def measure_dendrogram_properties(dend=None, cube303=cube303, cube321=cube321, cube13co=cube13co, cube18co=cube18co, noise_cube=noise_cube, sncube=sncube, suffix="", last_index=None, plot_some=True, line='303', write=True): assert (cube321.shape == cube303.shape == noise_cube.shape == cube13co.shape == cube18co.shape == sncube.shape) assert sncube.wcs is cube303.wcs is sncube.mask._wcs metadata = {} metadata['data_unit'] = u.K metadata['spatial_scale'] = 7.2 * u.arcsec metadata['beam_major'] = 30 * u.arcsec metadata['beam_minor'] = 30 * u.arcsec metadata['wavelength'] = 218.22219*u.GHz metadata['velocity_scale'] = u.km/u.s metadata['wcs'] = cube303.wcs keys = [ 'density_chi2', 'expected_density', 'dmin1sig_chi2', 'dmax1sig_chi2', 'column_chi2', 'expected_column', 'cmin1sig_chi2', 'cmax1sig_chi2', 'temperature_chi2', 'expected_temperature', 'tmin1sig_chi2', 'tmax1sig_chi2', 'eratio321303', 'ratio321303', 'logh2column', 'elogh2column', 'logabundance', 'elogabundance', ] obs_keys = [ 'Stot303', 'Smin303', 'Smax303', 'Stot321', 'Smean303', 'Smean321', 'npix', 'e303', 'e321', 'r321303', 'er321303', '13cosum', 'c18osum', '13comean', 'c18omean', 's_ntotal', 'index', 'is_leaf', 'parent', 'root', 'lon', 'lat', 'vcen', 'higaldusttem', 'reff', 'dustmass', 'dustmindens', 'bad', #'tkin_turb', ] columns = {k:[] for k in (keys+obs_keys)} log.debug("Initializing dendrogram temperature fitting loop") # FORCE wcs to match # (technically should reproject here) cube13co._wcs = cube18co._wcs = cube303.wcs cube13co.mask._wcs = cube18co.mask._wcs = cube303.wcs if line == '303': maincube = cube303 elif line == '321': maincube = cube321 else: raise ValueError("Unrecognized line: {0}".format(line)) # Prepare an array to hold the fitted temperatures tcubedata = np.empty(maincube.shape, dtype='float32') tcubedata[:] = np.nan tcubeleafdata = np.empty(maincube.shape, dtype='float32') tcubeleafdata[:] = np.nan nbad = 0 catalog = ppv_catalog(dend, metadata) pb = ProgressBar(len(catalog)) for ii,row in enumerate(catalog): structure = dend[row['_idx']] assert structure.idx == row['_idx'] == ii dend_obj_mask = BooleanArrayMask(structure.get_mask(), wcs=cube303.wcs) dend_inds = structure.indices() view = (slice(dend_inds[0].min(), dend_inds[0].max()+1), slice(dend_inds[1].min(), dend_inds[1].max()+1), slice(dend_inds[2].min(), dend_inds[2].max()+1),) #view2 = cube303.subcube_slices_from_mask(dend_obj_mask) submask = dend_obj_mask[view] #assert np.count_nonzero(submask.include()) == np.count_nonzero(dend_obj_mask.include()) sn = sncube[view].with_mask(submask) sntot = sn.sum().value #np.testing.assert_almost_equal(sntot, structure.values().sum(), decimal=0) c303 = cube303[view].with_mask(submask) c321 = cube321[view].with_mask(submask) co13sum = cube13co[view].with_mask(submask).sum().value co18sum = cube18co[view].with_mask(submask).sum().value if hasattr(co13sum,'__len__'): raise TypeError(".sum() applied to an array has yielded a non scalar.") npix = submask.include().sum() assert npix == structure.get_npix() Stot303 = c303.sum().value if np.isnan(Stot303): raise ValueError("NaN in cube. This can't happen: the data from " "which the dendrogram was derived can't have " "NaN pixels.") Smax303 = c303.max().value Smin303 = c303.min().value Stot321 = c321.sum().value if npix == 0: raise ValueError("npix=0. This is impossible.") Smean303 = Stot303/npix if Stot303 <= 0 and line=='303': raise ValueError("The 303 flux is <=0. This isn't possible because " "the dendrogram was derived from the 303 data with a " "non-zero threshold.") elif Stot303 <= 0 and line=='321': Stot303 = 0 Smean303 = 0 elif Stot321 <= 0 and line=='321': raise ValueError("The 321 flux is <=0. This isn't possible because " "the dendrogram was derived from the 321 data with a " "non-zero threshold.") if np.isnan(Stot321): raise ValueError("NaN in 321 line") Smean321 = Stot321/npix #error = (noise_cube[view][submask.include()]).sum() / submask.include().sum()**0.5 var = ((noise_cube[dend_obj_mask.include()]**2).sum() / npix**2) error = var**0.5 if np.isnan(error): raise ValueError("error is nan: this is impossible by definition.") if line == '321' and Stot303 == 0: r321303 = np.nan er321303 = np.nan elif Stot321 < 0: r321303 = error / Smean303 er321303 = (r321303**2 * (var/Smean303**2 + 1))**0.5 else: r321303 = Stot321 / Stot303 er321303 = (r321303**2 * (var/Smean303**2 + var/Smean321**2))**0.5 for c in columns: assert len(columns[c]) == ii columns['index'].append(row['_idx']) columns['s_ntotal'].append(sntot) columns['Stot303'].append(Stot303) columns['Smax303'].append(Smax303) columns['Smin303'].append(Smin303) columns['Stot321'].append(Stot321) columns['Smean303'].append(Smean303) columns['Smean321'].append(Smean321) columns['npix'].append(npix) columns['e303'].append(error) columns['e321'].append(error) columns['r321303'].append(r321303) columns['er321303'].append(er321303) columns['13cosum'].append(co13sum) columns['c18osum'].append(co18sum) columns['13comean'].append(co13sum/npix) columns['c18omean'].append(co18sum/npix) columns['is_leaf'].append(structure.is_leaf) columns['parent'].append(structure.parent.idx if structure.parent else -1) columns['root'].append(get_root(structure).idx) s_main = maincube._data[dend_inds] x,y,z = maincube.world[dend_inds] lon = ((z.value-(360*(z.value>180)))*s_main).sum()/s_main.sum() lat = (y*s_main).sum()/s_main.sum() vel = (x*s_main).sum()/s_main.sum() columns['lon'].append(lon) columns['lat'].append(lat.value) columns['vcen'].append(vel.value) mask2d = dend_obj_mask.include().max(axis=0)[view[1:]] logh2column = np.log10(np.nanmean(column_regridded.data[view[1:]][mask2d]) * 1e22) if np.isnan(logh2column): log.info("Source #{0} has NaNs".format(ii)) logh2column = 24 elogh2column = elogabundance columns['higaldusttem'].append(np.nanmean(dusttem_regridded.data[view[1:]][mask2d])) r_arcsec = row['radius']*u.arcsec reff = (r_arcsec*(8.5*u.kpc)).to(u.pc, u.dimensionless_angles()) mass = ((10**logh2column*u.cm**-2)*np.pi*reff**2*2.8*constants.m_p).to(u.M_sun) density = (mass/(4/3.*np.pi*reff**3)/constants.m_p/2.8).to(u.cm**-3) columns['reff'].append(reff.value) columns['dustmass'].append(mass.value) columns['dustmindens'].append(density.value) mindens = np.log10(density.value) if mindens < 3: mindens = 3 if (r321303 < 0 or np.isnan(r321303)) and line != '321': raise ValueError("Ratio <0: This can't happen any more because " "if either num/denom is <0, an exception is " "raised earlier") #for k in columns: # if k not in obs_keys: # columns[k].append(np.nan) elif (r321303 < 0 or np.isnan(r321303)) and line == '321': for k in keys: columns[k].append(np.nan) else: # Replace negatives for fitting if Smean321 <= 0: Smean321 = error mf.set_constraints(ratio321303=r321303, eratio321303=er321303, #ratio321322=ratio2, eratio321322=eratio2, logh2column=logh2column, elogh2column=elogh2column, logabundance=logabundance, elogabundance=elogabundance, taline303=Smean303, etaline303=error, taline321=Smean321, etaline321=error, mindens=mindens, linewidth=10) row_data = mf.get_parconstraints() row_data['ratio321303'] = r321303 row_data['eratio321303'] = er321303 for k in row_data: columns[k].append(row_data[k]) # Exclude bad velocities from cubes if row['v_cen'] < -80e3 or row['v_cen'] > 180e3: # Skip: there is no real structure down here nbad += 1 is_bad = True else: is_bad = False tcubedata[dend_obj_mask.include()] = row_data['expected_temperature'] if structure.is_leaf: tcubeleafdata[dend_obj_mask.include()] = row_data['expected_temperature'] columns['bad'].append(is_bad) width = row['v_rms']*u.km/u.s lengthscale = reff #REMOVED in favor of despotic version done in dendrograms.py # we use the analytic version here; the despotic version is # computed elsewhere (with appropriate gcor factors) #columns['tkin_turb'].append(heating.tkin_all(10**row_data['density_chi2']*u.cm**-3, # width, # lengthscale, # width/lengthscale, # columns['higaldusttem'][-1]*u.K, # crir=0./u.s)) if len(set(len(c) for k,c in columns.items())) != 1: print("Columns are different lengths. This is not allowed.") import ipdb; ipdb.set_trace() for c in columns: assert len(columns[c]) == ii+1 if plot_some and not is_bad and (ii-nbad % 100 == 0 or ii-nbad < 50): try: log.info("T: [{tmin1sig_chi2:7.2f},{expected_temperature:7.2f},{tmax1sig_chi2:7.2f}]" " R={ratio321303:8.4f}+/-{eratio321303:8.4f}" " Smean303={Smean303:8.4f} +/- {e303:8.4f}" " Stot303={Stot303:8.2e} npix={npix:6d}" .format(Smean303=Smean303, Stot303=Stot303, npix=npix, e303=error, **row_data)) pl.figure(1) pl.clf() mf.denstemplot() pl.savefig(fpath("dendrotem/diagnostics/{0}_{1}.png".format(suffix,ii))) pl.figure(2).clf() mf.parplot1d_all(levels=[0.68268949213708585]) pl.savefig(fpath("dendrotem/diagnostics/1dplot{0}_{1}.png".format(suffix,ii))) pl.draw() pl.show() except Exception as ex: print ex pass else: pb.update(ii+1) if last_index is not None and ii >= last_index: break if last_index is not None: catalog = catalog[:last_index+1] for k in columns: if k not in catalog.keys(): catalog.add_column(table.Column(name=k, data=columns[k])) for mid,lo,hi,letter in (('expected_temperature','tmin1sig_chi2','tmax1sig_chi2','t'), ('expected_density','dmin1sig_chi2','dmax1sig_chi2','d'), ('expected_column','cmin1sig_chi2','cmax1sig_chi2','c')): catalog.add_column(table.Column(name='elo_'+letter, data=catalog[mid]-catalog[lo])) catalog.add_column(table.Column(name='ehi_'+letter, data=catalog[hi]-catalog[mid])) if write: catalog.write(tpath('PPV_H2CO_Temperature{0}.ipac'.format(suffix)), format='ascii.ipac') # Note that there are overlaps in the catalog, which means that ORDER MATTERS # in the above loop. I haven't yet checked whether large scale overwrites # small or vice-versa; it may be that both views of the data are interesting. tcube = SpectralCube(data=tcubedata, wcs=cube303.wcs, mask=cube303.mask, meta={'unit':'K'}, header=cube303.header, ) tcubeleaf = SpectralCube(data=tcubeleafdata, wcs=cube303.wcs, mask=cube303.mask, meta={'unit':'K'}, header=cube303.header, ) if write: log.info("Writing TemperatureCube") outpath = 'TemperatureCube_DendrogramObjects{0}.fits' tcube.write(hpath(outpath.format(suffix)), overwrite=True) outpath_leaf = 'TemperatureCube_DendrogramObjects{0}_leaves.fits' tcubeleaf.write(hpath(outpath_leaf.format(suffix)), overwrite=True) return catalog, tcube
dendsm, ), ): # reset data tcubedata = np.empty(cubeA.shape) tcubedata[:] = np.nan rcubedata = np.empty(cubeA.shape) rcubedata[:] = np.nan pb = ProgressBar(len(objects)) for ii, structure in enumerate(objects): dend_obj_mask = BooleanArrayMask(structure.get_mask(), wcs=cubeA.wcs) view = cubeA.subcube_slices_from_mask(dend_obj_mask) submask = dend_obj_mask[view] assert submask.include().sum() == dend_obj_mask.include().sum() c303 = cubeA[view].with_mask(submask) c321 = cubeB[view].with_mask(submask) npix = submask.include().sum() Stot303 = c303.sum().value Stot321 = c321.sum().value if npix == 0: raise ValueError("npix=0. This is impossible.") Smean303 = Stot303 / npix Smean321 = Stot321 / npix try: r321303 = Stot321 / Stot303 except ZeroDivisionError: # py.FuckOff...
def measure_dendrogram_properties(dend=None, cube303=cube303, cube321=cube321, cube13co=cube13co, cube18co=cube18co, noise_cube=noise_cube, sncube=sncube, suffix="", last_index=None, plot_some=True, line='303', write=True): assert (cube321.shape == cube303.shape == noise_cube.shape == cube13co.shape == cube18co.shape == sncube.shape) assert sncube.wcs is cube303.wcs is sncube.mask._wcs metadata = {} metadata['data_unit'] = u.K metadata['spatial_scale'] = 7.2 * u.arcsec metadata['beam_major'] = 30 * u.arcsec metadata['beam_minor'] = 30 * u.arcsec metadata['wavelength'] = 218.22219 * u.GHz metadata['velocity_scale'] = u.km / u.s metadata['wcs'] = cube303.wcs keys = [ 'density_chi2', 'expected_density', 'dmin1sig_chi2', 'dmax1sig_chi2', 'column_chi2', 'expected_column', 'cmin1sig_chi2', 'cmax1sig_chi2', 'temperature_chi2', 'expected_temperature', 'tmin1sig_chi2', 'tmax1sig_chi2', 'eratio321303', 'ratio321303', 'logh2column', 'elogh2column', 'logabundance', 'elogabundance', ] obs_keys = [ 'Stot303', 'Smin303', 'Smax303', 'Stot321', 'Smean303', 'Smean321', 'npix', 'e303', 'e321', 'r321303', 'er321303', '13cosum', 'c18osum', '13comean', 'c18omean', 's_ntotal', 'index', 'is_leaf', 'parent', 'root', 'lon', 'lat', 'vcen', 'higaldusttem', 'reff', 'dustmass', 'dustmindens', 'bad', #'tkin_turb', ] columns = {k: [] for k in (keys + obs_keys)} log.debug("Initializing dendrogram temperature fitting loop") # FORCE wcs to match # (technically should reproject here) cube13co._wcs = cube18co._wcs = cube303.wcs cube13co.mask._wcs = cube18co.mask._wcs = cube303.wcs if line == '303': maincube = cube303 elif line == '321': maincube = cube321 else: raise ValueError("Unrecognized line: {0}".format(line)) # Prepare an array to hold the fitted temperatures tcubedata = np.empty(maincube.shape, dtype='float32') tcubedata[:] = np.nan tcubeleafdata = np.empty(maincube.shape, dtype='float32') tcubeleafdata[:] = np.nan nbad = 0 catalog = ppv_catalog(dend, metadata) pb = ProgressBar(len(catalog)) for ii, row in enumerate(catalog): structure = dend[row['_idx']] assert structure.idx == row['_idx'] == ii dend_obj_mask = BooleanArrayMask(structure.get_mask(), wcs=cube303.wcs) dend_inds = structure.indices() view = ( slice(dend_inds[0].min(), dend_inds[0].max() + 1), slice(dend_inds[1].min(), dend_inds[1].max() + 1), slice(dend_inds[2].min(), dend_inds[2].max() + 1), ) #view2 = cube303.subcube_slices_from_mask(dend_obj_mask) submask = dend_obj_mask[view] #assert np.count_nonzero(submask.include()) == np.count_nonzero(dend_obj_mask.include()) sn = sncube[view].with_mask(submask) sntot = sn.sum().value #np.testing.assert_almost_equal(sntot, structure.values().sum(), decimal=0) c303 = cube303[view].with_mask(submask) c321 = cube321[view].with_mask(submask) co13sum = cube13co[view].with_mask(submask).sum().value co18sum = cube18co[view].with_mask(submask).sum().value if hasattr(co13sum, '__len__'): raise TypeError( ".sum() applied to an array has yielded a non scalar.") npix = submask.include().sum() assert npix == structure.get_npix() Stot303 = c303.sum().value if np.isnan(Stot303): raise ValueError("NaN in cube. This can't happen: the data from " "which the dendrogram was derived can't have " "NaN pixels.") Smax303 = c303.max().value Smin303 = c303.min().value Stot321 = c321.sum().value if npix == 0: raise ValueError("npix=0. This is impossible.") Smean303 = Stot303 / npix if Stot303 <= 0 and line == '303': raise ValueError( "The 303 flux is <=0. This isn't possible because " "the dendrogram was derived from the 303 data with a " "non-zero threshold.") elif Stot303 <= 0 and line == '321': Stot303 = 0 Smean303 = 0 elif Stot321 <= 0 and line == '321': raise ValueError( "The 321 flux is <=0. This isn't possible because " "the dendrogram was derived from the 321 data with a " "non-zero threshold.") if np.isnan(Stot321): raise ValueError("NaN in 321 line") Smean321 = Stot321 / npix #error = (noise_cube[view][submask.include()]).sum() / submask.include().sum()**0.5 var = ((noise_cube[dend_obj_mask.include()]**2).sum() / npix**2) error = var**0.5 if np.isnan(error): raise ValueError("error is nan: this is impossible by definition.") if line == '321' and Stot303 == 0: r321303 = np.nan er321303 = np.nan elif Stot321 < 0: r321303 = error / Smean303 er321303 = (r321303**2 * (var / Smean303**2 + 1))**0.5 else: r321303 = Stot321 / Stot303 er321303 = (r321303**2 * (var / Smean303**2 + var / Smean321**2))**0.5 for c in columns: assert len(columns[c]) == ii columns['index'].append(row['_idx']) columns['s_ntotal'].append(sntot) columns['Stot303'].append(Stot303) columns['Smax303'].append(Smax303) columns['Smin303'].append(Smin303) columns['Stot321'].append(Stot321) columns['Smean303'].append(Smean303) columns['Smean321'].append(Smean321) columns['npix'].append(npix) columns['e303'].append(error) columns['e321'].append(error) columns['r321303'].append(r321303) columns['er321303'].append(er321303) columns['13cosum'].append(co13sum) columns['c18osum'].append(co18sum) columns['13comean'].append(co13sum / npix) columns['c18omean'].append(co18sum / npix) columns['is_leaf'].append(structure.is_leaf) columns['parent'].append( structure.parent.idx if structure.parent else -1) columns['root'].append(get_root(structure).idx) s_main = maincube._data[dend_inds] x, y, z = maincube.world[dend_inds] lon = ((z.value - (360 * (z.value > 180))) * s_main).sum() / s_main.sum() lat = (y * s_main).sum() / s_main.sum() vel = (x * s_main).sum() / s_main.sum() columns['lon'].append(lon) columns['lat'].append(lat.value) columns['vcen'].append(vel.value) mask2d = dend_obj_mask.include().max(axis=0)[view[1:]] logh2column = np.log10( np.nanmean(column_regridded.data[view[1:]][mask2d]) * 1e22) if np.isnan(logh2column): log.info("Source #{0} has NaNs".format(ii)) logh2column = 24 elogh2column = elogabundance columns['higaldusttem'].append( np.nanmean(dusttem_regridded.data[view[1:]][mask2d])) r_arcsec = row['radius'] * u.arcsec reff = (r_arcsec * (8.5 * u.kpc)).to(u.pc, u.dimensionless_angles()) mass = ((10**logh2column * u.cm**-2) * np.pi * reff**2 * 2.8 * constants.m_p).to(u.M_sun) density = (mass / (4 / 3. * np.pi * reff**3) / constants.m_p / 2.8).to( u.cm**-3) columns['reff'].append(reff.value) columns['dustmass'].append(mass.value) columns['dustmindens'].append(density.value) mindens = np.log10(density.value) if mindens < 3: mindens = 3 if (r321303 < 0 or np.isnan(r321303)) and line != '321': raise ValueError("Ratio <0: This can't happen any more because " "if either num/denom is <0, an exception is " "raised earlier") #for k in columns: # if k not in obs_keys: # columns[k].append(np.nan) elif (r321303 < 0 or np.isnan(r321303)) and line == '321': for k in keys: columns[k].append(np.nan) else: # Replace negatives for fitting if Smean321 <= 0: Smean321 = error mf.set_constraints( ratio321303=r321303, eratio321303=er321303, #ratio321322=ratio2, eratio321322=eratio2, logh2column=logh2column, elogh2column=elogh2column, logabundance=logabundance, elogabundance=elogabundance, taline303=Smean303, etaline303=error, taline321=Smean321, etaline321=error, mindens=mindens, linewidth=10) row_data = mf.get_parconstraints() row_data['ratio321303'] = r321303 row_data['eratio321303'] = er321303 for k in row_data: columns[k].append(row_data[k]) # Exclude bad velocities from cubes if row['v_cen'] < -80e3 or row['v_cen'] > 180e3: # Skip: there is no real structure down here nbad += 1 is_bad = True else: is_bad = False tcubedata[ dend_obj_mask.include()] = row_data['expected_temperature'] if structure.is_leaf: tcubeleafdata[dend_obj_mask.include( )] = row_data['expected_temperature'] columns['bad'].append(is_bad) width = row['v_rms'] * u.km / u.s lengthscale = reff #REMOVED in favor of despotic version done in dendrograms.py # we use the analytic version here; the despotic version is # computed elsewhere (with appropriate gcor factors) #columns['tkin_turb'].append(heating.tkin_all(10**row_data['density_chi2']*u.cm**-3, # width, # lengthscale, # width/lengthscale, # columns['higaldusttem'][-1]*u.K, # crir=0./u.s)) if len(set(len(c) for k, c in columns.items())) != 1: print("Columns are different lengths. This is not allowed.") import ipdb ipdb.set_trace() for c in columns: assert len(columns[c]) == ii + 1 if plot_some and not is_bad and (ii - nbad % 100 == 0 or ii - nbad < 50): try: log.info( "T: [{tmin1sig_chi2:7.2f},{expected_temperature:7.2f},{tmax1sig_chi2:7.2f}]" " R={ratio321303:8.4f}+/-{eratio321303:8.4f}" " Smean303={Smean303:8.4f} +/- {e303:8.4f}" " Stot303={Stot303:8.2e} npix={npix:6d}".format( Smean303=Smean303, Stot303=Stot303, npix=npix, e303=error, **row_data)) pl.figure(1) pl.clf() mf.denstemplot() pl.savefig( fpath("dendrotem/diagnostics/{0}_{1}.png".format( suffix, ii))) pl.figure(2).clf() mf.parplot1d_all(levels=[0.68268949213708585]) pl.savefig( fpath("dendrotem/diagnostics/1dplot{0}_{1}.png".format( suffix, ii))) pl.draw() pl.show() except Exception as ex: print ex pass else: pb.update(ii + 1) if last_index is not None and ii >= last_index: break if last_index is not None: catalog = catalog[:last_index + 1] for k in columns: if k not in catalog.keys(): catalog.add_column(table.Column(name=k, data=columns[k])) for mid, lo, hi, letter in (('expected_temperature', 'tmin1sig_chi2', 'tmax1sig_chi2', 't'), ('expected_density', 'dmin1sig_chi2', 'dmax1sig_chi2', 'd'), ('expected_column', 'cmin1sig_chi2', 'cmax1sig_chi2', 'c')): catalog.add_column( table.Column(name='elo_' + letter, data=catalog[mid] - catalog[lo])) catalog.add_column( table.Column(name='ehi_' + letter, data=catalog[hi] - catalog[mid])) if write: catalog.write(tpath('PPV_H2CO_Temperature{0}.ipac'.format(suffix)), format='ascii.ipac') # Note that there are overlaps in the catalog, which means that ORDER MATTERS # in the above loop. I haven't yet checked whether large scale overwrites # small or vice-versa; it may be that both views of the data are interesting. tcube = SpectralCube( data=tcubedata, wcs=cube303.wcs, mask=cube303.mask, meta={'unit': 'K'}, header=cube303.header, ) tcubeleaf = SpectralCube( data=tcubeleafdata, wcs=cube303.wcs, mask=cube303.mask, meta={'unit': 'K'}, header=cube303.header, ) if write: log.info("Writing TemperatureCube") outpath = 'TemperatureCube_DendrogramObjects{0}.fits' tcube.write(hpath(outpath.format(suffix)), overwrite=True) outpath_leaf = 'TemperatureCube_DendrogramObjects{0}_leaves.fits' tcubeleaf.write(hpath(outpath_leaf.format(suffix)), overwrite=True) return catalog, tcube
(cube303,cube303sm,), (cube321,cube321sm,), (dend,dendsm,),): # reset data tcubedata = np.empty(cubeA.shape) tcubedata[:] = np.nan rcubedata = np.empty(cubeA.shape) rcubedata[:] = np.nan pb = ProgressBar(len(objects)) for ii,structure in enumerate(objects): dend_obj_mask = BooleanArrayMask(structure.get_mask(), wcs=cubeA.wcs) view = cubeA.subcube_slices_from_mask(dend_obj_mask) submask = dend_obj_mask[view] assert submask.include().sum() == dend_obj_mask.include().sum() c303 = cubeA[view].with_mask(submask) c321 = cubeB[view].with_mask(submask) npix = submask.include().sum() Stot303 = c303.sum().value Stot321 = c321.sum().value if npix == 0: raise ValueError("npix=0. This is impossible.") Smean303 = Stot303/npix Smean321 = Stot321/npix try: r321303 = Stot321/Stot303 except ZeroDivisionError: # py.FuckOff...