diff --git a/bin/all_sky_search/pycbc_coinc_statmap_inj b/bin/all_sky_search/pycbc_coinc_statmap_inj index 357ccef068a..8b2522e11e6 100644 --- a/bin/all_sky_search/pycbc_coinc_statmap_inj +++ b/bin/all_sky_search/pycbc_coinc_statmap_inj @@ -33,7 +33,6 @@ init_logging(args.verbose) significance.check_significance_options(args, parser) - window = args.cluster_window logging.info("Loading coinc zerolag triggers") zdata = pycbc.io.MultiifoStatmapData(files=args.zero_lag_coincs, ifos=args.ifos) diff --git a/docs/_include/inference_example_lisa_smbhb_inj.sh b/docs/_include/inference_example_lisa_smbhb_inj.sh deleted file mode 100644 index d5ae715389b..00000000000 --- a/docs/_include/inference_example_lisa_smbhb_inj.sh +++ /dev/null @@ -1,6 +0,0 @@ -set -e -export OMP_NUM_THREADS=1 -cp ../../examples/inference/lisa_smbhb_inj/injection_smbhb.ini injection_smbhb.ini -sh ../../examples/inference/lisa_smbhb_inj/injection_smbhb.sh -sh ../../examples/inference/lisa_smbhb_inj/run.sh -sh ../../examples/inference/lisa_smbhb_inj/plot.sh diff --git a/docs/_include/inference_example_lisa_smbhb_ldc.sh b/docs/_include/inference_example_lisa_smbhb_ldc.sh deleted file mode 100644 index 29f3a6c8e8b..00000000000 --- a/docs/_include/inference_example_lisa_smbhb_ldc.sh +++ /dev/null @@ -1,5 +0,0 @@ -set -e -export OMP_NUM_THREADS=1 -sh ../../examples/inference/lisa_smbhb_ldc/get.sh -sh ../../examples/inference/lisa_smbhb_ldc/run.sh -python ../../examples/inference/lisa_smbhb_ldc/advanced_plot.py diff --git a/docs/inference.rst b/docs/inference.rst index 1ace8055679..41eb97a9b94 100644 --- a/docs/inference.rst +++ b/docs/inference.rst @@ -505,8 +505,6 @@ Examples inference/examples/single.rst inference/examples/relative.rst inference/examples/hierarchical.rst - inference/examples/lisa_smbhb_ldc_pe.rst - inference/examples/lisa_smbhb_inj_pe.rst inference/examples/sampler_platter.rst inference/models.rst diff --git a/docs/inference/examples/lisa_smbhb_inj_pe.rst b/docs/inference/examples/lisa_smbhb_inj_pe.rst deleted file mode 100644 index 7924261c8f9..00000000000 --- a/docs/inference/examples/lisa_smbhb_inj_pe.rst +++ /dev/null @@ -1,66 +0,0 @@ -.. _inference_example_lisa_smbhb_inj: - ---------------------------------------------- -LISA SMBHB injection and parameter estimation ---------------------------------------------- - -This example shows how to use PyCBC for time-domain LISA TDI noise generation and -supermassive black hole binaries (SMBHB) signal injection. This one is similar to -:ref:`LISA parameter estimation for simulated SMBHB from LDC example -`, the main difference is we generate our own mock data -in this example. In order to to that, we use -`LISA TDI PSD module `_ -to generate the stationary and Gaussian noise for each TDI channel in the time domain, then we use -`waveform injection module `_ -to add the simulated signal into the simulated noise. - -First, we use the following configuration file to define the parameters of our SMBHB injection, we use the -same parameters from the SMBHB signal in :ref:`LISA parameter estimation for simulated SMBHB from LDC example -`: - -.. literalinclude:: ../../../examples/inference/lisa_smbhb_inj/injection_smbhb.ini - :language: ini - -:download:`Download <../../../examples/inference/lisa_smbhb_inj/injection_smbhb.ini>` - -Then we run the following bash script to create a .hdf file that contains same information: - -.. literalinclude:: ../../../examples/inference/lisa_smbhb_inj/injection_smbhb.sh - :language: bash - -:download:`Download <../../../examples/inference/lisa_smbhb_inj/injection_smbhb.sh>` - -Here, we use a similar configuration file for parameter estimation, we also use -:py:class:`Relative ` model. We also just -set chirp mass, mass ratio and tc as variable parameters, `tc`, `eclipticlongitude`, `eclipticlatitude` -and `polarization` are defined in the LISA frame: - -.. literalinclude:: ../../../examples/inference/lisa_smbhb_inj/lisa_smbhb_relbin.ini - :language: ini - -:download:`Download <../../../examples/inference/lisa_smbhb_inj/lisa_smbhb_relbin.ini>` - - -Now run: - -.. literalinclude:: ../../../examples/inference/lisa_smbhb_inj/run.sh - :language: bash - -:download:`Download <../../../examples/inference/lisa_smbhb_inj/run.sh>` - -To plot the posterior distribution after the last iteration, you can run the following script: - -.. literalinclude:: ../../../examples/inference/lisa_smbhb_inj/plot.sh - :language: bash - -:download:`Download <../../../examples/inference/lisa_smbhb_inj/plot.sh>` - -In this example it will create the following plot: - -.. image:: ../../_include/lisa_smbhb_mass_tc.png - :scale: 60 - :align: center - -The scatter points show each walker's position after the last iteration. The -points are colored by the SNR at that point, with the 50th and 90th -percentile contours drawn. The red lines represent the true parameters of injected signal. diff --git a/docs/inference/examples/lisa_smbhb_ldc_pe.rst b/docs/inference/examples/lisa_smbhb_ldc_pe.rst deleted file mode 100644 index 0e49929d0bc..00000000000 --- a/docs/inference/examples/lisa_smbhb_ldc_pe.rst +++ /dev/null @@ -1,70 +0,0 @@ -.. _inference_example_lisa_smbhb_ldc: - ------------------------------------------------------- -LISA parameter estimation for simulated SMBHB from LDC ------------------------------------------------------- - -This example shows how to use PyCBC for parameter estimation of supermassive black hole binaries (SMBHB) -in LISA mock data. The `data `_ are generated from -`LISA Data Challenge 2a: Sangria `_, -and `BBHx `_ package is used to generate the ``IMRPhenomD`` template and calculate -the corresponding TDI response for LISA. Relative binning (heterodyned likelihood) -is used during sampling to speed up the computation of likelihood functions. Before doing parameter estimation, -you need to install `BBHx `_ and `the corresponding PyCBC waveform plugin `_, -please click the corresponding link to see the detailed description of the installation. - -First, we create the following configuration file, here we just set chirp mass, mass ratio and tc as variable parameters, -`tc`, `eclipticlongitude`, `eclipticlatitude` and `polarization` are defined in the LISA frame: - -.. literalinclude:: ../../../examples/inference/lisa_smbhb_ldc/lisa_smbhb_relbin.ini - :language: ini - -:download:`Download <../../../examples/inference/lisa_smbhb_ldc/lisa_smbhb_relbin.ini>` - -By setting the model name to ``relative`` we are using -:py:class:`Relative ` model. - -In this simple example, we do the parameter estimation for the first SMBHB signal in the LDC Sangria dataset -(you can also run parameter estimation for other SMBHB signals by choosing appropriate prior range), -we need download the data first (`MBHB_params_v2_LISA_frame.pkl` contains all the true parameters): - -.. literalinclude:: ../../../examples/inference/lisa_smbhb_ldc/get.sh - :language: bash - -:download:`Download <../../../examples/inference/lisa_smbhb_ldc/get.sh>` - -Now run: - -.. literalinclude:: ../../../examples/inference/lisa_smbhb_ldc/run.sh - :language: bash - -:download:`Download <../../../examples/inference/lisa_smbhb_ldc/run.sh>` - -This will run the ``dynesty`` sampler. When it is done, you will have a file called -``lisa_smbhb.hdf`` which contains the results. It should take about three minutes to -run. - -To plot the posterior distribution after the last iteration, you can run the following simplified script: - -.. literalinclude:: ../../../examples/inference/lisa_smbhb_ldc/plot.sh - :language: bash - -:download:`Download <../../../examples/inference/lisa_smbhb_ldc/plot.sh>` - -Or you can run the advanced one: - -.. literalinclude:: ../../../examples/inference/lisa_smbhb_ldc/advanced_plot.py - :language: python - -:download:`Download <../../../examples/inference/lisa_smbhb_ldc/advanced_plot.py>` - -You can modify this advanced plot script to generate the posterior of any SMBHB signals in the LDC Sangria dataset. -In this example it will create the following plot: - -.. image:: ../../_include/lisa_smbhb_mass_tc_0.png - :scale: 60 - :align: center - -The scatter points show each walker's position after the last iteration. The -points are colored by the SNR at that point, with the 50th and 90th -percentile contours drawn. The red lines represent the true parameters of injected signal. diff --git a/docs/inference/models.rst b/docs/inference/models.rst index d845476ec20..a3aa15746d2 100644 --- a/docs/inference/models.rst +++ b/docs/inference/models.rst @@ -133,8 +133,6 @@ Heterodyne / Relative Models to the models that need to generate a full waveform for every likelihood as these will usually be much faster. - There is also support in this model for use with :ref:`LISA Sangria data analysis ` and :ref:`LISA injection data analysis `. - Supported Marginalizations: distance, coa_phase (dominant mode), polarization +++ Earth Rotation:✅ LISA:✅ Higher Modes:❌ @@ -166,16 +164,6 @@ Heterodyne / Relative Models +++ Earth Rotation:❌ LISA:❌ Higher Modes:❌ -.. card:: Brute force LISA sky modes - - ``'brute_lisa_sky_modes_marginalize'`` :py:class:`pycbc.inference.models.relbin.Relative` - - The models does a brute force marginalization over the LISA sky mode - degeneracies. It is built upon the `relative` model - - Supported Marginalizations: distance, coa_phase (dominant mode) - +++ - Earth Rotation:❌ LISA:✅ Higher Modes:❌ ========================================= Extrinsic Parameter Only Models