Fix radec units

.github/workflows/delete-branch-cache.yml

@@ -10,18 +10,18 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v3
-        
+
       - name: Cleanup
         run: |
           gh extension install actions/gh-actions-cache
-          
+
           REPO=${{ github.repository }}
           BRANCH="refs/pull/${{ github.event.pull_request.number }}/merge"
 
           echo "Fetching list of cache key"
           cacheKeysForPR=$(gh actions-cache list -R $REPO -B $BRANCH -L 100 | cut -f 1 )
 
-          ## Setting this to not fail the workflow while deleting cache keys. 
+          ## Setting this to not fail the workflow while deleting cache keys.
           set +e
           echo "Deleting caches..."
           for cacheKey in $cacheKeysForPR
@@ -30,4 +30,4 @@ jobs:
           done
           echo "Done"
         env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}

Dockerfile

@@ -37,7 +37,7 @@ RUN apt-get install -y \
     cmake \
     liblapacke-dev \
     liblapack-dev \
-    libblas-dev 
+    libblas-dev
 
 RUN cd build && cmake .. && make
 

doc/conf.py

@@ -5,7 +5,7 @@
 
 import nmma
 
-extensions = ["myst_parser", "sphinx_copybutton","sphinx_github_changelog"]
+extensions = ["myst_parser", "sphinx_copybutton", "sphinx_github_changelog"]
 myst_enable_extensions = [
     "amsmath",
     "colon_fence",
@@ -58,7 +58,6 @@
 author = "The NMMA Team"
 
 
-
 version = nmma.__version__
 release = version
 

doc/index.rst

@@ -138,7 +138,7 @@ Install parallel-bilby:
 
 .. code::
 
-   conda install -c conda-forge parallel-bilby
+   pip install parallel-bilby
 
 .. note::
 
@@ -161,7 +161,7 @@ specifc instructions below)
 
 .. code::
 
-   conda install -c conda-forge pymultinest
+   conda install conda-forge::pymultinest
 
 .. warning::
 
@@ -184,14 +184,14 @@ requirements.txt file which are necessary for NMMA:
 
 .. code::
 
-   pip install .
+   pip install -e .
 
 .. note::
 
    There is an issue pip installing ``pyfftw`` on arm64 Mac systems; see the dedicated section below for a solution. If any package appeared to have an issue installing, you can first check by attempting to install it again using pip:
 
 .. code::
-
+   pip install keras, tensorflow
    pip install importlib_resources
    pip install  extinction
    pip install dill
@@ -201,6 +201,8 @@ requirements.txt file which are necessary for NMMA:
    pip install sncosmo
    pip install scikit-learn
    pip install joblib
+   pip install keras
+   pip install tensorflow
    conda install -c conda-forge p-tqdm
 
 .. note::

doc/joint_inference.md

@@ -1,6 +1,6 @@
 ## Multi-messenger inference
 
-A joint inference on gravitational-wave and electromagnetic signals requires NMMA to run on a supercomputer cluster because large memory space are required and need to be shared across many CPU cores. Here, we consider a full joint inference on the binary neutron star merger observed on 17th August 2017. 
+A joint inference on gravitational-wave and electromagnetic signals requires NMMA to run on a supercomputer cluster because large memory space are required and need to be shared across many CPU cores. Here, we consider a full joint inference on the binary neutron star merger observed on 17th August 2017.
 
 In order to run a multi-messenger inference, we need to follow to main steps:
 
@@ -27,11 +27,11 @@ Moreover, a prior on all observed messengers is required and needs to be tailore
 
 **Electroamagnetic data and models**
 
-In order to not only sample on gravitational-wave data, we provide further electromagnetic signal related flags. The flag `with-grb=True` will turn on the sampling on a GRB data. As NMMA currently only includes one GRB model, this model does not need to be further specified. If `with-grb=False`, a joint inference of GW+KN data is possible, excluding the GRB part. With regard to the kilonova model, we need to provide a specific model under `kilonova-model`, its respective reduced model grid (if applicable) under `kilonova-model-svd` and a `kilonova-interpolation-type` which can be either `sklearn_gp` or `tensorflow`. The `light-curve-data` flag should include both GRB and kilonova data if a joint inference on GW-GRB-KN is desired (meaning use: `with-grb=True`) or should just include the kilonova data if a GW-KN inference is targeted (meaning use: `with-grb=False`). The kilonova start/end time and time steps apply to both the GRB and kilonova model which will generate light curves during the inference to match the observed data provided. 
+In order to not only sample on gravitational-wave data, we provide further electromagnetic signal related flags. The flag `with-grb=True` will turn on the sampling on a GRB data. As NMMA currently only includes one GRB model, this model does not need to be further specified. If `with-grb=False`, a joint inference of GW+KN data is possible, excluding the GRB part. With regard to the kilonova model, we need to provide a specific model under `kilonova-model`, its respective reduced model grid (if applicable) under `kilonova-model-svd` and a `kilonova-interpolation-type` which can be either `sklearn_gp` or `tensorflow`. The `light-curve-data` flag should include both GRB and kilonova data if a joint inference on GW-GRB-KN is desired (meaning use: `with-grb=True`) or should just include the kilonova data if a GW-KN inference is targeted (meaning use: `with-grb=False`). The kilonova start/end time and time steps apply to both the GRB and kilonova model which will generate light curves during the inference to match the observed data provided.
 
 **Including EOS information**
 
-NMMA enables to include nuclear information by using equations-of-state (EOS) and sample over the EOS during the inference. In order to include a set of EOSs, each EOS.dat file needs to include information on Mass, Radius and Tidal deformability. For the example shown in the `config.ini` file below, we see that `Neos = 5000` meaning that we include 5000 EOS.dat files each containing information on mass, radius and tidal deformability. We also see that a constraint from NICER measurements has been folded in and thus the `eos-weight` reflects this in a weighting. The EOS set should be sorted according to this weighting in order to reduce runtime for the sampling on the EOSs. 
+NMMA enables to include nuclear information by using equations-of-state (EOS) and sample over the EOS during the inference. In order to include a set of EOSs, each EOS.dat file needs to include information on Mass, Radius and Tidal deformability. For the example shown in the `config.ini` file below, we see that `Neos = 5000` meaning that we include 5000 EOS.dat files each containing information on mass, radius and tidal deformability. We also see that a constraint from NICER measurements has been folded in and thus the `eos-weight` reflects this in a weighting. The EOS set should be sorted according to this weighting in order to reduce runtime for the sampling on the EOSs.
 
 **Running the config.ini generation**
 
@@ -40,9 +40,9 @@ In order to prepare the joint inference, a `config.ini` file is required which s
     ################################################################################
     ## Data generation arguments
     ################################################################################
-    
+
     trigger_time = 1187008882.43
-    
+
     ################################################################################
     ## Detector arguments
     ################################################################################
@@ -52,15 +52,15 @@ In order to prepare the joint inference, a `config.ini` file is required which s
     channel_dict = {H1=LOSC-STRAIN, L1=LOSC-STRAIN, V1=LOSC-STRAIN}
     data_dict = {H1=data/GW170817/H-H1_LOSC_CLN_16_V1-1187007040-2048.gwf, L1=data/GW170817/L-L1_LOSC_CLN_16_V1-1187007040-2048.gwf, V1=data/GW170817/V-V1_LOSC_CLN_16_V1-1187007040-2048.gwf}
     duration = 128
-    
+
     ################################################################################
     ## Calibration arguments
     ################################################################################
-    
+
     calibration-model = CubicSpline
     spline-calibration-nodes = 10
     spline-calibration-envelope-dict = {H1:data/GW170817/Feb-20-2018_O2_LHO_GPSTime_1187008882_C02_RelativeResponseUncertainty_FinalResults.txt, L1:data/GW170817/Feb-20-2018_O2_LLO_GPSTime_1187008882_C02_RelativeResponseUncertainty_FinalResults.txt, V1:data/GW170817/V_calibrationUncertaintyEnvelope_magnitude5p1percent_phase40mraddeg20microsecond.txt}
-    
+
     ################################################################################
     ## Job submission arguments
     ################################################################################
@@ -75,24 +75,24 @@ In order to prepare the joint inference, a `config.ini` file is required which s
     distance-marginalization=False
     phase-marginalization=False
     time-marginalization=False
-    
+
     ################################################################################
     ## Prior arguments
     ################################################################################
-    
+
     prior-file = GW170817_AT2017gfo_GRB170817A.prior
-    
+
     ################################################################################
     ## Waveform arguments
     ################################################################################
-    
+
     frequency-domain-source-model = lal_binary_neutron_star
     waveform_approximant = IMRPhenomPv2_NRTidalv2
-    
+
     ################################################################################
     ## EM arguments
     ################################################################################
-        
+
     binary-type=BNS
     light-curve-data=data/AT2017gfo-GRB170817A/AT2017gfo_GRB170817A.dat
     kilonova-model=Bu2019lm
@@ -107,22 +107,22 @@ In order to prepare the joint inference, a `config.ini` file is required which s
     kilonova-interpolation-type=sklearn_gp
     grb-resolution=12
     with-grb=True
-    
+
     ################################################################################
     ## EOS arguments
     ################################################################################
-    
-    with-eos=True 
+
+    with-eos=True
     eos-data=eos/with_NICER_J0740/EOS_024_uniform_5k_sorted
     Neos=5000
     eos-weight=eos/with_NICER_J0740/EOS_sorted_weight.dat
 
 
 The joint inference generation can be performed by running:
-    
+
     nmma-generation config.ini
 
-This will generate a `GW170817-AT2017gfo-GRB170817A_data_dump.pickle` file under `outdir/data/` which need to be provided for the joint inference function `nmma-analysis`. 
+This will generate a `GW170817-AT2017gfo-GRB170817A_data_dump.pickle` file under `outdir/data/` which need to be provided for the joint inference function `nmma-analysis`.
 
 **Running the analysis**
 
@@ -136,33 +136,32 @@ As detailed above, running the analysis with the command `nmma-analysis --data-d
     #PBS -o ./outdir/log_data_analysis/out.txt
     #PBS -m abe
     #PBS -M <email adress>
-    
+
     module load python
     module load mpt
     module load mpi4py
     source <provide path to venv>
 
     export MPI_UNBUFFERED_STDIO=true
     export MPI_LAUNCH_TIMEOUT=240
-    
+
     cd $PBS_O_WORKDIR
     mpirun -np 512 omplace -c 0-127:st=4 nmma-analysis --data-dump <absolute path to folder>/outdir/data/GW170817-AT2017gfo-GRB170817A_data_dump.pickle --nlive 1024 --nact 10 --maxmcmc 10000 --sampling-seed 20210213 --no-plot --outdir <absolute path to outdir/result folder>
 
-Note that settings might differ from cluster to cluster and also the installation of NMMA might be changed (conda vs. python installation). 
+Note that settings might differ from cluster to cluster and also the installation of NMMA might be changed (conda vs. python installation).
 
 
 **Maximum mass constraint from a joint analysis**
 
-From a joint posterior of GW and lightcurve data from a BNS, one can derive an upper limit on the TOV mass, if one assumes that the remnant collapsed to a black hole. The idea is to determine the posterior distribution on the remnant's mass from the posterior distribution of the individual neutron star masses $m_1$, $m_2$ and the ejecta and compare this to the TOV mass of EOSs. 
+From a joint posterior of GW and lightcurve data from a BNS, one can derive an upper limit on the TOV mass, if one assumes that the remnant collapsed to a black hole. The idea is to determine the posterior distribution on the remnant's mass from the posterior distribution of the individual neutron star masses $m_1$, $m_2$ and the ejecta and compare this to the TOV mass of EOSs.
+
+This can be done via the command
 
-This can be done via the command 
-
     maximum-mass-constraint --outdir <path to folder> --joint-posterior <path to the file with samples from GW+EM analysis>  --prior <path to a bilby prior file> --eos-path-macro <path to macroscopic EOS> --eos-path-micro <path to microscopic EOS> [--use-M-Kepler]
 
-The last flag determines whether the remnant mass is compared against the TOV mass or the maximum mass limit for a rotating NS (Kepler limit). The latter is less conservative. The joint posterior should contain the parameters chirp mass, eta_star, log10_mdisk, log10_mej_dyn as named columns. Here, eta_star is $η* = \ln(0.25-η)$ from the symmetric mass ratio $η$. The macroscopic EOS curves must have the central pressure p0 in MeV/fm³ of each NS mass as last column. 
+The last flag determines whether the remnant mass is compared against the TOV mass or the maximum mass limit for a rotating NS (Kepler limit). The latter is less conservative. The joint posterior should contain the parameters chirp mass, eta_star, log10_mdisk, log10_mej_dyn as named columns. Here, eta_star is $η* = \ln(0.25-η)$ from the symmetric mass ratio $η$. The macroscopic EOS curves must have the central pressure p0 in MeV/fm³ of each NS mass as last column.
 
-If --use-M-Kepler is set, the prior file needs to contain two additional fiducial paramters for the quasi-universal relations: 
+If --use-M-Kepler is set, the prior file needs to contain two additional fiducial paramters for the quasi-universal relations:
 
     ratio_R = Gaussian(name = "R", mu = 1.255, sigma = 0.024)
     delta = Uniform(name="delta", minimum = -0.0125, maximum = 0.0125)
-

doc/lfi_analysis.md

@@ -1,6 +1,6 @@
 # Perform Parameter Estimation Using Liklihood Free Inference (LFI)
 
-NMMA is adding machine learning functionality to its currently offered analysis methods. In this initial incorporation, a neural network approach will perform parameter estimation on light curves from BNS events. We will address the limitations first, and then provide an example run. 
+NMMA is adding machine learning functionality to its currently offered analysis methods. In this initial incorporation, a neural network approach will perform parameter estimation on light curves from BNS events. We will address the limitations first, and then provide an example run.
 
 ## Limitations
 
@@ -21,18 +21,16 @@ In addition to installing the standard requirements.txt file, you must also run
 
 ### Generate a simulation set
 
-First, we will create an injection file that describes our light curves. Running the following command line will generate a json file (injection.json). For Ka2017, this will include the parameters: luminosity_distance, timeshift, log10_mej, log10_vej, log10_Xlan, and geocent_time. 
+First, we will create an injection file that describes our light curves. Running the following command line will generate a json file (injection.json). For Ka2017, this will include the parameters: luminosity_distance, timeshift, log10_mej, log10_vej, log10_Xlan, and geocent_time.
 
 	nmma_create_injection --prior-file ./priors/Ka2017 --eos-file ./example_files/eos/ALF2.dat --binary-type BNS --filename ./output/injection --n-injection 10 --original-parameters --extension json
 
 We can generate light curves using this injection file with the following command. Here, we can define the start and ending time of the light curve, its filters, and we can add ztf-like noise.
 
  	lightcurve-generation --model Ka2017 --outdir outdir --outfile-type json --label test --tmin -2 --tmax 20 --dt 0.25 --filters ztfg,ztfr,ztfi --injection ./outdir/injection.json --injection-detection-limit 22.0,22.0,22.0 --ztf-uncertainties
 
-There are two options when running the analysis. One can run it using the injection file directly, which will cause the analysis to generate a light curve on the fly. Or, the command will accept a pre-generated light curve. To run using LFI, the --sampler argument must be neuralnet. In the first example, we call the injection file and pass the first injection to the analysis. In the second, we take the first light curve generated by the injection file. Both of these commands will output a posterior plot to the provided outdir. 
+There are two options when running the analysis. One can run it using the injection file directly, which will cause the analysis to generate a light curve on the fly. Or, the command will accept a pre-generated light curve. To run using LFI, the --sampler argument must be neuralnet. In the first example, we call the injection file and pass the first injection to the analysis. In the second, we take the first light curve generated by the injection file. Both of these commands will output a posterior plot to the provided outdir.
 
 	lightcurve-analysis --sampler neuralnet --model Ka2017 --outdir inferences --label with_inj --prior priors/Ka2017.prior --injection outdir/injection.json --injection-num 0 --dt 0.25
 
-	lightcurve-analysis --sampler neuralnet --model Ka2017 --outdir inferences --label with_data --data outdir/test_0.json --prior priors/Ka2017.prior 
-
-
+	lightcurve-analysis --sampler neuralnet --model Ka2017 --outdir inferences --label with_data --data outdir/test_0.json --prior priors/Ka2017.prior

doc/observing-scenarios-light-curves.md

@@ -7,7 +7,7 @@ This document provides a detailed description of how to use the **injections.dat
 Running the following command will generate a JSON file (`injection_Bu2019lm.json`) with the BILBY processing of compact binary merger events. Here, we consider binaries of type BNS (Binary Neutron Stars), although NSBH (Neutron Star-Black Hole) is also an option. This injection contains a simulation set of parameters: `luminosity_distance`, `log10_mej_wind`, `KNphi`, `inclination_EM`, `timeshift`, `geocent_time` for the Bu2019lm model.
 
 
-### Command to Run [nmma-create-injection]: 
+### Command to Run [nmma-create-injection]:
 
 Run this command to create the JSON injection file:
 
@@ -70,4 +70,4 @@ Install  `gwpy` in your NMMA environment before splitting the BNS and NSBH event
 [paper]: https://doi.org/10.3847/1538-4357/acfcb1
 
 [nmma-create-injection]: https://github.com/nuclear-multimessenger-astronomy/nmma/blob/main/nmma/eos/create_injection.py
-[lightcurve-analysis]: https://github.com/nuclear-multimessenger-astronomy/nmma/blob/main/nmma/em/analysis.py
+[lightcurve-analysis]: https://github.com/nuclear-multimessenger-astronomy/nmma/blob/main/nmma/em/analysis.py

doc/systematics.rst

@@ -78,7 +78,7 @@ In this configuration, the ``sdssu`` and ``ztfr`` filters are sampled together f
         type: Uniform
         minimum: 0
         maximum: 2
-  
+
 
 Distribution types
 ==================

example_files/lightcurves/AT2017gfo_reduced.dat

@@ -105,4 +105,4 @@
 2017-08-27T00:00:00.000 ps1::y 20.78000 0.11000
 2017-08-27T00:00:00.000 2massj 20.23000 0.10000
 2017-08-27T00:00:00.000 2massh 19.66000 0.14000
-2017-08-27T00:00:00.000 2massks 18.50000 0.20000
+2017-08-27T00:00:00.000 2massks 18.50000 0.20000

example_files/multi_config_analysis/config.yaml

@@ -72,4 +72,4 @@ Me2017_4:
   generation-seed: 42
   filters: ztfg,ztfr,ztfi
   remove-nondetections: True
-  #process-per-config: 7
+  #process-per-config: 7

example_files/multi_config_analysis/injection.json

@@ -28,4 +28,4 @@
             ]
         }
     }
-}
+}