def run_correlation(bubble, yso, outfile, binStep=0.2):
"""Cross correlate and write to file
Parameters
----------
bubble : astropy Table
yso : astropy Table
outfile : str
"""
theta, corr, err = calc_corr(bubble, yso, corrType='x',
rSize=50, nbStrap=100, binStep=binStep)
t = Table([theta, corr, err], names=['theta', 'w', 'dw'])
t.write(outfile, format='ascii', delimiter=',')
# do counts for the different source types:
types=np.unique(yso['type'])
counts=np.zeros((len(types),3))
for i in range(0,len(types)):
counts[i,0] = np.size(yso[yso['type'] == types[i]])
# the divSample function is kind of independent but I've included it in the calc_corr file.
dr1_assoc, dr1_assoc2, dr1_control = calc_corr.divSample(yso, dr1)
#sample correlation calls, with optional data file output of the results:
# all MWP bubbles and RMS sources:
mwprms_theta, mwprms_corr, mwprms_err = calc_corr.calc_corr(dr1, yso, corrType='x', rSize=50, nbStrap=100, binStep=0.2)
x=writeData(mwprms_theta, mwprms_corr, mwprms_err, outFile='mwprms_all.dat', bubCat='dr1', ysoCat='rms', rSize=50, nbStrap=100 )
# just the large bubbles and RMS sources:
mwpLrms_theta, mwpLrms_corr, mwpLrms_err = calc_corr.calc_corr(dr1L, yso, corrType='x', rSize=50, nbStrap=100, binStep=0.2)
y=writeData(mwpLrms_theta, mwpLrms_corr, mwpLrms_err, outFile='mwpLrms.dat', bubCat='dr1L', ysoCat='rms', rSize=50, nbStrap=100 )
# RMS YSOs auto-correlations:
ysodr1_theta, ysodr1_acorr, ysodr1_err = calc_corr.calc_corr(dr1, yso, corrType='a', rSize=50, nbStrap=100, binStep=0.5)
z=writeData(ysodr1_theta, ysodr1_acorr, ysodr1_err, outFile='mwpyso_acorr_all.dat', bubCat='dr1', ysoCat='rms all', rSize=50, nbStrap=100 )
# Sample correlation function plot:
mwpFig = plt.figure()
plt.errorbar(mwprms_theta, mwprms_corr, yerr=mwprms_err, c='k', marker='o', ls='None', mew=1.5, mec='k', mfc='None', label='MWP all + RMS YSOs')
plt.errorbar(mwpLrms_theta, mwpLrms_corr, yerr=mwpLrms_err, c='r', marker='x', ls='None', mew=1.5, mec='r', mfc='None', label='MWP-L + RMS YSOs')
# do counts for the different source types:
types=np.unique(yso['type'])
counts=np.zeros((len(types),3))
for i in range(0,len(types)):
counts[i,0] = np.size(yso[yso['type'] == types[i]])
# the divSample function is kind of independent but I've included it in the calc_corr file.
dr1_assoc, dr1_assoc2, dr1_control = calc_corr.divSample(yso, dr1)
#sample correlation calls, with optional data file output of the results:
# all MWP bubbles and RMS sources:
mwprms_theta, mwprms_corr, mwprms_err = calc_corr.calc_corr(dr1, yso, corrType='x', rSize=10, nbStrap=10, binStep=0.2)
#x=writeData(mwprms_theta, mwprms_corr, mwprms_err, outFile='mwprms_all.dat', bubCat='dr1', ysoCat='rms', rSize=50, nbStrap=100 )
# just the large bubbles and RMS sources:
#mwpLrms_theta, mwpLrms_corr, mwpLrms_err = calc_corr.calc_corr(dr1L, yso, corrType='x', rSize=50, nbStrap=100, binStep=0.2)
#y=writeData(mwpLrms_theta, mwpLrms_corr, mwpLrms_err, outFile='mwpLrms.dat', bubCat='dr1L', ysoCat='rms', rSize=50, nbStrap=100 )
# RMS YSOs auto-correlations:
#ysodr1_theta, ysodr1_acorr, ysodr1_err = calc_corr.calc_corr(dr1, yso, corrType='a', rSize=50, nbStrap=100, binStep=0.5)
#z=writeData(ysodr1_theta, ysodr1_acorr, ysodr1_err, outFile='mwpyso_acorr_all.dat', bubCat='dr1', ysoCat='rms all', rSize=50, nbStrap=100 )
# Sample correlation function plot:
mwpFig = plt.figure()
plt.errorbar(mwprms_theta, mwprms_corr, yerr=mwprms_err, c='k', marker='o', ls='None', mew=1.5, mec='k', mfc='None', label='MWP all + RMS YSOs')
# Add any additional clipping criteria here:
# do counts for the different source types:
types = np.unique(yso['type'])
counts = np.zeros((len(types), 3))
for i in range(0, len(types)):
counts[i, 0] = np.size(yso[yso['type'] == types[i]])
# the divSample function is kind of independent but I've included it in the calc_corr file.
dr1_assoc, dr1_assoc2, dr1_control = calc_corr.divSample(yso, dr1)
#sample correlation calls, with optional data file output of the results:
# all MWP bubbles and RMS sources:
mwprms_theta, mwprms_corr, mwprms_err = calc_corr.calc_corr(dr1,
yso,
corrType='x',
rSize=50,
nbStrap=100,
binStep=0.2)
x = writeData(mwprms_theta,
mwprms_corr,
mwprms_err,
outFile='mwprms_all.dat',
bubCat='dr1',
ysoCat='rms',
rSize=50,
nbStrap=100)
# just the large bubbles and RMS sources:
mwpLrms_theta, mwpLrms_corr, mwpLrms_err = calc_corr.calc_corr(dr1L,
yso,
corrType='x',
