model grid is same (no error)
+ required_grid = copy.deepcopy(installed_grid)
+
+ evolution.check_evo_grid_number(required_grid, models_dir)
+
+ # Case 3: installed model grid is higher than required grid (no error)
+ required_grid = installed_grid - 1.0
+
+ evolution.check_evo_grid_number(required_grid, models_dir)
+
+ return
\ No newline at end of file
diff --git a/spisea/imf/tests/test_imf.py b/spisea/tests/test_imf.py
similarity index 77%
rename from spisea/imf/tests/test_imf.py
rename to spisea/tests/test_imf.py
index 1c5fac06..5f465747 100755
--- a/spisea/imf/tests/test_imf.py
+++ b/spisea/tests/test_imf.py
@@ -1,12 +1,10 @@
import numpy as np
-import nose.tools
import time
import pdb
+import cProfile
+from spisea.imf import imf, multiplicity
def test_generate_cluster():
- from .. import imf
- from .. import multiplicity
-
# Make multiplicity object
imf_multi = multiplicity.MultiplicityUnresolved()
@@ -33,8 +31,6 @@ def test_generate_cluster():
return
def test_prim_power():
- from .. import imf
-
#mass_limits = np.array([0.1, 1.0, 100.0])
#powers = np.array([-2.0, -1.8])
mass_limits = np.array([1.0, 100.0])
@@ -61,9 +57,6 @@ def test_prim_power():
return
def test_xi():
- from .. import imf
-
- import cProfile, pstats, io
#from pstats import SortKey
pr = cProfile.Profile()
@@ -73,14 +66,14 @@ def test_xi():
##########
#
- # Test validity of returned values.
- #
+ # Test validity of returned values.
+ #
##########
N_size = 10
m = np.linspace(0.2, 20, N_size)
val_good = np.array([1.6206566 , 0.26895718, 0.10135922, 0.05639448, 0.03703704,
0.0243668 , 0.01613091, 0.01137139, 0.00839526, 0.00642142])
-
+
val_test = np.zeros(len(m), dtype=float)
for ii in range(len(m)):
val_test[ii] = imf_tmp.xi(m[ii])
@@ -91,33 +84,90 @@ def test_xi():
##########
#
# Performance testing
- #
+ #
##########
t1 = time.time()
# pr.enable()
-
+
# Run a time test
N_size = int(1e4)
m = np.random.uniform(1.1, 99.0, size=N_size)
foo1 = imf_tmp.xi(m)
-
+
# pr.disable()
t2 = time.time()
print('test_xi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size))
-
+
# s = io.StringIO()
# sortby = SortKey.CUMULATIVE
# ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
# ps.print_stats()
- # print(s.getvalue())
+ # print(s.getvalue())
return
-def test_mxi():
- from .. import imf
+def test_xi2():
+ """
+ Test that xi() produces the correct probability for
+ a given slope.
+ """
+ #from pstats import SortKey
+ pr = cProfile.Profile()
+
+ # Negative slope
+ mass_limits = np.array([1.0, 10.0])
+ powers = np.array([-2.0])
+ imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers)
+ imf_tmp.normalize(1e4)
+
+ tmp = np.random.rand(100)
+ masses = imf_tmp.dice_star_cl(tmp)
+
+ pdf = imf_tmp.xi(masses)
+
+ sdx = masses.argsort()
+
+ pdf_sorted = pdf[sdx]
+
+ print('Testing negative slope')
+ assert pdf_sorted[0] > pdf_sorted[-1]
+ assert pdf_sorted[0] > pdf_sorted[1]
+ assert pdf.max() == pdf_sorted[0]
+
+ # Positive slope
+ mass_limits2 = np.array([1.0, 10.0])
+ powers2 = np.array([2.0])
+ imf_tmp2 = imf.IMF_broken_powerlaw(mass_limits2, powers2)
+ imf_tmp2.normalize(1e4)
+
+ tmp2 = np.random.rand(100)
+ masses2 = imf_tmp2.dice_star_cl(tmp2)
+
+ pdf2 = imf_tmp2.xi(masses2)
+
+ sdx2 = masses2.argsort()
- import cProfile, pstats, io
+ pdf_sorted2 = pdf2[sdx2]
+
+ print('Testing positive slope')
+ assert pdf_sorted2[0] < pdf_sorted2[-1]
+ assert pdf_sorted2[0] < pdf_sorted2[1]
+ assert pdf2.min() == pdf_sorted2[0]
+
+
+ import pylab as plt
+ plt.clf()
+ plt.loglog(masses[sdx], pdf_sorted, 'k.')
+ plt.axis('equal')
+ # pdb.set_trace()
+
+ return
+
+
+
+
+def test_mxi():
#from pstats import SortKey
pr = cProfile.Profile()
@@ -127,8 +177,8 @@ def test_mxi():
##########
#
- # Test validity of returned values.
- #
+ # Test validity of returned values.
+ #
##########
N_size = 10
m = np.linspace(0.2, 20, N_size)
@@ -145,11 +195,11 @@ def test_mxi():
##########
#
# Performance testing
- #
+ #
##########
t1 = time.time()
# pr.enable()
-
+
# Run a time test
N_size = int(1e4)
m = np.random.uniform(1.1, 99.0, size=N_size)
@@ -160,29 +210,26 @@ def test_mxi():
t2 = time.time()
print('test_mxi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size))
-
+
# s = io.StringIO()
# sortby = SortKey.CUMULATIVE
# ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
# ps.print_stats()
- # print(s.getvalue())
+ # print(s.getvalue())
return
def test_theta_closed():
- from .. import imf
-
- import cProfile, pstats, io
#from pstats import SortKey
-
+
mass_limits = np.array([0.1, 1.0, 10.0, 100.0])
powers = np.array([-0.3, -1.5, -2.3])
imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers)
##########
#
- # Test validity of returned values.
- #
+ # Test validity of returned values.
+ #
##########
N_size = 10
m = np.linspace(0.2, 20, N_size)
@@ -203,22 +250,22 @@ def test_theta_closed():
np.testing.assert_equal(val_test, val_good)
-
+
##########
#
# Speed tests and performance profiling.
- #
+ #
##########
N_size = 10000
m = np.linspace(1.1, 99, N_size)
-
+
tmp = np.zeros((len(m), len(powers)), dtype=float)
t1 = time.time()
# pr = cProfile.Profile()
# pr.enable()
-
+
for ii in range(len(m)):
tmp[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low)
@@ -226,7 +273,7 @@ def test_theta_closed():
t2 = time.time()
print('Runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size))
-
+
# s = io.StringIO()
# sortby = SortKey.CUMULATIVE
# ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
@@ -235,4 +282,4 @@ def test_theta_closed():
return
-
+
diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py
index 27c66ff2..4ba0e683 100644
--- a/spisea/tests/test_models.py
+++ b/spisea/tests/test_models.py
@@ -1,30 +1,44 @@
# Test functions for the different stellar evolution and atmosphere models
+from spisea import evolution, atmospheres, synthetic
+import numpy as np
import pdb
+def test_evo_model_grid_num():
+ """
+ Make sure evolution models have both evo_grid_num
+ and evo_grid_min (e.g., make sure these functions
+ are working). Try it on one evolution model here;
+ we'll test on all evo models in another function.
+ """
+ # Make MIST evolution model, check evo grid variables
+ evo = evolution.MISTv1()
+ assert isinstance(evo.evo_grid_min, float)
+
+ return
+
def test_evolution_models():
"""
Test to make sure the different evolution models work
"""
- from spisea import evolution
-
# Age ranges to test
age_young_arr = [6.7, 7.9]
age_all_arr = [6.7, 8.0, 9.7]
+ age_all_MIST_arr = [5.2, 6.7, 9.7, 10.13]
# Metallicity ranges to test (if applicable)
- metal_range = [-2.5, 0, 0.4]
+ metal_range = [-2.5, -1.5, 0, 0.25, 0.4]
metal_solar = [0]
# Array of evolution models to test
- evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), evolution.MergedSiessGenevaPadova(),
+ evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(),
evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa()]
-
+
# Array of age_ranges for the specific evolution models to test
- age_vals = [age_all_arr, age_all_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr]
+ age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr]
# Array of metallicities for the specific evolution models to test
- metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar]
+ metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar]
assert len(evo_models) == len(age_vals) == len(metal_vals)
@@ -38,22 +52,56 @@ def test_evolution_models():
for kk in age_vals[ii]:
try:
test = evo.isochrone(age=10**kk, metallicity=jj)
+
+ # Make sure the actual isochrone metallicity taken is
+ # indeed the closest to the desired metallicity (e.g., closest point
+ # in evo grid)
+ z_ratio = np.log10(np.array(evo.z_list) / evo.z_solar)
+ closest_idx = np.where( abs(z_ratio - jj) == min(abs(z_ratio - jj)) )[0][0]
+ expected_val = z_ratio[closest_idx]
+
+ assert np.isclose(test.meta['metallicity_act'], expected_val, atol=0.01)
+
except:
raise Exception('EVO TEST FAILED: {0}, age = {1}, metal = {2}'.format(evo, kk, jj))
print('Done {0}'.format(evo))
-
+
+ return
+
+def test_synthpop_MIST_extension():
+ """
+ Testing the synthpop MIST extension to consistently lower masses
+ """
+ evo1_grid = evolution.MISTv1(version=1.2, synthpop_extension=False)
+ evo2_grid = evolution.MISTv1(version=1.2, synthpop_extension=True)
+
+ # Extract same isochrone from these two models
+ logAge = 10.12
+ evo1 = evo1_grid.isochrone(10**logAge, metallicity=0)
+ evo2 = evo2_grid.isochrone(10**logAge, metallicity=0)
+
+ # I expect evo2 extends to low masses velow evo1
+ assert len(evo2) > len(evo1)
+ assert np.min(evo2['mass']) < np.min(evo1['mass'])
+
return
def test_atmosphere_models():
"""
Test the rebinned atmosphere models used for synthetic photometry
"""
- from spisea import atmospheres as atm
-
# Array of atmospheres
- atm_arr = [atm.get_merged_atmosphere, atm.get_castelli_atmosphere, atm.get_phoenixv16_atmosphere, atm.get_BTSettl_2015_atmosphere,
- atm.get_BTSettl_atmosphere, atm.get_kurucz_atmosphere, atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere]
+ atm_arr = [
+ atmospheres.get_merged_atmosphere,
+ atmospheres.get_castelli_atmosphere,
+ atmospheres.get_phoenixv16_atmosphere,
+ atmospheres.get_BTSettl_2015_atmosphere,
+ atmospheres.get_BTSettl_atmosphere,
+ atmospheres.get_kurucz_atmosphere,
+ atmospheres.get_phoenix_atmosphere,
+ atmospheres.get_wdKoester_atmosphere
+ ]
# Array of metallicities
metals_range = [-2.0, 0, 0.15]
@@ -72,12 +120,12 @@ def test_atmosphere_models():
test = atm_func(metallicity=jj)
except:
raise Exception('ATM TEST FAILED: {0}, metal = {1}'.format(atm_func, jj))
-
+
print('Done {0}'.format(atm_func))
-
- # One last test: get_merged_atmospheres at different temps
+
+ # Test get_merged_atmospheres at different temps
temp_range = [2000, 3500, 4000, 5250, 6000, 12000]
- atm_func = atm.get_merged_atmosphere
+ atm_func = atmospheres.get_merged_atmosphere
for ii in metals_range:
for jj in temp_range:
try:
@@ -88,17 +136,27 @@ def test_atmosphere_models():
print('get_merged_atmosphere: all temps/metallicities passed')
+ # Test get_bb_atmosphere at different temps
+ # This func only requests temp
+ temp_range = [2000, 3500, 4000, 5250, 6000, 12000]
+ atm_func = atmospheres.get_bb_atmosphere
+ for jj in temp_range:
+ try:
+ test = atm_func(temperature=jj, verbose=True)
+ except:
+ raise Exception('ATM TEST FAILED: {0}, temp = {2}'.format(atm_func, jj))
+
+ print('get_bb_atmosphere: all temps passed')
+
return
def test_filters():
"""
Test to make sure all of the filters work as expected
"""
- from spisea import synthetic
-
# Define vega spectrum
vega = synthetic.Vega()
-
+
# Filter list to test
filt_list = ['wfc3,ir,f127m','acs,wfc1,f814w',
'2mass,J', '2mass,H','2mass,Ks',
@@ -116,6 +174,8 @@ def test_filters():
'jwst,F360M', 'jwst,F405N', 'jwst,F410M', 'jwst,F430M',
'jwst,F444W', 'jwst,F460M', 'jwst,F466N', 'jwst,F470N',
'jwst,F480M', 'naco,J', 'naco,H', 'naco,Ks',
+ 'naco,IB_2.00','naco,IB_2.03', 'naco,IB_2.06', 'naco,IB_2.24',
+ 'naco,IB_2.27', 'naco,IB_2.30', 'naco,IB_2.33', 'naco,IB_2.36',
'nirc1,K', 'nirc1,H', 'nirc2,J', 'nirc2,H',
'nirc2,Kp', 'nirc2,K', 'nirc2,Lp', 'nirc2,Hcont',
'nirc2,FeII', 'nirc2,Brgamma', 'ps1,z',
@@ -123,28 +183,28 @@ def test_filters():
'ukirt,J', 'ukirt,H', 'ukirt,K',
'vista,Y', 'vista,Z', 'vista,J',
'vista,H', 'vista,Ks', 'ztf,g', 'ztf,r', 'ztf,i',
- 'hawki,J', 'hawki,H', 'hawki,Ks']
+ 'hawki,J', 'hawki,H', 'hawki,Ks', 'roman,wfi,f062',
+ 'roman,wfi,f087', 'roman,wfi,f106', 'roman,wfi,f129',
+ 'roman,wfi,f158', 'roman,wfi,f146', 'roman,wfi,f213',
+ 'roman,wfi,f184', 'rubin,g', 'rubin,i', 'rubin,r',
+ 'rubin,u', 'rubin,z', 'rubin,y',
+ 'euclid,VIS', 'euclid,Y', 'euclid,J', 'euclid,H',
+ 'nsfcam,L']
# Loop through filters to test that they work: get_filter_info
for ii in filt_list:
- try:
- filt = synthetic.get_filter_info(ii, rebin=True, vega=vega)
- except:
- raise Exception('get_filter_info TEST FAILED for {0}'.format(ii))
+ filt = synthetic.get_filter_info(ii, rebin=True, vega=vega)
print('get_filter_info pass')
-
+
# Loop through filters to test that they work: get_obs_str
for ii in filt_list:
- try:
- # Test going from col_name to obs_str
- col_name = synthetic.get_filter_col_name(ii)
- obs_str = synthetic.get_obs_str('m_{0}'.format(col_name))
- # Does the obs_str work?
- filt_info = synthetic.get_filter_info(obs_str)
- except:
- raise Exception('get_obs_str TEST FAILED for {0}'.format(ii))
-
+ # Test going from col_name to obs_str
+ col_name = synthetic.get_filter_col_name(ii)
+ obs_str = synthetic.get_obs_str('m_{0}'.format(col_name))
+ # Does the obs_str work?
+ filt_info = synthetic.get_filter_info(obs_str)
+
print('get_obs_str pass')
print('Filters done')
diff --git a/spisea/imf/tests/test_multiplicity.py b/spisea/tests/test_multiplicity.py
similarity index 74%
rename from spisea/imf/tests/test_multiplicity.py
rename to spisea/tests/test_multiplicity.py
index fe89239a..9df0776d 100755
--- a/spisea/imf/tests/test_multiplicity.py
+++ b/spisea/tests/test_multiplicity.py
@@ -1,13 +1,12 @@
import numpy as np
-import nose.tools
import time
-
+import spisea
+from spisea.imf import imf, multiplicity
+
def test_create_MultiplicityUnresolved():
"""
Tests creating and accessing a MultiplicityUnresolved object.
"""
- from .. import multiplicity
-
# All default parameters -- check their values
mu1 = multiplicity.MultiplicityUnresolved()
assert mu1.MF_amp == 0.44
@@ -19,53 +18,51 @@ def test_create_MultiplicityUnresolved():
assert mu1.q_min == 0.01
# Test setting different parameters
- mu2 = multiplicity.MultiplicityUnresolved(MF_amp=0.4,
+ mu2 = multiplicity.MultiplicityUnresolved(MF_amp=0.4,
MF_power=0.4,
- CSF_amp=0.4,
- CSF_power=0.4,
+ CSF_amp=0.4,
+ CSF_power=0.4,
CSF_max=4,
- q_power=0.4,
+ q_power=0.4,
q_min=0.04)
- assert mu2.MF_amp == 0.4
+ assert mu2.MF_amp == 0.4
assert mu2.MF_pow == 0.4
- assert mu2.CSF_amp == 0.4
- assert mu2.CSF_pow == 0.4
+ assert mu2.CSF_amp == 0.4
+ assert mu2.CSF_pow == 0.4
assert mu2.CSF_max == 4
- assert mu2.q_pow == 0.4
+ assert mu2.q_pow == 0.4
assert mu2.q_min == 0.04
def test_multiplicity_fraction():
"""
Test creating a MultiplicityUnresolved object and getting
the multiplicity fraction out.
- """
- from spisea.imf import multiplicity
-
+ """
# First set of multiplicity parameters
mu1 = multiplicity.MultiplicityUnresolved()
mf1_1 = mu1.multiplicity_fraction(1.0)
- nose.tools.assert_almost_equal(mf1_1, 0.44, places=2)
+ np.testing.assert_almost_equal(mf1_1, 0.44, decimal=2)
mf1_2 = mu1.multiplicity_fraction(10.0)
- nose.tools.assert_almost_equal(mf1_2, 1.0, places=2)
+ np.testing.assert_almost_equal(mf1_2, 1.0, decimal=2)
mf1_3 = mu1.multiplicity_fraction(0.1)
- nose.tools.assert_almost_equal(mf1_3, 0.136, places=2)
+ np.testing.assert_almost_equal(mf1_3, 0.136, decimal=2)
# Second set of multiplicity parameters
mu2 = multiplicity.MultiplicityUnresolved(MF_amp=0.4, MF_power=0.4,
CSF_amp=0.4, CSF_power=0.4, CSF_max=4,
q_power=0.4, q_min=0.04)
- mf2_1 = mu1.multiplicity_fraction(1.0)
- nose.tools.assert_almost_equal(mf2_1, 0.44, places=2)
+ mf2_1 = mu2.multiplicity_fraction(1.0)
+ np.testing.assert_almost_equal(mf2_1, 0.4, decimal=2)
- mf2_2 = mu1.multiplicity_fraction(10.0)
- nose.tools.assert_almost_equal(mf2_2, 1.0, places=2)
+ mf2_2 = mu2.multiplicity_fraction(10.0)
+ np.testing.assert_almost_equal(mf2_2, 1.0, decimal=2)
- mf2_3 = mu1.multiplicity_fraction(0.1)
- nose.tools.assert_almost_equal(mf2_3, 0.136, places=2)
+ mf2_3 = mu2.multiplicity_fraction(0.1)
+ np.testing.assert_almost_equal(mf2_3, 0.159, decimal=2)
def test_multiplicity_fraction_array():
@@ -73,36 +70,32 @@ def test_multiplicity_fraction_array():
Test multiplicity_fraction() on the MultiplicityUnresolved object
where the inputs and outputs are in array form.
"""
- from spisea.imf import multiplicity
-
# First set of multiplicity parameters
mu1 = multiplicity.MultiplicityUnresolved()
mass_array = np.array([1.0, 10.0, 0.1])
mf_array = mu1.multiplicity_fraction(mass_array)
- nose.tools.assert_almost_equal(mf_array[0], 0.44, places=2)
- nose.tools.assert_almost_equal(mf_array[1], 1.0, places=2)
- nose.tools.assert_almost_equal(mf_array[2], 0.136, places=2)
-
-
+ np.testing.assert_almost_equal(mf_array[0], 0.44, decimal=2)
+ np.testing.assert_almost_equal(mf_array[1], 1.0, decimal=2)
+ np.testing.assert_almost_equal(mf_array[2], 0.136, decimal=2)
+
+
def test_companion_star_fraction():
"""
Test the companion_star fraction on the MultiplicityUnresolved object.
"""
- from spisea.imf import multiplicity
-
# First set of multiplicity parameters
mu1 = multiplicity.MultiplicityUnresolved()
csf1_1 = mu1.companion_star_fraction(1.0)
- nose.tools.assert_almost_equal(csf1_1, 0.5, places=2)
+ np.testing.assert_almost_equal(csf1_1, 0.5, decimal=2)
csf1_2 = mu1.companion_star_fraction(70.0)
- nose.tools.assert_almost_equal(csf1_2, 3.0, places=2)
+ np.testing.assert_almost_equal(csf1_2, 3.0, decimal=2)
csf1_3 = mu1.companion_star_fraction(0.1)
- nose.tools.assert_almost_equal(csf1_3, 0.177, places=2)
+ np.testing.assert_almost_equal(csf1_3, 0.177, decimal=2)
# Second set of multiplicity parameters
mu2 = multiplicity.MultiplicityUnresolved(MF_amp=0.4, MF_power=0.4,
@@ -110,23 +103,21 @@ def test_companion_star_fraction():
q_power=0.4, q_min=0.04)
# csf2_1 = mu1.companion_star_fraction(1.0)
- # nose.tools.assert_almost_equal(csf2_1, 0.4, places=2)
+ # np.testing.assert_almost_equal(csf2_1, 0.4, decimal=2)
# csf2_2 = mu1.companion_star_fraction(70.0)
- # nose.tools.assert_almost_equal(csf2_2, 2.0, places=2)
+ # np.testing.assert_almost_equal(csf2_2, 2.0, decimal=2)
# csf2_3 = mu1.companion_star_fraction(0.1)
- # nose.tools.assert_almost_equal(csf2_3, 0.159, places=2)
+ # np.testing.assert_almost_equal(csf2_3, 0.159, decimal=2)
def test_resolvedmult():
"""
- Test creating a MultiplicityResolvedDK object
+ Test creating a MultiplicityResolvedDK object
and that the parameters it's populated with are correct.
"""
from spisea import synthetic, evolution, atmospheres, reddening, ifmr
- from spisea.imf import imf, multiplicity
-
# Fetch isochrone
logAge = 6.70 # Age in log(years)
AKs = 1.0 # Ks filter extinction in mags
@@ -136,56 +127,54 @@ def test_resolvedmult():
evo_merged = evolution.MISTv1()
redlaw = reddening.RedLawCardelli(3.1) # Rv = 3.1
filt_list = ['nirc2,J', 'nirc2,Kp']
-
+
startTime = time.time()
iso_merged = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=metallicity,
evo_model=evo_merged, atm_func=atm_func,
filters=filt_list, red_law=redlaw,
- mass_sampling=3)
+ mass_sampling=3, iso_dir=f'{spisea.__path__[0]}/tests/isochrones')
print('Constructed isochrone: %d seconds' % (time.time() - startTime))
-
- # Now we can make the cluster.
+
+ # Now we can make the cluster.
clust_mtot = 10**4.
clust_multiplicity = multiplicity.MultiplicityResolvedDK()
# Multiplicity is defined in the IMF object
clust_imf_Mult = imf.Kroupa_2001(multiplicity=clust_multiplicity)
-
+
# Make clusters
clust_Mult = synthetic.ResolvedCluster(iso_merged, clust_imf_Mult, clust_mtot)
clust_Mult_ss = clust_Mult.star_systems
-
+
print('Constructed cluster: %d seconds' % (time.time() - startTime))
-
+
#check if columns were created
assert 'log_a' in clust_Mult.companions.colnames
assert 'e' in clust_Mult.companions.colnames
assert 'i' in clust_Mult.companions.colnames
assert 'Omega' in clust_Mult.companions.colnames
assert 'omega' in clust_Mult.companions.colnames
-
+
#check values are in correct range
assert all(10**i<= 2000 and 10**i>= 0 for i in clust_Mult.companions['log_a']) #max separation is 2000 AU
assert all(i<= 1 and i>= 0 for i in clust_Mult.companions['e'])
assert all(i<= 180 and i>= 0 for i in clust_Mult.companions['i'])
assert all(i<= 360 and i>= 0 for i in clust_Mult.companions['omega'])
assert all(i<= 360 and i>= 0 for i in clust_Mult.companions['Omega'])
-
+
#checks sign for inclination is being randomly genarated
assert any(i > 90 for i in clust_Mult.companions['i']) and any(i < 90 for i in clust_Mult.companions['i'])
-
+
#checks eccentricity follows f(e) = 2e pdf
n, bins = np.histogram(clust_Mult.companions['e'], density = True)
bin_centers = 0.5*(bins[1:] + bins[:-1])
assert all(np.abs(i) < 0.3 for i in 2*bin_centers - n)
-
+
#checks shape of inclination histogram is sin(i)
n, bins = np.histogram(clust_Mult.companions['i'])
bin_centers = 0.5*(bins[1:] + bins[:-1])
assert all(np.abs(i) < 0.15 for i in n/max(n) - np.sin(np.pi*bin_centers/180))
-
- return
-
+ return
diff --git a/spisea/tests/test_reddening.py b/spisea/tests/test_reddening.py
index 73c6ecaf..99bbf78b 100644
--- a/spisea/tests/test_reddening.py
+++ b/spisea/tests/test_reddening.py
@@ -1,6 +1,7 @@
import numpy as np
-from spisea import reddening, synthetic
+from spisea import reddening, synthetic, evolution, atmospheres
import pylab as py
+import os
import pdb
@@ -38,7 +39,7 @@ def test_RedLawBrokenPowerLaw(plots=False):
# Compare law_test and the output from the redlaw object
law_output = red_law.broken_powerlaw(wave_test, 1)
-
+
assert len(law_test) == len(law_output)
assert np.sum(np.isnan(law_output)) == 0
@@ -61,7 +62,7 @@ def test_RedLawBrokenPowerLaw(plots=False):
idx2 = np.where(abs(wave_test-2.1) == np.min(abs(wave_test-2.1)))
slope = (log_output[idx1] - log_output[idx2]) / (log_wave[idx1] - log_wave[idx2])
assert abs(slope - (-1.0 * alpha1)) < 10**-4
-
+
# If desired (debug only), make plot to see what law looks like
if plots:
# Test plot: these should match nearly exactly
@@ -76,6 +77,11 @@ def test_RedLawBrokenPowerLaw(plots=False):
py.gca().invert_xaxis()
py.title('2-segment Extlaw: should match')
+ # Additional test here: make sure get_red_law is working
+ # for broken power law as intended
+ red_law_test = reddening.get_red_law('broken_pl,{0},{1},{2}'.format(lambda_limits, alpha_vals, K_wave))
+ assert np.all(red_law_test.obscuration == red_law.obscuration)
+
#===============================#
# Test 1: 4-segment law
#===============================#
@@ -103,7 +109,7 @@ def test_RedLawBrokenPowerLaw(plots=False):
idx = np.where( (wave_test >= 1.27) & (wave_test < 1.63))
coeff = (1.63 ** (-1*alpha1)) / (1.63 ** (-1*alpha2))
law_test[idx] = coeff * wave_test[idx] ** (-1*alpha2)
-
+
# 1.27 - 0.8
idx = np.where( (wave_test >= 0.8) & (wave_test < 1.27))
coeff1 = (1.63 ** (-1*alpha1)) / (1.63 ** (-1*alpha2))
@@ -118,7 +124,7 @@ def test_RedLawBrokenPowerLaw(plots=False):
coeff3 = (0.8 ** (-1*alpha3)) / (0.8 ** (-1*alpha4))
coeff_f = coeff1 * coeff2 * coeff3
law_test[idx] = coeff_f * wave_test[idx] ** (-1*alpha4)
-
+
assert np.sum(np.isnan(law_test)) == 0
# Put in terms of A_lambda / A_Ks, like the reddening object
@@ -127,7 +133,7 @@ def test_RedLawBrokenPowerLaw(plots=False):
# Compare law_test and the output from the redlaw object
law_output = red_law.broken_powerlaw(wave_test, 1)
-
+
assert len(law_test) == len(law_output)
assert np.sum(np.isnan(law_output)) == 0
@@ -160,7 +166,7 @@ def test_RedLawBrokenPowerLaw(plots=False):
idx2 = np.where(abs(wave_test-0.7) == np.min(abs(wave_test-0.7)))
slope = (log_output[idx1] - log_output[idx2]) / (log_wave[idx1] - log_wave[idx2])
assert abs(slope - (-1.0 * alpha4)) < 10**-4
-
+
# If desired (debug only), make plot to see what law looks like
if plots:
# Test plot: these should match nearly exactly
@@ -179,3 +185,74 @@ def test_RedLawBrokenPowerLaw(plots=False):
return
+def test_red_law_IsochronePhot():
+ """
+ Make sure each reddening law can run with IsochronePhot
+ """
+ # Define properties of stellar pop to models
+ logAge = np.log10(5*10**6.) # Age in log(years)
+ dist = 8000 # distance in parsec
+ metallicity = 0 # Metallicity in [M/H]
+
+ # Define evolution/atmosphere models and extinction law
+ evo_model = evolution.MISTv1()
+ atm_func = atmospheres.get_merged_atmosphere
+
+ # Also specify filters for synthetic photometry.
+ filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f153m', 'nirc2,H', 'nirc2,Kp']
+
+ # Define reddening laws and associated AKs vals
+ redlaw_arr = ['F11', 'S10', 'NL20', 'I05', 'N09', 'RZ07',
+ 'RL85', 'D16', 'F09,2.5,3', 'S16,1.55,0', 'DM16',
+ 'H18b', 'NL18', 'C89,3.1', 'pl,2.12,0.9,2.4',
+ 'broken_pl,[2.3,1.63,0.9],[2.23, 3.0],2.12']
+
+ aks_arr = [2.62, 2.46, 1.67, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3]
+ for ii in range(len(redlaw_arr)):
+ redlaw = reddening.get_red_law(redlaw_arr[ii])
+ #redlaw_arr[ii]
+ aks = aks_arr[ii]
+
+ # Try to run isochrone phot
+ iso_test = synthetic.IsochronePhot(
+ logAge,
+ aks,
+ dist,
+ metallicity=0,
+ evo_model=evo_model,
+ atm_func=atm_func,
+ red_law=redlaw,
+ filters=filt_list,
+ min_mass=0.95,
+ max_mass=1.05,
+ iso_dir='isochrones/',
+ recomp=True
+ )
+ print('----EL {0} works OK!-----'.format(redlaw_arr[ii]))
+
+ return
+
+def test_all_EL():
+ """
+ Test to make sure all expected ELs are callable
+ and work OK.
+
+ (except for the broken power law, which is tested elsewhere)
+ """
+ # Will crash if any of these calls crash
+ red_law = reddening.RedLawNishiyama09()
+ red_law = reddening.RedLawCardelli(3.1)
+ red_law = reddening.RedLawRomanZuniga07()
+ red_law = reddening.RedLawRiekeLebofsky()
+ red_law = reddening.RedLawDamineli16()
+ red_law = reddening.RedLawDeMarchi16()
+ red_law = reddening.RedLawFitzpatrick09(1.8, 5)
+ red_law = reddening.RedLawSchlafly16(1.55, 3.3)
+ red_law = reddening.RedLawIndebetouw05()
+ red_law = reddening.RedLawFritz11()
+ red_law = reddening.RedLawHosek18b()
+ red_law = reddening.RedLawSchoedel10()
+ red_law = reddening.RedLawNoguerasLara18()
+ red_law = reddening.RedLawNoguerasLara20()
+
+ return
diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py
index c8294788..7694ebfd 100755
--- a/spisea/tests/test_synthetic.py
+++ b/spisea/tests/test_synthetic.py
@@ -2,6 +2,8 @@
import numpy as np
import pylab as plt
import numpy as np
+import pickle
+import spisea
from spisea import reddening, evolution, atmospheres, ifmr
from spisea import synthetic as syn
from spisea.imf import imf
@@ -11,6 +13,8 @@
import pdb
from scipy.spatial import cKDTree as KDTree
+spisea_path = os.path.dirname(spisea.__file__)
+
def test_isochrone(plot=False):
logAge = 6.7
AKs = 2.7
@@ -26,11 +30,11 @@ def test_isochrone(plot=False):
assert iso.points.meta['AKS'] == AKs
assert iso.points.meta['DISTANCE'] == distance
assert len(iso.points) > 100
-
+
if plot:
- plt.figure(1)
+ plt.figure(1)
iso.plot_HR_diagram()
-
+
plt.figure(2)
iso.plot_mass_luminosity()
@@ -38,21 +42,23 @@ def test_isochrone(plot=False):
def test_iso_wave():
"""
- Test to make sure isochrones generated have spectra with the proper
+ Test to make sure isochrones generated have spectra with the proper
wavelength range, and that the user has control over that wavelength
range (propagated through IsochronePhot)
"""
# Define isochrone parameters
- logAge = np.log10(5*10**6.) # Age in log(years)
- AKs = 0.8 # extinction in mags
+ logAge = 6.7 # Age in log(years)
+ AKs = 2.7 # extinction in mags
dist = 4000 # distance in parsec
+ metal = 0.0 # metallicity
+ iso_dir = f'{spisea_path}/tests/isochrones'
# Define evolution/atmosphere models and extinction law (optional)
- evo_model = evolution.MergedBaraffePisaEkstromParsec()
+ evo_model = evolution.MergedBaraffePisaEkstromParsec()
atm_func = atmospheres.get_merged_atmosphere
red_law = reddening.RedLawHosek18b()
- # Also specify filters for synthetic photometry (optional). Here we use
+ # Also specify filters for synthetic photometry (optional). Here we use
# the HST WFC3-IR F127M, F139M, and F153M filters
filt_list = ['wfc3,ir,f127m']
@@ -65,11 +71,18 @@ def test_iso_wave():
# Make Isochrone object. Will use wave_range = [3000,52000].
# Make sure range matches to resolution of atmosphere.
wave_range1 = [3000, 52000]
- my_iso = syn.IsochronePhot(logAge, AKs, dist,
- evo_model=evo_model, atm_func=atm_func,
- red_law=red_law, filters=filt_list,
- mass_sampling=10, wave_range=wave_range1,
- recomp=True)
+ my_iso = syn.IsochronePhot(
+ logAge, AKs, dist,
+ metallicity=metal,
+ evo_model=evo_model,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=10,
+ wave_range=wave_range1,
+ recomp=True,
+ iso_dir=iso_dir
+ )
test = my_iso.spec_list[0]
@@ -79,11 +92,19 @@ def test_iso_wave():
# Now let's try changing the wave range. Is it carried through
# properly?
wave_range2 = [1200, 90000]
- my_iso = syn.IsochronePhot(logAge, AKs, dist,
- evo_model=evo_model, atm_func=atm_func,
- red_law=red_law, filters=filt_list,
- mass_sampling=10, wave_range=wave_range2,
- recomp=True)
+ my_iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ dist,
+ evo_model=evo_model,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=10,
+ wave_range=wave_range2,
+ recomp=True,
+ iso_dir=iso_dir
+ )
test2 = my_iso.spec_list[0]
@@ -93,13 +114,21 @@ def test_iso_wave():
# Does the error exception catch the bad wave_range?
wave_range3 = [1200, 1000000]
try:
- my_iso = syn.IsochronePhot(logAge, AKs, dist,
- evo_model=evo_model, atm_func=atm_func,
- red_law=red_law, filters=filt_list,
- mass_sampling=10, wave_range=wave_range3,
- recomp=True)
+ my_iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ dist,
+ evo_model=evo_model,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=10,
+ wave_range=wave_range3,
+ recomp=True,
+ iso_dir=iso_dir
+ )
print('WAVE TEST FAILED!!! Should have crashed here, wavelength range out of bounds')
- raise ValueError()
+ raise ValueError()
except:
print('Wavelength out of bound condition passed. Test is good')
pass
@@ -111,20 +140,28 @@ def test_IsochronePhot(plot=False):
distance = 4000
filt_list = ['wfc3,ir,f127m', 'nirc2,J']
mass_sampling=1
- iso_dir = 'iso/'
+ iso_dir = f'{spisea_path}/tests/isochrones'
evo_model = evolution.MISTv1()
atm_func = atmospheres.get_merged_atmosphere
redlaw = reddening.RedLawNishiyama09()
startTime = time.time()
- iso = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model,
- atm_func=atm_func, red_law=redlaw,
- filters=filt_list,
- mass_sampling=mass_sampling, iso_dir=iso_dir)
+ iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo_model,
+ atm_func=atm_func,
+ red_law=redlaw,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir,
+ recomp=True
+ )
endTime = time.time()
print('IsochronePhot generated in: %d seconds' % (endTime - startTime))
- # Typically takes 120 seconds if file is regenerated.
+ # Typically takes 40 seconds if file is regenerated.
# Limited by pysynphot.Icat call in atmospheres.py
assert iso.points.meta['LOGAGE'] == logAge
@@ -135,54 +172,116 @@ def test_IsochronePhot(plot=False):
assert 'm_nirc2_J' in iso.points.colnames
if plot:
- plt.figure(1)
+ plt.figure(1)
iso.plot_CMD('mag814w', 'mag160w')
-
+
plt.figure(2)
iso.plot_mass_magnitude('mag160w')
# Finally, let's test the isochronePhot file generation
- assert os.path.exists('{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_p00.fits'.format(iso_dir, logAge,
- AKs, str(distance).zfill(5)))
-
+ metal_value = 0.
+ metal_sign = 'm' if metal_value < 0 else 'p'
+ assert os.path.exists(f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(distance).zfill(5)}_{metal_sign}{metal_value:.2f}.fits')
+
# Check 1: If we try to remake the isochrone, does it read the file rather than
# making a new one
- iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model,
- atm_func=atm_func, red_law=redlaw,
- filters=filt_list,
- mass_sampling=mass_sampling, iso_dir=iso_dir)
+ iso_new = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo_model,
+ atm_func=atm_func,
+ red_law=redlaw,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir
+ )
assert iso_new.recalc == False
- # Check 2: If we change evo model, atmo model, or redlaw,
+ # Check 2: Confirm that adding a new column to an existing isochrone works properly.
+ # Does the new filter get added to the isochrone? And the old ones still there?
+ # Does the computed data for the new filter match the same result if you fully regenerate the isochrone?
+ iso_new_addfilt = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo_model,
+ atm_func=atm_func,
+ red_law=redlaw,
+ filters=filt_list+['2mass,Ks'],
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir
+ )
+
+ assert iso_new_addfilt.recalc == False
+ assert 'm_2mass_Ks' in iso_new_addfilt.points.colnames
+ assert 'm_nirc2_J' in iso_new_addfilt.points.colnames
+
+ iso_new_3filt = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo_model,
+ atm_func=atm_func,
+ red_law=redlaw,
+ filters=filt_list+['2mass,Ks'],
+ mass_sampling=mass_sampling,
+ recomp=True,
+ iso_dir=iso_dir
+ )
+ np.testing.assert_almost_equal(iso_new_addfilt.points['m_2mass_Ks'], iso_new_3filt.points['m_2mass_Ks'])
+ assert iso_new_3filt.recalc==True
+
+ # Check 3: If we change evo model, atmo model, or redlaw,
# does IsochronePhot regenerate the isochrone and overwrite the existing one?
evo2 = evolution.MergedBaraffePisaEkstromParsec()
mass_sampling=20
- iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2,
- atm_func=atm_func, red_law=redlaw,
- filters=filt_list,
- mass_sampling=mass_sampling, iso_dir=iso_dir)
+ iso_new = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo2,
+ atm_func=atm_func,
+ red_law=redlaw,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir
+ )
assert iso_new.recalc == True
redlaw2 = reddening.RedLawHosek18b()
- iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2,
- atm_func=atm_func, red_law=redlaw2,
- filters=filt_list,
- mass_sampling=mass_sampling, iso_dir=iso_dir)
+ iso_new = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo2,
+ atm_func=atm_func,
+ red_law=redlaw2,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir
+ )
assert iso_new.recalc == True
atm2 = atmospheres.get_castelli_atmosphere
- iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2,
- atm_func=atm2, red_law=redlaw2,
- filters=filt_list,
- mass_sampling=mass_sampling, iso_dir=iso_dir)
+ iso_new = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo2,
+ atm_func=atm2,
+ red_law=redlaw2,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir
+ )
assert iso_new.recalc == True
-
return
def test_ResolvedCluster():
@@ -192,21 +291,30 @@ def test_ResolvedCluster():
distance = 4000
cluster_mass = 10**5.
mass_sampling=5
+ iso_dir = f'{spisea_path}/tests/isochrones'
# Test filters
filt_list = ['nirc2,J', 'nirc2,Kp']
startTime = time.time()
-
+
evo = evolution.MergedBaraffePisaEkstromParsec()
atm_func = atmospheres.get_merged_atmosphere
red_law = reddening.RedLawNishiyama09()
-
- iso = syn.IsochronePhot(logAge, AKs, distance,
- evo_model=evo, atm_func=atm_func,
- red_law=red_law, filters=filt_list,
- mass_sampling=mass_sampling)
+
+ iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir,
+ recomp=True
+ )
print('Constructed isochrone: %d seconds' % (time.time() - startTime))
@@ -220,7 +328,7 @@ def test_ResolvedCluster():
my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,
multiplicity=None)
print('Constructed IMF: %d seconds' % (time.time() - startTime))
-
+
cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass)
clust1 = cluster1.star_systems
print('Constructed cluster: %d seconds' % (time.time() - startTime))
@@ -248,7 +356,7 @@ def test_ResolvedCluster():
my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,
multiplicity=multi)
print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime))
-
+
cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass)
clust2 = cluster2.star_systems
print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime))
@@ -258,7 +366,7 @@ def test_ResolvedCluster():
assert np.sum(clust2['N_companions']) == len(cluster2.companions)
##########
- # Plots
+ # Plots
##########
# Plot an IR CMD and compare cluster members to isochrone.
plt.figure(1)
@@ -268,7 +376,7 @@ def test_ResolvedCluster():
plt.plot(iso.points['m_nirc2_J'] - iso.points['m_nirc2_Kp'], iso.points['m_nirc2_J'], 'c-')
plt.gca().invert_yaxis()
plt.xlabel('J - Kp (mag)')
- plt.ylabel('J (mag')
+ plt.ylabel('J (mag)')
# Plot a mass-magnitude relationship.
plt.figure(2)
@@ -278,7 +386,7 @@ def test_ResolvedCluster():
plt.gca().invert_yaxis()
plt.xlabel('Mass (Msun)')
plt.ylabel('J (mag)')
-
+
# # Plot the spectrum of the most massive star
# idx = cluster.mass.argmax()
# plt.clf()
@@ -298,21 +406,29 @@ def test_ResolvedClusterDiffRedden():
cluster_mass = 10**5.
deltaAKs = 0.05
mass_sampling=5
+ iso_dir = f'{spisea_path}/tests/isochrones'
# Test filters
filt_list = ['nirc2,J', 'nirc2,Kp']
-
+
startTime = time.time()
-
+
evo = evolution.MergedBaraffePisaEkstromParsec()
atm_func = atmospheres.get_merged_atmosphere
red_law = reddening.RedLawNishiyama09()
-
- iso = syn.IsochronePhot(logAge, AKs, distance,
- evo_model=evo, atm_func=atm_func,
- red_law=red_law, filters=filt_list,
- mass_sampling=mass_sampling)
+
+ iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir
+ )
print('Constructed isochrone: %d seconds' % (time.time() - startTime))
@@ -325,13 +441,13 @@ def test_ResolvedClusterDiffRedden():
my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,
multiplicity=None)
print('Constructed IMF: %d seconds' % (time.time() - startTime))
-
+
cluster1 = syn.ResolvedClusterDiffRedden(iso, my_imf1, cluster_mass, deltaAKs)
clust1 = cluster1.star_systems
print('Constructed cluster: %d seconds' % (time.time() - startTime))
assert len(clust1) > 0
-
+
plt.figure(3)
plt.clf()
plt.plot(clust1['m_nirc2_J'] - clust1['m_nirc2_Kp'], clust1['m_nirc2_J'], 'r.')
@@ -348,7 +464,7 @@ def test_ResolvedClusterDiffRedden():
my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,
multiplicity=multi)
print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime))
-
+
cluster2 = syn.ResolvedClusterDiffRedden(iso, my_imf2, cluster_mass, deltaAKs)
clust2 = cluster2.star_systems
print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime))
@@ -358,7 +474,7 @@ def test_ResolvedClusterDiffRedden():
assert np.sum(clust2['N_companions']) == len(cluster2.companions)
##########
- # Plots
+ # Plots
##########
# Plot an IR CMD and compare cluster members to isochrone.
plt.figure(1)
@@ -380,7 +496,7 @@ def test_ResolvedClusterDiffRedden():
plt.ylabel('J (mag)')
return
-
+
def test_UnresolvedCluster():
log_age = 6.7
AKs = 0.0
@@ -388,7 +504,7 @@ def test_UnresolvedCluster():
metallicity=0
cluster_mass = 10**4.
- startTime = time.time()
+ startTime = time.time()
multi = multiplicity.MultiplicityUnresolved()
imf_in = imf.Kroupa_2001(multiplicity=multi)
evo = evolution.MergedBaraffePisaEkstromParsec()
@@ -415,22 +531,30 @@ def test_ifmr_multiplicity():
distance = 1000
cluster_mass = 1e6
mass_sampling = 5
+ iso_dir = f'{spisea_path}/tests/isochrones'
# Test all filters
filt_list = ['nirc2,Kp', 'nirc2,H', 'nirc2,J']
startTime = time.time()
-
+
evo = evolution.MISTv1()
atm_func = atmospheres.get_merged_atmosphere
ifmr_obj = ifmr.IFMR_Raithel18()
red_law = reddening.RedLawNishiyama09()
-
- iso = syn.IsochronePhot(logAge, AKs, distance,
- evo_model=evo, atm_func=atm_func,
- red_law=red_law, filters=filt_list,
- mass_sampling=mass_sampling)
+
+ iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir
+ )
print('Constructed isochrone: %d seconds' % (time.time() - startTime))
@@ -443,13 +567,13 @@ def test_ifmr_multiplicity():
##########
my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,
multiplicity=None)
- print('Constructed IMF: %d seconds' % (time.time() - startTime))
-
+ print('Constructed IMF: %d seconds' % (time.time() - startTime))
+
cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass, ifmr=ifmr_obj)
clust1 = cluster1.star_systems
print('Constructed cluster: %d seconds' % (time.time() - startTime))
-
+
##########
# Test with multiplicity and IFMR
##########
@@ -457,7 +581,7 @@ def test_ifmr_multiplicity():
my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,
multiplicity=multi)
print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime))
-
+
cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=ifmr_obj)
clust2 = cluster2.star_systems
comps2 = cluster2.companions
@@ -493,43 +617,73 @@ def test_metallicity():
Test isochrone generation at different metallicities
"""
# Define isochrone parameters
- logAge = np.log10(5*10**6.)
- AKs = 0.8
- dist = 4000
+ logAge = np.log10(5*10**6.)
+ AKs = 0.8
+ dist = 4000
evo_model = evolution.MISTv1()
atm_func = atmospheres.get_phoenixv16_atmosphere
red_law = reddening.RedLawHosek18b()
filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m']
+ iso_dir = f'{spisea_path}/tests/isochrones'
- # Start with a solar metallicity isochrone
- metallicity= 0.0
+ # Start with a solar metallicity isochrone
+ metallicity= 0.
+ metal_sign = 'm' if metallicity < 0 else 'p'
# Make Isochrone object, with high mass_sampling to decrease compute time
- my_iso = syn.IsochronePhot(logAge, AKs, dist, metallicity=metallicity,
- evo_model=evo_model, atm_func=atm_func,
- red_law=red_law, filters=filt_list,
- mass_sampling=10)
+ my_iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ dist,
+ metallicity=metallicity,
+ evo_model=evo_model,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=10,
+ iso_dir=iso_dir
+ )
# Test isochrone properties
assert my_iso.points.meta['METAL_IN'] == 0.0
- assert os.path.exists('iso_6.70_0.80_04000_p00.fits')
+ assert os.path.exists(f'{iso_dir}/iso_6.70_0.80_04000_p0.00.fits')
# Now for non-solar metallicity
metallicity= -1.5
# Make Isochrone object, with high mass_sampling to decrease compute time
- my_iso = syn.IsochronePhot(logAge, AKs, dist, metallicity=metallicity,
- evo_model=evo_model, atm_func=atm_func,
- red_law=red_law, filters=filt_list,
- mass_sampling=10)
-
- metal_act = np.log10(0.00047 / 0.0142) # For Mist isochrones
+ my_iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ dist,
+ metallicity=metallicity,
+ evo_model=evo_model,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=10,
+ iso_dir=iso_dir
+ )
+
+ # MIST model sub-directory names changed in SPISEA v2.1.4 update;
+ # changing what "metal_act" value was. version 1 of MIST grid
+ # names mistakenly used 0.015 for Zsolar, which was fixed for
+ # version 2 onward. Note that this only effects this variable,
+ # and not the grid output itself.
+ # So, here the expected value for metal_act changes depending
+ # on the grid value
+ grid_version = evolution.get_installed_grid_num('{0}/evolution/'.format(os.environ['SPISEA_MODELS']))
+
+ if grid_version == 1:
+ metal_act = np.log10(0.00047 / 0.0142) # For Mist isochrones
+ else:
+ metal_act = np.log10(0.00045 / 0.0142) # For Mist isochrones
# Test isochrone properties
assert my_iso.points.meta['METAL_IN'] == -1.5
- assert my_iso.points.meta['METAL_ACT'] == metal_act
- assert os.path.exists('iso_6.70_0.80_04000_m15.fits')
-
+ assert np.isclose(my_iso.points.meta['METAL_ACT'], metal_act)
+ assert os.path.exists(f'{iso_dir}/iso_6.70_0.80_04000_m1.50.fits')
+
return
def test_cluster_mass():
@@ -539,21 +693,29 @@ def test_cluster_mass():
distance = 4000
cluster_mass = 10**5.
mass_sampling = 5
+ iso_dir = f'{spisea_path}/tests/isochrones'
# Test filters
filt_list = ['nirc2,J', 'nirc2,Kp']
startTime = time.time()
-
+
# Define evolution/atmosphere models and extinction law
- evo = evolution.MISTv1()
+ evo = evolution.MISTv1()
atm_func = atmospheres.get_merged_atmosphere
red_law = reddening.RedLawHosek18b()
-
- iso = syn.IsochronePhot(logAge, AKs, distance,
- evo_model=evo, atm_func=atm_func,
- red_law=red_law, filters=filt_list,
- mass_sampling=mass_sampling)
+
+ iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir
+ )
print('Constructed isochrone: %d seconds' % (time.time() - startTime))
@@ -563,7 +725,7 @@ def test_cluster_mass():
# IFMR
my_ifmr = ifmr.IFMR_Raithel18()
-
+
##########
# Start without multiplicity
@@ -571,7 +733,7 @@ def test_cluster_mass():
my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,
multiplicity=None)
print('Constructed IMF: %d seconds' % (time.time() - startTime))
-
+
cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass, ifmr=my_ifmr)
clust1 = cluster1.star_systems
print('Constructed cluster: %d seconds' % (time.time() - startTime))
@@ -581,6 +743,15 @@ def test_cluster_mass():
assert np.abs(cluster_mass_out - cluster_mass) < 200.0 # within 200 Msun of desired mass.
print('Cluster Mass: IN = ', cluster_mass, " OUT = ", cluster_mass_out)
+ cluster3 = syn.ResolvedCluster(iso, my_imf1, cluster_mass, ifmr=my_ifmr, keep_low_mass_stars=True)
+ clust3 = cluster3.star_systems
+ print('Constructed cluster w/ keep_low_mass_stars: %d seconds' % (time.time() - startTime))
+
+ # Check that the total mass is within tolerance of input mass
+ cluster_mass_out = clust3['systemMass'].sum()
+ assert np.abs(cluster_mass_out - cluster_mass) < 200.0 # within 200 Msun of desired mass.
+ print('Cluster Mass: IN = ', cluster_mass, " OUT = ", cluster_mass_out)
+
##########
# Test with multiplicity
##########
@@ -588,7 +759,7 @@ def test_cluster_mass():
my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,
multiplicity=multi)
print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime))
-
+
cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=my_ifmr)
clust2 = cluster2.star_systems
print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime))
@@ -600,41 +771,120 @@ def test_cluster_mass():
return
+def test_keep_low_mass_stars():
+ """
+ Test "keep_low_mass_stars = True" functionality introduced in v2.2
+ """
+ # Define cluster parameters, pulling on an isochrone generated in an earlier test (since
+ # we don't care about isochrone generation here
+ logAge = 6.7
+ AKs = 2.4
+ distance = 4000
+ cluster_mass = 10**5.
+ mass_sampling = 5
+ iso_dir = f'{spisea_path}/tests/isochrones'
+
+ # Test filters
+ filt_list = ['nirc2,J', 'nirc2,Kp']
+
+ # Define evolution/atmosphere models and extinction law
+ evo = evolution.MISTv1()
+ atm_func = atmospheres.get_merged_atmosphere
+ red_law = reddening.RedLawHosek18b()
+
+ iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir
+ )
+
+ # Get the minimum mass in the isochrones. This should be the lowest
+ # mass psosbile when keep_low_mass_stars == False.
+ # Make sure this min mass is low enough for a reasonalbe test
+ min_mass_iso = np.min(iso.points['mass'])
+ assert min_mass_iso >= 0.05
+
+ # Define IMF + IFMR. Make sure IMF goes to really low masses,
+ # below the 0.08 Msun limit of the MIST isochrones
+ imf_min = 0.01
+ imf_mass_limits = np.array([imf_min, 0.5, 1, 120.0])
+ imf_powers = np.array([-1.3, -2.3, -2.3])
+ my_ifmr = ifmr.IFMR_Raithel18()
+ my_imf = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,
+ multiplicity=multiplicity.MultiplicityUnresolved())
+
+ # Define 2 clusters: one without keep_low_mass_stars and the other
+ # with keep_low_mass_stars
+ clust_remove = syn.ResolvedCluster(iso, my_imf, cluster_mass, ifmr=my_ifmr, keep_low_mass_stars=False)
+ clust_keep = syn.ResolvedCluster(iso, my_imf, cluster_mass, ifmr=my_ifmr, keep_low_mass_stars=True)
+
+ # Check the star_systems tables: clust_remove loweset mass should match
+ # min_mass_iso, while clust_keep should stretch to IMF limit (1%)
+ assert np.isclose(np.min(clust_keep.star_systems['mass']), imf_min, rtol=0.01, atol=10**-8)
+ assert (np.isclose(np.min(clust_remove.star_systems['mass']), imf_min, rtol=0.01, atol=10**-8) == False)
+
+ # NOTE: companion table always had the low-mass stars, this functionality is only on the star_systems table
+
+ # Finally, make sure these low-mass stars have properly assigned phase = 98
+ idx = np.where(clust_keep.star_systems['mass'] < min_mass_iso)
+ assert ( (len(idx[0]) > 0) & (np.all(clust_keep.star_systems['phase'][idx] == 98)) )
+
+ return
+
+
def test_compact_object_companions():
-
+
# Define cluster parameters
logAge = 6.7
AKs = 2.4
distance = 4000
cluster_mass = 10**4.
mass_sampling=5
+ iso_dir = f'{spisea_path}/tests/isochrones'
# Test filters
filt_list = ['nirc2,J', 'nirc2,Kp']
startTime = time.time()
-
+
evo = evolution.MergedBaraffePisaEkstromParsec()
atm_func = atmospheres.get_merged_atmosphere
red_law = reddening.RedLawNishiyama09()
-
- iso = syn.IsochronePhot(logAge, AKs, distance,
- evo_model=evo, atm_func=atm_func,
- red_law=red_law, filters=filt_list,
- mass_sampling=mass_sampling)
+
+ iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=mass_sampling,
+ iso_dir=iso_dir
+ )
print('Constructed isochrone: %d seconds' % (time.time() - startTime))
-
+
clust_multiplicity = multiplicity.MultiplicityResolvedDK()
- clust_imf_Mult = imf.Kroupa_2001(multiplicity=clust_multiplicity)
+
+ massLimits = np.array([0.2, 0.5, 1, 120]) # mass segments
+ powers = np.array([-1.3, -2.3, -2.3]) # power-law exponents
+ clust_imf_Mult = imf.IMF_broken_powerlaw(massLimits, powers, multiplicity=clust_multiplicity)
+ #clust_imf_Mult = imf.Kroupa_2001(multiplicity=clust_multiplicity)
clust_Mult = syn.ResolvedCluster(iso, clust_imf_Mult, cluster_mass, ifmr=ifmr.IFMR_Raithel18())
-
+
# Makes sure compact object companions not including MIST WDs
#(i.e. those with no luminosity) are being given phases
nan_lum_companions = clust_Mult.companions[np.isnan(clust_Mult.companions['L'])]
-
- assert all(np.isnan(nan_lum_companions['phase'])) == False
+
+ assert (len(nan_lum_companions) == 0) | (all(np.isnan(nan_lum_companions['phase'])) == False)
#=================================#
# Additional timing functions
@@ -645,17 +895,25 @@ def time_test_cluster():
AKs = 2.7
distance = 4000
cluster_mass = 10**4
+ iso_dir = f'{spisea_path}/tests/isochrones'
startTime = time.time()
-
+
evo = evolution.MergedBaraffePisaEkstromParsec()
atm_func = atmospheres.get_merged_atmosphere
red_law = reddening.RedLawNishiyama09()
filt_list = ['nirc2,J', 'nirc2,Kp']
-
- iso = syn.IsochronePhot(logAge, AKs, distance,
- evo_model=evo, atm_func=atm_func,
- red_law=red_law, filters=filt_list)
+
+ iso = syn.IsochronePhot(
+ logAge,
+ AKs,
+ distance,
+ evo_model=evo,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ iso_dir=iso_dir
+ )
print('Constructed isochrone: %d seconds' % (time.time() - startTime))
imf_limits = np.array([0.07, 0.5, 150])
@@ -663,23 +921,30 @@ def time_test_cluster():
multi = multiplicity.MultiplicityUnresolved()
my_imf = imf.IMF_broken_powerlaw(imf_limits, imf_powers, multiplicity=multi)
print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime))
-
+
cluster = syn.ResolvedCluster(iso, my_imf, cluster_mass)
print('Constructed cluster: %d seconds' % (time.time() - startTime))
return
-
+
def model_young_cluster_object(resolved=False):
log_age = 6.5
AKs = 0.1
distance = 8000.0
cluster_mass = 10000.
-
+
multi = multiplicity.MultiplicityUnresolved()
imf_in = imf.Kroupa_2001(multiplicity=multi)
- evo = evolution.MergedPisaEkstromParsec()
+ evo = evolution.MergedBaraffePisaEkstromParsec()
atm_func = atmospheres.get_merged_atmosphere
- iso = syn.Isochrone(log_age, AKs, distance, evo, mass_sampling=10)
+ iso = syn.Isochrone(
+ log_age,
+ AKs,
+ distance,
+ evo_model=evo,
+ atm_func=atm_func,
+ mass_sampling=10
+ )
if resolved:
cluster = syn.ResolvedCluster(iso, imf_in, cluster_mass)
@@ -701,30 +966,31 @@ def model_young_cluster_object(resolved=False):
plt.plot(wave, flux, 'k.')
return
-
+
def time_test_mass_match():
log_age = 6.7
AKs = 2.7
distance = 4000
cluster_mass = 5e3
-
+ iso_dir = f'{spisea_path}/tests/isochrones'
+
imf_in = imf.Kroupa_2001(multiplicity=None)
start_time = time.time()
- iso = syn.IsochronePhot(log_age, AKs, distance)
+ iso = syn.IsochronePhot(log_age, AKs, distance, iso_dir=iso_dir)
iso_masses = iso.points['mass']
print('Generated iso masses in {0:.0f} s'.format(time.time() - start_time))
start_time = time.time()
star_masses, isMulti, compMass, sysMass = imf_in.generate_cluster(cluster_mass)
print('Generated cluster masses in {0:.0f} s'.format(time.time() - start_time))
-
+
def match_model_masses1(isoMasses, starMasses):
indices = np.empty(len(starMasses), dtype=int)
-
+
for ii in range(len(starMasses)):
theMass = starMasses[ii]
-
+
dm = np.abs(isoMasses - theMass)
mdx = dm.argmin()
@@ -735,12 +1001,12 @@ def match_model_masses1(isoMasses, starMasses):
indices[ii] = mdx
return indices
-
+
def match_model_masses2(isoMasses, starMasses):
isoMasses_tmp = isoMasses.reshape((len(isoMasses), 1))
kdt = KDTree(isoMasses_tmp)
-
+
starMasses_tmp = starMasses.reshape((len(starMasses), 1))
q_results = kdt.query(starMasses_tmp, k=1)
indices = q_results[1]
@@ -749,7 +1015,7 @@ def match_model_masses2(isoMasses, starMasses):
idx = np.where(dm_frac > 0.1)[0]
indices[idx] = -1
-
+
return indices
print('Test #1 START')
@@ -780,7 +1046,7 @@ def FeH_from_Z(Z):
metal = np.array([2.0e-4, 1.0e-3, 2.0e-3, 2.0e-2]) #ensure that all Spera metallicity regimes are represented
FeH = FeH_from_Z(metal) #generate death mass takes metallicty as [Fe/H]
- #want to get a good range of masses for Spera, should expect 8 invalids, 8 WDs, 3 NSs, and 9 BHs
+ #want to get a good range of masses for Spera, should expect 8 invalids, 8 WDs, 3 NSs, and 9 BHs
ZAMS = np.array([-0.2*np.ones(len(FeH)), 0.2*np.ones(len(FeH)), 4.0*np.ones(len(FeH)), 9.2*np.ones(len(FeH)),
15.0*np.ones(len(FeH)), 30.0*np.ones(len(FeH)), 150.0*np.ones(len(FeH))])
@@ -872,7 +1138,7 @@ def test_Spera15_IFMR_7():
assert len(BH_idx) == 10 , "There are not the right number of BHs for the Spera15 IFMR"
return
-
+
def generate_Raithel18_IFMR():
"""
Make a set of objects using the Raithel18 IFMR for the purposes of testing
@@ -881,7 +1147,7 @@ def generate_Raithel18_IFMR():
"""
Raithel = ifmr.IFMR_Raithel18()
- ZAMS = np.array([-0.2, 0.2, 1.0, 7.0, 10.0, 14.0, 16.0, 18.0, 18.6, 22.0, 26.0, 28.0, 50.0, 61.0, 119.0, 121.0])
+ ZAMS = np.array([-0.2, 0.2, 1.0, 7.0, 10.0, 14.0, 16.0, 18.0, 18.6, 22.0, 26.0, 28.0, 50.0, 61.0, 119.0, 121.0])
#3 invalid indices, 2 WDs, cannot make statements about #of BHs and NSs because the Raithel IFMR has some randomness
output_array = Raithel.generate_death_mass(ZAMS)
@@ -949,3 +1215,62 @@ def test_Raithel18_IFMR_5():
assert len(WD_idx) == 2 , "There are not the right number of WDs for the Raithel18 IFMR"
return
+
+def test_ResolvedCluster_random_state():
+ """
+ Test that the random state is properly set in ResolvedCluster, such that two clusters with the same seed have the same stars.
+ """
+ log_age = 6.7
+ AKs = 2.7
+ distance = 4000
+ cluster_mass = 10**4.
+ iso_dir = f'{spisea_path}/tests/isochrones'
+
+ evo = evolution.MergedBaraffePisaEkstromParsec()
+ atm_func = atmospheres.get_merged_atmosphere
+ red_law = reddening.RedLawNishiyama09()
+ filt_list = ['nirc2,J', 'nirc2,Kp']
+
+ iso = syn.IsochronePhot(
+ log_age,
+ AKs,
+ distance,
+ evo_model=evo,
+ atm_func=atm_func,
+ red_law=red_law,
+ filters=filt_list,
+ mass_sampling=10,
+ iso_dir=iso_dir
+ )
+
+ imf_limits = np.array([0.07, 0.5, 150])
+ imf_powers = np.array([-1.3, -2.35])
+ imf_multi = multiplicity.MultiplicityUnresolved()
+ imf_test = imf.IMF_broken_powerlaw(imf_limits, imf_powers, multiplicity=imf_multi)
+
+ # Test that the same random seed produces the same cluster
+ imf_test.rng = np.random.default_rng(seed=42)
+ result1 = imf_test.generate_cluster(cluster_mass)
+ imf_test.rng = np.random.default_rng(seed=42)
+ result2 = imf_test.generate_cluster(cluster_mass)
+ np.testing.assert_equal(result1, result2)
+
+ # Test that two clusters generated with the same seed have the same star systems and companions
+ cluster1 = syn.ResolvedCluster(iso, imf_test, cluster_mass, seed=42)
+ cluster2 = syn.ResolvedCluster(iso, imf_test, cluster_mass, seed=42)
+ np.testing.assert_array_equal(cluster1.star_systems, cluster2.star_systems)
+
+ with open(f'{spisea_path}/tests/test_data/star_systems.pkl', 'rb') as file:
+ old_star_systems = pickle.load(file)
+ with open(f'{spisea_path}/tests/test_data/companions.pkl', 'rb') as file:
+ old_companion = pickle.load(file)
+
+ for key in old_star_systems.colnames:
+ #np.testing.assert_array_equal(cluster1.star_systems[key], old_star_systems[key])
+ assert np.all(np.isclose(cluster1.star_systems[key], old_star_systems[key], rtol=1e-05, atol=1e-08))
+
+ for key in old_companion.colnames:
+ # Require values are consistent within reasonable bounds
+ assert np.all(np.isclose(cluster1.companions[key], old_companion[key], rtol=1e-05, atol=1e-08))
+
+ return