Commit d4f7ba7d authored by Daniel Williams's avatar Daniel Williams
Browse files

ADI work.

parent c28e577c

.travis/run_tests.sh

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@@ -7,4 +7,4 @@ else 
    _strict=""

fi



coverage run --source=gwpy -m py.test -v ${_strict} tests/
 
coverage run --source=minke -m py.test -v ${_strict} tests/

README.rst

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@@ -2,6 +2,9 @@ 
Minke: The Burst MDC Factory

===============================



.. image:: https://zenodo.org/badge/53331163.svg

   :target: https://zenodo.org/badge/latestdoi/53331163



.. image:: https://img.shields.io/pypi/v/minke.svg

        :target: https://pypi.python.org/pypi/minke
@@ -16,7 +19,7 @@ Minke: The Burst MDC Factory 
	   :target: https://coveralls.io/github/transientlunatic/minke?branch=master

	   :alt: Coverage status

	      

.. image:: https://code.daniel-williams.co.uk/minke/minke.png

.. image:: https://code.daniel-williams.co.uk/minke/_images/minke.png

	   :alt: Project Minke Logo



Minke is a Python package to produce Mock Data Challenge data sets for LIGO interferometers.
@@ -29,15 +32,8 @@ Features 


* Produces burst MDCs with Gaussian, SineGaussian, and White Noise Burst ad-hoc waveforms

* Produces numerical relativity burst MDCs for supernovae

* Produces numerical relativity burst MDCs for long duration searches

* Produces GWF frame files for MDCs

* Produces GravEn-format log files for MDCs

* Produces hardware-injection ready data files

* Produces SimBurstTable XML files for MDCs
 




Credits

---------



This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.



.. _Cookiecutter: https://github.com/audreyr/cookiecutter

.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage

docs/numerical.rst

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@@ -25,8 +25,12 @@ Dimmelmeier+08 


The rotational core-collapse simulations detailed in Dimmelmeier et

al. (2008) are available from

http://mpa.iwww.mpg.de/177514/Gravitational-Waveform-Catalog

http://mpa.iwww.mpg.de/177514/Gravitational-Waveform-Catalog.





Ott+13

------



The 3D hydrodynamic 3-species neutrino heating waveforms from Ott et

al. (2013) are available from https://stellarcollapse.org/ottetal2013.

minke/sources.py

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@@ -140,9 +140,9 @@ class Waveform(object): 
            hp0, hx0 = hp, hx

        

        # Rescale for a given distance 

        if distance and hasattr(self, supernova): 

            rescale = 1.0 / (self.file_distance / row.amplitude)

            hp, hx, hp0, hx0 = hp * rescale, hx * rescale, hp0 * rescale,hx0 * rescale

        #if distance and hasattr(self, supernova): 

        #    rescale = 1.0 / (self.file_distance / row.amplitude)

        #    hp, hx, hp0, hx0 = hp * rescale, hx * rescale, hp0 * rescale,hx0 * rescale

            

        return hp, hx, hp0, hx0
@@ -541,32 +541,37 @@ class Ott2013(Supernova): 
        self.params['phi'] = phi

        self.params['incl'] = theta

        self.sky_dist = sky_dist

        self.params['numrel_data'] = filepath #decomposed_path #self.numrel_data

        #self.params['numrel_data'] = filepath #decomposed_path #self.numrel_data

        if not decomposed_path : decomposed_path = filepath+".dec"

        if not os.path.isfile(decomposed_path) :

            decomposed = self.decompose(filepath, sample_rate = 16384.0, step_back = 0.01)

            np.savetxt(decomposed_path, decomposed, header="time (2,-2) (2,-1) (2,0) (2,1) (2,2)", fmt='%.8e')

        self.numrel_data = self.params['numrel_data'] = decomposed_path

        self.params['amplitude'] = distance # We store the distance in the amplitude column because there isn't a distance column

        self.params['hrss'] = self.file_distance # Again the hrss value is the distance at which the files are scaled





    def _generate(self):

        """

    # def _generate(self):

    #     """



        Generate the Ott waveforms. This must be performed

        differently to other waveform morphologies, since we require

        the use of pre-generated text files.

    #     Generate the Ott waveforms. This must be performed

    #     differently to other waveform morphologies, since we require

    #     the use of pre-generated text files.



        The filepath and the start of the filenames should be provided in

        the numrel_data column of the SimBurstTable, so we need to contruct

        the rest of the filename from the theta and phi angles, and then load 

        that file.

    #     The filepath and the start of the filenames should be provided in

    #     the numrel_data column of the SimBurstTable, so we need to contruct

    #     the rest of the filename from the theta and phi angles, and then load 

    #     that file.



        """

        theta, phi = self.params['incl'], self.params['phi']

        numrel_file_hp = self.numrel_data + "_costheta{:.3f}_phi{:.3f}-plus.txt".format(theta, phi)

        numrel_file_hx = self.numrel_data + "_costheta{:.3f}_phi{:.3f}-cross.txt".format(theta, phi)

    #     """

    #     theta, phi = self.params['incl'], self.params['phi']

    #     numrel_file_hp = self.numrel_data + "_costheta{:.3f}_phi{:.3f}-plus.txt".format(theta, phi)

    #     numrel_file_hx = self.numrel_data + "_costheta{:.3f}_phi{:.3f}-cross.txt".format(theta, phi)



        data_hp = np.loadtxt(numrel_file_hp)

        data_hx = np.loadtxt(numrel_file_hx)

    #     data_hp = np.loadtxt(numrel_file_hp)

    #     data_hx = np.loadtxt(numrel_file_hx)



        return data_hp, data_hx, data_hp, data_hx

    #     return data_hp, data_hx, data_hp, data_hx



class Mueller2012(Supernova):

    """