コード例 #1
0
ファイル: swave_characterize.py プロジェクト: wafels/eitwave
        print(' - Using the griddata method %s.' % method)
        final[method] = []

        # Which data to use
        for source in analysis_data_sources:
            print('Using the %s data source' % source)
            if source == 'finalmaps':
                # Get the final map out from the wave simulation
                mc = euv_wave_data['finalmaps']

                # Accumulate the data in space and time to increase the signal
                # to noise ratio
                print(' - Performing spatial summing of HPC data.')
                mc = mapcube_tools.accumulate(mapcube_tools.superpixel(mc, spatial_summing), temporal_summing)
                if develop is not None:
                    aware_utils.write_movie(mc, img_filepath + '_accummulated_data')
                # Swing the position of the start of the longitudinal
                # unwrapping
                for ils, longitude_start in enumerate(longitude_starts):

                    # Which angle to start the longitudinal unwrapping
                    transform_hpc2hg_parameters['longitude_start'] = longitude_start

                    # Which version of AWARE to use
                    if aware_version == 0:
                        #
                        # AWARE version 0 - first do the image processing
                        # to isolate the wave front, then do the transformation into
                        # heliographic co-ordinates to measure the wavefront.
                        #
                        print(' - Performing AWARE v0 image processing.')
コード例 #2
0
ファイル: swave_characterize3.py プロジェクト: wafels/eitwave
        # Transform parameters used to convert HPC image data to HG data.
        # The HPC data is transformed to HG using the location below as the
        # "pole" around which the data is transformed
        transform_hpc2hg_parameters['epi_lon'] = euv_wave_data['epi_lon'] * u.deg
        transform_hpc2hg_parameters['epi_lat'] = euv_wave_data['epi_lat'] * u.deg

    # Storage for the results from all methods and polynomial fits
    print(' - Using the griddata method %s.' % griddata_method)

    # Accumulate the data in space and time to increase the signal
    # to noise ratio
    print(' - Performing spatial summing of HPC data.')
    mc = mapcube_tools.accumulate(mapcube_tools.superpixel(hpc_maps, spatial_summing), temporal_summing)
    if develop is not None:
        aware_utils.write_movie(mc, img_filepath + '_accummulated_data')
    # Swing the position of the start of the longitudinal
    # unwrapping
    for ils, longitude_start in enumerate(longitude_starts):

        # Which angle to start the longitudinal unwrapping
        transform_hpc2hg_parameters['longitude_start'] = longitude_start

        # Which version of AWARE to use
        if aware_version == 0:
            #
            # AWARE version 0 - first do the image processing
            # to isolate the wave front, then do the transformation into
            # heliographic co-ordinates to measure the wavefront.
            #
            print(' - Performing AWARE v0 image processing.')
コード例 #3
0
def processing(mc, radii=[[11, 11]*u.degree],
               clip_limit=None,
               histogram_clip=[0.0, 99.],
               func=np.sqrt,
               three_d=False,
               develop=None):
    """
    Image processing steps used to isolate the EUV wave from the data.  Use
    this part of AWARE to perform the image processing steps that segment
    propagating features that brighten new pixels as they propagate.

    Parameters
    ----------

    mc : sunpy.map.MapCube
    radii : list of lists. Each list contains a pair of numbers that describe the
    radius of the median filter and the closing operation
    histogram_clip
    clip_limit :
    func :
    three_d :
    develop :

    """

    # Define the disks that will be used on all the images.
    # The first disk in each pair is the disk that is used by the median
    # filter.  The second disk is used by the morphological closing
    # operation.
    disks = []
    for r in radii:
        e1 = (r[0]/mc[0].scale.x).to('pixel').value  # median circle radius - across wavefront
        e3 = (r[1]/mc[0].scale.x).to('pixel').value  # closing circle width - across wavefront
        disks.append([disk(e1), disk(e3)])

    # For the dump images
    rstring = ''
    for r in radii:
        z = '%i_%i__' % (r[0].value, r[1].value)
        rstring += z

    # Calculate the persistence
    new = mapcube_tools.persistence(mc)
    if develop is not None:
        develop_filepaths = {}
        filename = develop['img'] + '_persistence_mc.mp4'
        print('\nWriting persistence movie to {:s}'.format(filename))
        aware_utils.write_movie(new, filename)

        filename = develop['dat'] + '_persistence_mc.pkl'
        develop_filepaths['persistence_mc'] = filename
        print('\nWriting persistence mapcube to {:s}'.format(filename))
        f = open(filename, 'wb')
        pickle.dump(new, f)
        f.close()

    # Calculate the running difference
    new = mapcube_tools.running_difference(new)
    if develop is not None:
        filename = develop['img'] + '_rdpi_mc.mp4'
        print('\nWriting RDPI movie to {:s}'.format(filename))
        aware_utils.write_movie(new, filename)

        filename = develop['dat'] + '_rdpi_mc.pkl'
        develop_filepaths['rdpi_mc'] = filename
        print('\nWriting RDPI mapcube to {:s}'.format(filename))
        f = open(filename, 'wb')
        pickle.dump(new, f)
        f.close()

    # Storage for the processed mapcube.
    new_mc = []

    # Only want positive differences, so everything lower than zero
    # should be set to zero
    mc_data = func(new.as_array())
    mc_data[mc_data < 0.0] = 0.0

    # Clip the data to be within a range, and then normalize it.
    if clip_limit is None:
        cl = np.nanpercentile(mc_data, histogram_clip)
    mc_data[mc_data > cl[1]] = cl[1]
    mc_data = (mc_data - cl[0]) / (cl[1]-cl[0])

    # Get rid of NaNs
    nans_here = np.logical_not(np.isfinite(mc_data))
    nans_replaced = deepcopy(mc_data)
    nans_replaced[nans_here] = 0.0

    # Clean the data to isolate the wave front.  Use three dimensional
    # operations from scipy.ndimage.  This approach should get rid of
    # more noise and have better continuity in the time-direction.
    final = np.zeros_like(mc_data, dtype=np.float32)

    # Do the cleaning and isolation operations on multiple length-scales,
    # and add up the final results.
    nr = deepcopy(nans_replaced)
    # Use three-dimensional filters
    for j, d in enumerate(disks):
        pancake = np.swapaxes(np.tile(d[0], (3, 1, 1)), 0, -1)

        print('\n', nr.shape, pancake.shape, '\n', 'started median filter.')
        nr = _apply_median_filter(nr, d[0], three_d)
        if develop is not None:
            filename = develop['dat'] + '_np_median_dc_{:n}.npy'.format(j)
            develop_filepaths['np_median_dc'] = filename
            print('\nWriting results of median filter to {:s}'.format(filename))
            f = open(filename, 'wb')
            np.save(f, nr)
            f.close()

        print(' started grey closing.')
        nr = _apply_closing(nr, d[0], three_d)
        if develop is not None:
            filename = develop['dat'] + '_np_closing_dc_{:n}.npy'.format(j)
            develop_filepaths['np_closing_dc'] = filename
            print('\nWriting results of closing to {:s}'.format(filename))
            f = open(filename, 'wb')
            np.save(f, nr)
            f.close()

        # Sum all the
        final += nr*1.0

    # If in development mode, now dump out the meta's and the nans
    if develop:
        filename = develop['dat'] + '_np_meta.pkl'
        develop_filepaths['np_meta'] = filename
        print('\nWriting all meta data information to {:s}'.format(filename))
        f = open(filename, 'wb')
        pickle.dump(mc.all_meta(), f)
        f.close()
        filename = develop['dat'] + '_np_nans.npy'
        develop_filepaths['np_nans'] = filename
        print('\nWriting all nans to {:s}'.format(filename))
        f = open(filename, 'wb')
        np.save(f, nans_here)
        f.close()

    # Create the list that will be turned in to a mapcube
    for i, m in enumerate(new):
        new_map = Map(ma.masked_array(final[:, :, i],
                                          mask=nans_here[:, :, i]),
                          m.meta)
        new_map.plot_settings = deepcopy(m.plot_settings)
        new_mc.append(new_map)

    # Return the cleaned mapcube
    if develop:
        return Map(new_mc, cube=True), develop_filepaths
    else:
        return Map(new_mc, cube=True)
コード例 #4
0
    ta.set_xlabel('x (arcsec)', fontsize=fontsize)
    xtl = ta.axes.xaxis.get_majorticklabels()
    for l in range(0, len(xtl)):
        xtl[l].set_fontsize(0.67*fontsize)
    ta.set_ylabel('y (arcsec)', fontsize=fontsize)
    ytl = ta.axes.yaxis.get_majorticklabels()
    for l in range(0, len(ytl)):
        ytl[l].set_fontsize(0.67*fontsize)

plt.tight_layout()
plt.show()
plt.savefig(os.path.expanduser(image_filepath))
plt.close('all')


n = np_median_dc.shape[2]
mc = []
for i in range(0, n):
    mc.append(sunpy.map.Map(np_median_dc[:, :, i], np_meta[i]))

mc = sunpy.map.Map(mc, cube=True)
aware_utils.write_movie(mc, image_filepath + 'median_0')

n = np_closing_dc.shape[2]
mc = []
for i in range(0, n):
    mc.append(sunpy.map.Map(np_closing_dc[:, :, i], np_meta[i]))

mc = sunpy.map.Map(mc, cube=True)
aware_utils.write_movie(mc, 'closing0')