def test_par_io():
bm = BitMask.from_par_file(data_test_file('imagebitmask.par'), 'IMAGEMASK')
assert list(bm.keys()) == ['BPM', 'COSMIC', 'SATURATED']
assert list(bm.bits.values()) == [0, 1, 2]
bm = ImageBitMaskFromPar()
assert list(bm.keys()) == ['BPM', 'COSMIC', 'SATURATED']
assert list(bm.bits.values()) == [0, 1, 2]
def test_hdr_io():
image_bm = ImageBitMask()
hdr = fits.Header()
image_bm.to_header(hdr)
assert list(hdr.keys()) == ['BIT0', 'BIT1', 'BIT2']
assert list(hdr.values()) == ['BPM', 'COSMIC', 'SATURATED']
bm = BitMask.from_header(hdr)
assert list(bm.bits.keys()) == ['BPM', 'COSMIC', 'SATURATED']
assert list(bm.bits.values()) == [0, 1, 2]
def mpl8_main_sample(file):
mpl8 = pd.read_csv(file, comment='#')
tab = mpl8[(((mpl8.mngtarg1 != 0) | (mpl8.mngtarg3 != 0)) &
((mpl8.mngtarg3 & int(2)**int(19) + int(2)**int(20)) == 0))]
sdssMaskbits = os.path.join(os.environ['IDLUTILS_DIR'], 'data', 'sdss',
'sdssMaskbits.par')
bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET1')
main_sample = bm.flagged(
tab.mngtarg1,
flag=['PRIMARY_v1_2_0', 'SECONDARY_v1_2_0', 'COLOR_ENHANCED_v1_2_0'])
# Removing ancillary progs and repeat obs.
mpl8_main = tab[main_sample]
mpl8_main = mpl8_main.iloc[np.unique(mpl8_main.mangaid.values,
return_index=True)[1]]
return mpl8_main
def __init__(self):
tmp = BitMask.from_par_file(data_test_file('imagebitmask.par'), 'IMAGEMASK')
super(ImageBitMaskFromPar, self).__init__(tmp.keys(), descr=tmp.descr)
def test_ini_io():
bm = BitMask.from_ini_file(data_test_file('imagebitmask.ini'))
assert list(bm.bits.keys()) == ['BPM', 'COSMIC', 'SATURATED']
assert list(bm.bits.values()) == [0, 1, 2]
def __init__(self, sdss_maskbits=None, trgt='MANGA_TARGET1'):
_sdss_maskbits = defaults.sdss_maskbits_file() if sdss_maskbits is None else sdss_maskbits
tmp = BitMask.from_par_file(str(_sdss_maskbits), trgt)
keys, descr = tmp._init_objs()
super(MaNGATargetBitMask, self).__init__(keys, descr=descr)
def channel_bitmask():
return BitMask(['ERRORS', 'MASKED'])
import numpy
from astropy.io import fits
from matplotlib import pyplot
from mangadap.util.bitmask import BitMask
from mangadap.config import defaults
sdssbits_file = defaults.sdss_maskbits_file()
targ1bm = BitMask.from_par_file(sdssbits_file, 'MANGA_TARGET1')
targ2bm = BitMask.from_par_file(sdssbits_file, 'MANGA_TARGET2')
targ3bm = BitMask.from_par_file(sdssbits_file, 'MANGA_TARGET3')
hdu = fits.open('drpall-v3_1_1.fits')
#print(hdu['MANGA'].columns.names)
indx = hdu['MANGA'].data['z'] < 0
mngtarg1 = hdu['MANGA'].data['mngtarg1'][indx]
mngtarg2 = hdu['MANGA'].data['mngtarg2'][indx]
mngtarg3 = hdu['MANGA'].data['mngtarg3'][indx]
print('MANGA_TARGET1')
for b in numpy.unique(mngtarg1):
print(b, targ1bm.flagged_bits(b))
print('MANGA_TARGET2')
for b in numpy.unique(mngtarg2):
print(b, targ2bm.flagged_bits(b))
def radial_coverage_histogram(dapall, daptype, ofile=None):
# Must have finished, be of the correct daptype, and *not* be
# critical
indx = (dapall['DAPDONE'] == 1) & (dapall['DAPTYPE'] == daptype) \
& numpy.invert(DAPQualityBitMask().flagged(dapall['DAPQUAL'], 'CRITICAL'))
# Get the range
arcsec_rng = growth_lim(dapall['RCOV90'][indx], 0.99, fac=1.1)
rore = numpy.ma.divide(dapall['RCOV90'][indx],
dapall['NSA_ELPETRO_TH50_R'][indx])
rore[dapall['NSA_ELPETRO_TH50_R'][indx] < 0] = numpy.ma.masked
rore_rng = growth_lim(rore, 0.99, fac=1.1)
# Determine the sample
sdssMaskbits = defaults.sdss_maskbits_file()
targ1bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET1')
ancillary = dapall['MNGTARG3'][indx] > 0
primaryplus = targ1bm.flagged(
dapall['MNGTARG1'][indx],
flag=['PRIMARY_v1_2_0', 'COLOR_ENHANCED_v1_2_0'])
secondary = targ1bm.flagged(dapall['MNGTARG1'][indx],
flag='SECONDARY_v1_2_0')
other = numpy.invert(ancillary) & numpy.invert(primaryplus) & numpy.invert(
secondary)
print('Num. of observations with 90% coverage <1 arcsec: {0}'.format(
numpy.sum(dapall['RCOV90'][indx] < 1)))
font = {'size': 10}
rc('font', **font)
# Instantiate the figure
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5 * w, 1.5 * h))
nlim = [0.8, 1000]
# Show RCOV90 in terms of arcseconds for each IFU
ax = init_ax(fig, [0.1, 0.58, 0.8, 0.4])
ax.set_ylim(nlim)
ax.set_yscale('log', nonpositive='clip')
ifu = [19, 37, 61, 91, 127]
for i, f in enumerate(ifu):
_indx = indx & ((dapall['IFUDESIGN'] / 100).astype(int) == f)
if not numpy.any(_indx):
continue
ax.hist(dapall['RCOV90'][_indx],
bins=50,
range=arcsec_rng,
alpha=0.5,
histtype='stepfilled',
color='C{0}'.format(i),
zorder=2)
ax.hist(dapall['RCOV90'][_indx],
bins=50,
range=arcsec_rng,
histtype='step',
color='C{0}'.format(i),
zorder=3)
medp = numpy.median(dapall['RCOV90'][_indx])
ax.plot([medp, medp],
nlim,
color='C{0}'.format(i),
zorder=4,
linestyle='--')
ax.text(-0.1,
0.5,
'N',
ha='center',
va='center',
transform=ax.transAxes,
rotation='vertical')
ax.text(0.5,
-0.13,
r'$R_{90}$ (arcsec)',
ha='center',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.95,
'19-fiber',
color='C0',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.90,
'37-fiber',
color='C1',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.85,
'61-fiber',
color='C2',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.80,
'91-fiber',
color='C3',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.75,
'127-fiber',
color='C4',
ha='left',
va='center',
transform=ax.transAxes)
# Show RCOV90 in terms of Re for each sample
ax = init_ax(fig, [0.1, 0.08, 0.8, 0.4])
ax.set_ylim(nlim)
ax.set_yscale('log', nonpositive='clip')
if numpy.any(primaryplus):
_rore = (rore[primaryplus]).compressed()
ax.hist(_rore,
bins=50,
range=rore_rng,
alpha=0.5,
histtype='stepfilled',
color='C0',
zorder=2)
ax.hist(_rore,
bins=50,
range=rore_rng,
histtype='step',
color='C0',
zorder=3)
medp = numpy.median(_rore)
ax.plot([medp, medp], nlim, color='C0', zorder=4, linestyle='--')
if numpy.any(secondary):
_rore = (rore[secondary]).compressed()
ax.hist(_rore,
bins=50,
range=rore_rng,
alpha=0.5,
histtype='stepfilled',
color='C1',
zorder=2)
ax.hist(_rore,
bins=50,
range=rore_rng,
histtype='step',
color='C1',
zorder=3)
medp = numpy.median(_rore)
ax.plot([medp, medp], nlim, color='C1', zorder=4, linestyle='--')
if numpy.any(other):
_rore = (rore[other]).compressed()
ax.hist(_rore,
bins=50,
range=rore_rng,
alpha=0.5,
histtype='stepfilled',
color='C2',
zorder=2)
ax.hist(_rore,
bins=50,
range=rore_rng,
histtype='step',
color='C2',
zorder=3)
if numpy.any(ancillary):
_rore = (rore[ancillary]).compressed()
ax.hist(_rore,
bins=50,
range=rore_rng,
alpha=0.5,
histtype='stepfilled',
color='C3',
zorder=2)
ax.hist(_rore,
bins=50,
range=rore_rng,
histtype='step',
color='C3',
zorder=3)
ax.text(-0.1,
0.5,
'N',
ha='center',
va='center',
transform=ax.transAxes,
rotation='vertical')
ax.text(0.5,
-0.12,
r'$R_{90}/R_{eff}$',
ha='center',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.95,
'Primary+',
color='C0',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.90,
'Secondary',
color='C1',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.85,
'Other',
color='C2',
ha='left',
va='center',
transform=ax.transAxes)
ax.text(0.05,
0.80,
'Ancillary',
color='C3',
ha='left',
va='center',
transform=ax.transAxes)
if ofile is None:
pyplot.show()
else:
print('Writing: {0}'.format(ofile))
fig.canvas.print_figure(ofile, bbox_inches='tight')
fig.clear()
pyplot.close(fig)
from mangadap.util.bitmask import BitMask
import os
from astropy.io import fits
import sys
# ---------------------------------------------------------------------------------------
# defining number of processes to use. since this is local, lets not go crazy.
processes = os.cpu_count()
# ---------------------------------------------------------------------------------------
# Setting up environment of loading in non-critical files.
drpall = fits.open('/Users/cd201/Data/MaNGA/MPL-8-DAP/find_maps/drpall-v2_5_3.fits')[1].data
gal = drpall[(((drpall.mngtarg1 != 0) | (drpall.mngtarg3 != 0)) & ((drpall.mngtarg3 & int(2)**int(19)+int(2)**int(20)) == 0) )]
sdssMaskbits = os.path.join(os.environ['IDLUTILS_DIR'], 'data', 'sdss', 'sdssMaskbits.par')
bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_DRP3QUAL')
critical = bm.flagged(gal.drp3qual, flag='CRITICAL')
data_dir = '/Users/cd201/Data/MaNGA/MPL-8-DAP/VOR10-GAU-MILESHC/'
# ---------------------------------------------------------------------------------------
# Defining wrapper function for multiprocessing.
def multiproc_wrapper(gal_str):
file_name = 'manga-'+gal_str+'-MAPS-VOR10-MILESHC-MILESHC.fits.gz'
file_path = os.path.join(data_dir, gal_str.split("-")[0], gal_str.split("-")[1],file_name)
plt.close()
stellar_pa, halpha_pa = pa_funcs.pa_wrapper(file_path, plot=True)
plt.savefig('/Users/cd201/mpl8-kin-mis/plots/pa_fits/'+gal_str+'-PA.pdf', format='pdf', bbox_inches='tight')
return stellar_pa, halpha_pa
# ---------------------------------------------------------------------------------------
def find_repeat_observations(output_file, drpver, redux_path, dapver,
analysis_path, directory_path):
# Get the DRPComplete database
drpc = DRPComplete(drpver=drpver,
redux_path=redux_path,
dapver=dapver,
analysis_path=analysis_path,
directory_path=directory_path,
readonly=True)
try:
drpc._confirm_access()
except FileNotFoundError:
warnings.warn(
'DRPComplete file must be present to find repeat observations!')
raise
except:
raise
# Get the maskbits file
sdssMaskbits = defaults.sdss_maskbits_file()
# Instantiate the BitMask object
mngtarg1_bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET1')
mngtarg3_bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET3')
print('# DRPComplete file: {0}'.format(drpc.file_path()))
print('# Total number of DRPComplete entries: {0}'.format(drpc.nobs))
valid_galaxy = ((drpc['MANGA_TARGET1'] > 0) |
(drpc['MANGA_TARGET3'] > 0)) & (drpc['VEL'] > 0)
ngal = numpy.sum(valid_galaxy)
print('# Number of valid galaxies: {0}'.format(ngal))
mid = drpc['MANGAID'][valid_galaxy].copy()
plt = drpc['PLATE'][valid_galaxy].copy()
ifu = drpc['IFUDESIGN'][valid_galaxy].copy()
# Find the unique MaNGA IDs
unid, unid_indx, unid_invs, unid_cnts = numpy.unique(mid,
return_index=True,
return_inverse=True,
return_counts=True)
with open(output_file, 'w') as f:
double_print(f,
'# Number of DRPComplete entries: {0}'.format(mid.size))
double_print(f, '# Number of unique MaNGA IDs: {0}'.format(unid.size))
max_rep = numpy.amax(unid_cnts)
double_print(
f,
'# Maximum number of repeated observations: {0}'.format(max_rep))
double_print(f, '#')
double_print(f, '#{0:>9s}'.format('MANGAID'), end='')
for i in range(max_rep):
double_print(f,
' {1:>4s}{0} {2:>4s}{0}'.format(i + 1, 'PLT', 'IFU'),
end='')
double_print(f, '')
for i in range(len(unid)):
if unid_cnts[i] == 1:
continue
indx = numpy.where(mid == unid[i])[0]
nempty = max_rep - len(indx)
double_print(f, ' {0:>9s}'.format(unid[i]), end='')
for j in range(len(indx)):
double_print(f,
' {0:>5} {1:>5}'.format(plt[indx[j]],
ifu[indx[j]]),
end='')
if nempty > 0:
for j in range(nempty):
double_print(f, ' {0:>5} {1:>5}'.format(-1, -1), end='')
double_print(f, '')
def radial_coverage_histogram(dapall, plot_file=None):
# Get the range
arcsec_rng = growth_lim(dapall['RCOV90'], 0.99, fac=1.1)
rore = numpy.ma.divide(dapall['RCOV90'], dapall['NSA_ELPETRO_TH50_R'])
rore[dapall['NSA_ELPETRO_TH50_R'] < 0] = numpy.ma.masked
rore_rng = growth_lim(rore, 0.99, fac=1.1)
# Determine the sample
sdssMaskbits = sdss_maskbits_file()
targ1bm = BitMask.from_par_file(sdssMaskbits, 'MANGA_TARGET1')
ancillary = dapall['MNGTARG3'] > 0
primaryplus = targ1bm.flagged(dapall['MNGTARG1'],
flag=['PRIMARY_v1_2_0', 'COLOR_ENHANCED_v1_2_0'])
secondary = targ1bm.flagged(dapall['MNGTARG1'], flag='SECONDARY_v1_2_0')
other = numpy.invert(ancillary) & numpy.invert(primaryplus) & numpy.invert(secondary)
indx = dapall['RCOV90'] < 1
print('Num. of observations with 90% coverage <1 arcsec: {0}'.format(numpy.sum(indx)))
print(dapall['PLATEIFU'][indx])
# Instantiate the figure
w,h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5*w,1.5*h))
nlim = [0.8, 1000]
# Show RCOV90 in terms of arcseconds for each IFU
ax = init_ax(fig, [0.1, 0.58, 0.8, 0.4])
ax.set_ylim(nlim)
ax.set_yscale('log', nonposy='clip')
ifu = [ 19, 37, 61, 91, 127 ]
for i,f in enumerate(ifu):
indx = (dapall['IFUDESIGN']/100).astype(int) == f
ax.hist(dapall['RCOV90'][indx], bins=50, range=arcsec_rng, alpha=0.5,
histtype='stepfilled', color='C{0}'.format(i), zorder=2)
ax.hist(dapall['RCOV90'][indx], bins=50, range=arcsec_rng,
histtype='step', color='C{0}'.format(i), zorder=3)
medp = numpy.median(dapall['RCOV90'][indx])
ax.plot([medp, medp], nlim, color='C{0}'.format(i), zorder=4, linestyle='--')
ax.text(-0.1, 0.5, 'N', ha='center', va='center', transform=ax.transAxes, rotation='vertical')
ax.text(0.5, -0.13, r'$R_{90}$ (arcsec)', ha='center', va='center', transform=ax.transAxes)
ax.text(0.05, 0.95, '19-fiber', color='C0', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.90, '37-fiber', color='C1', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.85, '61-fiber', color='C2', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.80, '91-fiber', color='C3', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.75, '127-fiber', color='C4', ha='left', va='center', transform=ax.transAxes)
# Show RCOV90 in terms of Re for each sample
ax = init_ax(fig, [0.1, 0.08, 0.8, 0.4])
ax.set_ylim(nlim)
ax.set_yscale('log', nonposy='clip')
ax.hist(rore[primaryplus], bins=50, range=rore_rng, alpha=0.5, histtype='stepfilled',
color='C0', zorder=2)
ax.hist(rore[primaryplus], bins=50, range=rore_rng, histtype='step',
color='C0', zorder=3)
medp = numpy.median(rore[primaryplus])
ax.plot([medp, medp], nlim, color='C0', zorder=4, linestyle='--')
ax.hist(rore[secondary], bins=50, range=rore_rng, alpha=0.5, histtype='stepfilled',
color='C1', zorder=2)
ax.hist(rore[secondary], bins=50, range=rore_rng, histtype='step',
color='C1', zorder=3)
medp = numpy.median(rore[secondary])
ax.plot([medp, medp], nlim, color='C1', zorder=4, linestyle='--')
ax.hist(rore[other], bins=50, range=rore_rng, alpha=0.5, histtype='stepfilled',
color='C2', zorder=2)
ax.hist(rore[other], bins=50, range=rore_rng, histtype='step',
color='C2', zorder=3)
ax.hist(rore[ancillary], bins=50, range=rore_rng, alpha=0.5, histtype='stepfilled',
color='C3', zorder=2)
ax.hist(rore[ancillary], bins=50, range=rore_rng, histtype='step',
color='C3', zorder=3)
ax.text(-0.1, 0.5, 'N', ha='center', va='center', transform=ax.transAxes, rotation='vertical')
ax.text(0.5, -0.12, r'$R_{90}/R_{eff}$', ha='center', va='center', transform=ax.transAxes)
ax.text(0.05, 0.95, 'Primary+', color='C0', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.90, 'Secondary', color='C1', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.85, 'Other', color='C2', ha='left', va='center', transform=ax.transAxes)
ax.text(0.05, 0.80, 'Ancillary', color='C3', ha='left', va='center', transform=ax.transAxes)
if plot_file is None:
pyplot.show()
else:
fig.canvas.print_figure(plot_file, bbox_inches='tight')
fig.clear()
pyplot.close(fig)