def test_read_p03_g09():
""" XQ-100 """
p03 = DLASurvey.load_P03()
assert p03.nsys == 105
g09 = DLASurvey.load_G09()
assert g09.nsys == 38
def test_read_hst16():
# Statistical
hst16 = DLASurvey.load_HST16()
assert hst16.nsys == 4
# All
hst16_all = DLASurvey.load_HST16(sample='all')
assert hst16_all.nsys == 48
def test_sdss():
# All
sdss = DLASurvey.load_SDSS_DR5(sample='all')
# Testing
assert sdss.nsys == 1182
# Stat
sdss = DLASurvey.load_SDSS_DR5()
assert len(sdss.NHI) == 737
def test_sdss():
# All
sdss = DLASurvey.load_SDSS_DR5(sample='all')
# Testing
assert sdss.nsys == 1182
# Stat
sdss = DLASurvey.load_SDSS_DR5()
assert len(sdss.NHI) == 737
def neeleman13():
""" Build a summary file for the Neeleman+13 sample
"""
prefix = "H100"
outpath = os.getenv("DROPBOX_DIR") + "/Public/DLA/" + prefix + "/"
dlasurvey = DLASurvey.from_flist("Lists/Neeleman13.lst", tree=os.environ.get("DLA"))
dlasurvey.ref = "Neeleman+13"
# Reset vlim
for dla in dlasurvey._abs_sys:
dla.vlim = [-1000.0, 1000.0] * u.km / u.s
# Mask
dlasurvey.mask = dlasurvey.NHI == dlasurvey.NHI
# Json file for ions
dlasurvey.fill_ions(use_Nfile=True)
mk_json_ions(dlasurvey, prefix, outpath + prefix + "_DLA_ions.json")
# Json files for .clm files
mk_json_clms(dlasurvey, outpath + "CLMS/", prefix)
print("It is likely you wish to tarball the CLMS folder for distribution")
# JSON SYS files (preferred)
mk_json_sys(dlasurvey, outpath, prefix)
# Summary file and spectra
mk_summary(
dlasurvey,
prefix,
outpath + prefix + "_DLA.fits",
specpath=outpath + "/Spectra/",
htmlfil=outpath + prefix + "_DLA.html",
)
def main(args=None):
import pdb
import numpy as np
from linetools import utils as ltu
from pyigm.surveys.analysis import fit_atan_dla_lz, fit_fN_dblpow
from pyigm.surveys.dlasurvey import load_dla_surveys, update_dla_fits
from pyigm.surveys.dlasurvey import DLASurvey
from pyigm.surveys import dlasurvey
pargs = parser()
# DLA l(z) analysis
if pargs.dla_lz or pargs.all:
# arctan from Prochaska & Neeleman 2017
surveys = load_dla_surveys()
dfits, _ = fit_atan_dla_lz(surveys,
nstep=100,
bootstrap=pargs.dla_lz_boot,
nboot=50000,
nproc=pargs.nproc,
boot_out=dlasurvey.lz_boot_file)
# Calculate error
lz_boot = dlasurvey.load_boot_lz()
for key in ['A', 'B', 'C']:
boot = lz_boot[key].data
# 68%
perc = np.percentile(boot, [16., 84.])
dfits['lz']['atan']['sig_{:s}'.format(
key)] = perc - dfits['lz']['atan'][key]
# Write
dfits['lz']['atan']['Ref'] = 'Prochaska & Neeleman 2017'
update_dla_fits(dfits)
# Fit double power law to f(N) of DLA [PW09 only]
if pargs.dla_dpow or pargs.all:
sdss_dr5 = DLASurvey.load_SDSS_DR5()
dfits, best, Ndgrid, a3grid, a4grid, lik = fit_fN_dblpow(sdss_dr5.NHI,
(-3., -1.1),
(-6, -2),
(21., 22.),
nstep=100)
# Write
dfits['fN']['dpow']['Ref'] = 'PHW05'
update_dla_fits(dfits)
# DLA ne/nH
if pargs.dla_nenH or pargs.all:
dfits = {}
dfits['nenH'] = {}
dfits['nenH']['loglog'] = dict(
bp=-2.881,
m=-0.352,
bp_sig=(+0.253, -0.256),
m_sig=(+0.321, -0.317)) # Values with all 50 measurements
dfits['nenH']['loglog']['Ref'] = 'Neeleman+15; PN17'
# Update
update_dla_fits(dfits)
def test_read_h100():
h100 = DLASurvey.load_H100()
assert h100.nsys == 100
SiII_clms = h100.ions((14, 2))
gdSiII = np.where(SiII_clms['flag_N'] > 0)[0]
assert len(gdSiII) == 98
def test_read_h100():
h100 = DLASurvey.load_H100()
assert h100.nsys == 100
SiII_clms = h100.ions((14, 2))
gdSiII = np.where(SiII_clms['flag_N'] > 0)[0]
assert len(gdSiII) == 98
def neeleman13():
""" Build a summary file for the Neeleman+13 sample
"""
prefix = 'H100'
outpath = os.getenv('DROPBOX_DIR') + '/Public/DLA/' + prefix + '/'
dlasurvey = DLASurvey.from_flist('Lists/Neeleman13.lst',
tree=os.environ.get('DLA'))
dlasurvey.ref = 'Neeleman+13'
# Reset vlim
for dla in dlasurvey._abs_sys:
dla.vlim = [-1000., 1000.] * u.km / u.s
# Mask
dlasurvey.mask = dlasurvey.NHI == dlasurvey.NHI
# Json file for ions
dlasurvey.fill_ions(use_Nfile=True)
mk_json_ions(dlasurvey, prefix, outpath + prefix + '_DLA_ions.json')
# Json files for .clm files
mk_json_clms(dlasurvey, outpath + 'CLMS/', prefix)
print('It is likely you wish to tarball the CLMS folder for distribution')
# JSON SYS files (preferred)
mk_json_sys(dlasurvey, outpath, prefix)
# Summary file and spectra
mk_summary(dlasurvey,
prefix,
outpath + prefix + '_DLA.fits',
specpath=outpath + '/Spectra/',
htmlfil=outpath + prefix + '_DLA.html')
def write_sdss_sightlines():
""" Writes the SDSS DR5 sightlines that have no (or very few) DLAs
Returns
-------
None : Writes to Dropbox
"""
import os
import h5py
outfile = os.getenv(
'DROPBOX_DIR') + '/MachineLearning/DR5/SDSS_DR5_noDLAs.hdf5'
# Load
sdss = DLASurvey.load_SDSS_DR5(sample='all')
slines, sdict = grab_sightlines(sdss, flg_bal=0)
coords = SkyCoord(ra=slines['RA'], dec=slines['DEC'], unit='deg')
# Load spectra -- RA/DEC in igmsp is not identical to RA_GROUP, DEC_GROUP in SDSS_DR7
igmsp = IgmSpec()
sdss_meta = igmsp['SDSS_DR7'].meta
qso_coord = SkyCoord(ra=sdss_meta['RA_GROUP'],
dec=sdss_meta['DEC_GROUP'],
unit='deg')
idxq, d2dq, d3dq = match_coordinates_sky(coords, qso_coord, nthneighbor=1)
in_igmsp = d2dq < 1 * u.arcsec # Check
# Cut meta
cut_meta = sdss_meta[idxq[in_igmsp]]
assert len(slines) == len(cut_meta)
# Grab
spectra = igmsp['SDSS_DR7'].spec_from_meta(cut_meta)
# Write
hdf = h5py.File(outfile, 'w')
spectra.write_to_hdf5(outfile, hdf5=hdf, clobber=True, fill_val=0.)
# Add table (meta is already used)
hdf['cut_meta'] = cut_meta
hdf.close()
def test_sdss():
# All
sdss = DLASurvey.load_SDSS_DR5(sample='all')
# Testing
assert sdss.nsys == 1182
# Stat
sdss_stat = DLASurvey.load_SDSS_DR5()
assert len(sdss_stat.NHI) == 737
# Binned
lX, lX_lo, lX_hi = sdss_stat.calculate_lox([2., 2.5, 3])
assert np.isclose(lX[0], 0.04625038, atol=1e-5)
fN, fN_lo, fN_hi = sdss_stat.calculate_fn([20.3, 20.5, 21., 21.5, 22.],
[2, 2.5],
log=True)
assert fN.size == 4
assert np.isclose(fN_lo[0], 0.0682087, atol=1e-5)
def test_dat_list():
"""JXP format :: Likely to be Deprecated
"""
if os.getenv('DLA') is None:
assert True
return
# Load
dlas = DLASurvey.neeleman13_tree()
# tests
assert dlas.nsys == 100
def test_dat_list():
"""JXP format :: Likely to be Deprecated
"""
if os.getenv('DLA') is None:
assert True
return
# Load
dlas = DLASurvey.neeleman13_tree()
# tests
assert dlas.nsys == 100
def main(flg_tst, sdss=None, ml_survey=None):
# Load JSON for DR5
if (flg_tst % 2**1) >= 2**0:
if sdss is None:
sdss = DLASurvey.load_SDSS_DR5()
#ml_survey = json_to_sdss_dlasurvey('../results/dr5_v1_predictions.json', sdss)
ml_survey = json_to_sdss_dlasurvey('../results/dr5_v2_results.json', sdss)
# Vette
if (flg_tst % 2**2) >= 2**1:
if ml_survey is None:
sdss = DLASurvey.load_SDSS_DR5()
ml_survey = json_to_sdss_dlasurvey('../results/dr5_v2_results.json', sdss)
vette_dlasurvey(ml_survey, sdss)
# Vette v5 and generate CSV
if (flg_tst % 2**3) >= 2**2:
if ml_survey is None:
sdss = DLASurvey.load_SDSS_DR5()
ml_survey = json_to_sdss_dlasurvey('../results/dr5_v5_predictions.json', sdss)
false_neg, midx, _ = vette_dlasurvey(ml_survey, sdss)
# CSV of false negatives
mk_false_neg_table(false_neg, '../results/false_negative_DR5_v5.csv')
# Vette v6 and generate CSV
if (flg_tst % 2**4) >= 2**3:
if ml_survey is None:
sdss = DLASurvey.load_SDSS_DR5()
ml_survey = json_to_sdss_dlasurvey('../results/dr5_v6.1_results.json', sdss)
false_neg, midx, _ = vette_dlasurvey(ml_survey, sdss)
# CSV of false negatives
mk_false_neg_table(false_neg, '../results/false_negative_DR5_v6.1.csv')
# Vette gensample v2
if (flg_tst % 2**5) >= 2**4:
if ml_survey is None:
sdss = DLASurvey.load_SDSS_DR5()
ml_survey = json_to_sdss_dlasurvey('../results/results_catalog_dr7_model_gensample_v2.json',sdss)
false_neg, midx, false_pos = vette_dlasurvey(ml_survey, sdss)
# CSV of false negatives
mk_false_neg_table(false_neg, '../results/false_negative_DR5_v2_gen.csv')
mk_false_neg_table(false_pos, '../results/false_positives_DR5_v2_gen.csv')
# Vette gensample v4.3.1
if flg_tst & (2**5):
if ml_survey is None:
sdss = DLASurvey.load_SDSS_DR5()
ml_survey = json_to_sdss_dlasurvey('../results/results_model_4.3.1_data_dr5.json',sdss)
false_neg, midx, false_pos = vette_dlasurvey(ml_survey, sdss)
# CSV of false negatives
mk_false_neg_table(false_neg, '../results/false_negative_DR5_v4.3.1_gen.csv')
mk_false_neg_table(false_pos, '../results/false_positives_DR5_v4.3.1_gen.csv')
if flg_tst & (2**6):
dr5_for_david()
def fig_rhoHI(lw=1.5, csz=15., lsz=14.):
""" Generate a DLA in optical depth and flux space
Parameters
----------
"""
sdss = DLASurvey.load_SDSS_DR5()
zbins = [2.2, 2.4, 2.75, 3., 3.5, 4.5]
rho_HI, rho_HI_low, rho_HI_hi = sdss.binned_rhoHI(zbins)
outfile = 'fig_rhoHI.png'
# Figure
plt.figure(figsize=(5, 5))
plt.clf()
gs = gridspec.GridSpec(1, 1)
# Tau plot
ax = plt.subplot(gs[0])
# Plot
for kk in range(len(zbins)-1):
zcen = np.sum(zbins[kk:kk+2])/2.
yerr= np.array([rho_HI_low[kk].value/1e8, rho_HI_hi[kk].value/1e8])
ax.errorbar([zcen], [rho_HI[kk].value/1e8], xerr=zcen-zbins[kk], fmt='o', color='blue', capthick=2)
ax.errorbar([zcen], [rho_HI[kk].value/1e8], yerr=[yerr], color='blue', capthick=2)
# z=0
xmnx = [2., 4.5]
ax.fill_between(xmnx, 0.45, 0.6, color='green', alpha=0.5)
# Axes
ax.set_xlim(xmnx)
#ax.set_ylim(1e-2, 5e7)
ax.set_ylabel(r'$\rho_{\rm HI} \; (10^8 \, \rm M_\odot \, Mpc^{-3} \, h_{72})$')
ax.set_xlabel(r'$z$')
ax.text(0.1, 0.9, 'SDSS-DR5 (PW09)', color='blue', size=lsz, transform=ax.transAxes, ha='left')
ax.text(0.9, 0.1, 'z~0 [21cm] \n (Zwaan+05)', color='green', size=lsz, transform=ax.transAxes, ha='right')
#ax.xaxis.set_major_locator(plt.MultipleLocator(10.))
#
set_spines(ax, 2.)
set_fontsize(ax,csz)
# Write
plt.tight_layout(pad=0.2,h_pad=0.,w_pad=0.1)
plt.savefig(outfile, dpi=750)
plt.close()
print("Wrote {:s}".format(outfile))
def grab_meta():
""" Generates the meta data needed for the IGMSpec build
Returns
-------
meta : Table
spec_files : list
List of spec_file names
"""
# Load DLA
from pyigm.surveys.dlasurvey import DLASurvey
hdla100 = DLASurvey.neeleman13_tree()
# Cut down to unique QSOs
spec_files = []
names = []
ra = []
dec = []
coords = hdla100.coord
cnt = 0
for coord in coords:
# Load
names.append('J{:s}{:s}'.format(
coord.ra.to_string(unit=u.hour, sep='', pad=True, precision=2),
coord.dec.to_string(sep='', pad=True, precision=1)))
# RA/DEC
ra.append(coord.ra.value)
dec.append(coord.dec.value)
# SPEC_FILE
fname = hdla100._abs_sys[cnt]._datdict['hi res file'].split('/')[-1]
spec_files.append(fname)
cnt += 1
uni, uni_idx = np.unique(names, return_index=True)
nqso = len(uni_idx)
#
meta = Table()
meta['RA_GROUP'] = np.array(ra)[uni_idx]
meta['DEC_GROUP'] = np.array(dec)[uni_idx]
meta['zem_GROUP'] = hdla100.zem[uni_idx]
meta['sig_zem'] = [0.] * nqso
meta['flag_zem'] = [str('UNKN')] * nqso
meta['STYPE'] = [str('QSO')] * nqso
meta['SPEC_FILE'] = np.array(spec_files)[uni_idx]
# Check
assert chk_meta(meta, chk_cat_only=True)
return meta
def grab_meta():
""" Generates the meta data needed for the IGMSpec build
Returns
-------
meta : Table
spec_files : list
List of spec_file names
"""
# Load DLA
from pyigm.surveys.dlasurvey import DLASurvey
hdla100 = DLASurvey.neeleman13_tree()
# Cut down to unique QSOs
spec_files = []
names = []
ra = []
dec = []
coords = hdla100.coord
cnt = 0
for coord in coords:
# Load
names.append('J{:s}{:s}'.format(coord.ra.to_string(unit=u.hour, sep='', pad=True, precision=2),
coord.dec.to_string(sep='', pad=True, precision=1)))
# RA/DEC
ra.append(coord.ra.value)
dec.append(coord.dec.value)
# SPEC_FILE
fname = hdla100._abs_sys[cnt]._datdict['hi res file'].split('/')[-1]
spec_files.append(fname)
cnt += 1
uni, uni_idx = np.unique(names, return_index=True)
nqso = len(uni_idx)
#
meta = Table()
meta['RA_GROUP'] = np.array(ra)[uni_idx]
meta['DEC_GROUP'] = np.array(dec)[uni_idx]
meta['zem_GROUP'] = hdla100.zem[uni_idx]
meta['sig_zem'] = [0.]*nqso
meta['flag_zem'] = [str('UNKN')]*nqso
meta['STYPE'] = [str('QSO')]*nqso
meta['SPEC_FILE'] = np.array(spec_files)[uni_idx]
# Check
assert chk_meta(meta, chk_cat_only=True)
return meta
def dr5_for_david():
""" Generate a Table for David
"""
# imports
from pyigm.abssys.dla import DLASystem
from pyigm.abssys.lls import LLSSystem
sdss_survey = DLASurvey.load_SDSS_DR5()
# Fiber key
for fkey in ['FIBER', 'FIBER_ID', 'FIB']:
if fkey in sdss_survey.sightlines.keys():
break
# Init
#idict = dict(plate=[], fiber=[], classification_confidence=[], # FOR v2
# classification=[], ra=[], dec=[])
# Connect to sightlines
s_coord = SkyCoord(ra=sdss_survey.sightlines['RA'], dec=sdss_survey.sightlines['DEC'], unit='deg')
# Add plate/fiber to statistical DLAs
dla_coord = sdss_survey.coord
idx2, d2d, d3d = match_coordinates_sky(dla_coord, s_coord, nthneighbor=1)
if np.min(d2d.to('arcsec').value) > 1.:
raise ValueError("Bad match to sightlines")
plates, fibers = [], []
for jj,igd in enumerate(np.where(sdss_survey.mask)[0]):
dla = sdss_survey._abs_sys[igd]
try:
dla.plate = sdss_survey.sightlines['PLATE'][idx2[jj]]
except IndexError:
pdb.set_trace()
dla.fiber = sdss_survey.sightlines[fkey][idx2[jj]]
plates.append(sdss_survey.sightlines['PLATE'][idx2[jj]])
fibers.append(sdss_survey.sightlines[fkey][idx2[jj]])
# Write
dtbl = Table()
dtbl['plate'] = plates
dtbl['fiber'] = fibers
dtbl['zabs'] = sdss_survey.zabs
dtbl['NHI'] = sdss_survey.NHI
dtbl.write('results/dr5_for_david.ascii', format='ascii')
# Write sightline info
stbl = sdss_survey.sightlines[['PLATE', 'FIB', 'Z_START', 'Z_END', 'RA', 'DEC']]
gdsl = stbl['Z_END'] > stbl['Z_START']
stbl[gdsl].write('results/dr5_sightlines_for_david.ascii', format='ascii')
def neeleman13():
""" Build a summary file for the Neeleman+13 sample
"""
prefix = 'H100'
outpath = os.getenv('DROPBOX_DIR')+'/Public/DLA/'+prefix+'/'
dlasurvey = DLASurvey.from_flist('Lists/Neeleman13.lst',
tree=os.environ.get('DLA'))
dlasurvey.ref = 'Neeleman+13'
# Json file for ions
dlasurvey.fill_ions(use_Nfile=True)
mk_json_ions(dlasurvey, prefix, outpath+prefix+'_DLA_ions.json')
# Json files for .clm files
mk_json_clms(dlasurvey, outpath+'CLMS/', prefix)
print('It is likely you wish to tarball the CLMS folder for distribution')
# Summary file and spectra
mk_summary(dlasurvey, prefix, outpath+prefix+'_DLA.fits',
specpath=outpath+'/Spectra/',
htmlfil=outpath+prefix+'_DLA.html')
def test_init():
dlas = DLASurvey(ref='null')
assert dlas.abs_type == 'DLA'
coord = SkyCoord(ra=123.1143, dec=-12.4321, unit='deg')
dlasys = DLASystem(coord, 1.244, [-300, 300.] * u.km / u.s, 20.4)
dlasys.name = 'Sys1'
#
coord2 = SkyCoord(ra=223.1143, dec=42.4321, unit='deg')
dlasys2 = DLASystem(coord2, 1.744, [-300, 300.] * u.km / u.s, 21.7)
dlasys2.name = 'Sys2'
# Add systems
dlas.add_abs_sys(dlasys)
dlas.add_abs_sys(dlasys2)
assert dlas.nsys == 2
def test_dla_fitted():
dlas = DLASurvey(ref='null')
# f(N) double power law
fN = dlas.fitted_fN(21.)
assert isinstance(fN, float)
assert np.isclose(fN, 12.661299335610309)
fN = dlas.fitted_fN(np.arange(20.3, 21.3, 0.1))
assert isinstance(fN, np.ndarray)
# l(z)
lz = dlas.fitted_lz(1.)
assert isinstance(lz, float)
assert np.isclose(lz, 0.054821907396422453)
# Error
lz, sig_lz = dlas.fitted_lz(1., boot_error=True)
assert sig_lz.shape == (1, 2)
# nenH
nenH = dlas.fitted_nenH(21.)
assert isinstance(nenH, float)
assert np.isclose(nenH, -3.12739999999999999)
def neeleman13():
""" Build a summary file for the Neeleman+13 sample
"""
prefix = 'H100'
outpath = os.getenv('DROPBOX_DIR') + '/Public/DLA/' + prefix + '/'
dlasurvey = DLASurvey.from_flist('Lists/Neeleman13.lst',
tree=os.environ.get('DLA'))
dlasurvey.ref = 'Neeleman+13'
# Json file for ions
dlasurvey.fill_ions(use_Nfile=True)
mk_json_ions(dlasurvey, prefix, outpath + prefix + '_DLA_ions.json')
# Json files for .clm files
mk_json_clms(dlasurvey, outpath + 'CLMS/', prefix)
print('It is likely you wish to tarball the CLMS folder for distribution')
# Summary file and spectra
mk_summary(dlasurvey,
prefix,
outpath + prefix + '_DLA.fits',
specpath=outpath + '/Spectra/',
htmlfil=outpath + prefix + '_DLA.html')
def main(args=None):
pargs = parser(options=args)
# Setup
import sys
pfind = __file__.rfind('/scripts')
spth = __file__[:pfind] + '/src'
sys.path.append(spth)
import training_set as tset
from pyigm.surveys.dlasurvey import DLASurvey
outroot = pargs.outpath + '/training_{:d}_{:d}'.format(
pargs.seed, pargs.ntrain)
# Sightlines
sdss = DLASurvey.load_SDSS_DR5(sample='all')
slines, sdict = tset.grab_sightlines(sdss, flg_bal=0)
# Run
_, _ = tset.make_set(pargs.ntrain,
slines,
outroot=outroot,
seed=pargs.seed,
slls=pargs.slls)
def grab_sightlines(dlasurvey=None,
flg_bal=None,
zmin=2.3,
s2n=5.,
DX=0.,
igmsp_survey='SDSS_DR7',
update_zem=True):
""" Grab a set of sightlines without DLAs from a DLA survey
Insist that all have spectra occur in igmspec
Update sightline zem with igmspec zem
Parameters
----------
dlas : DLASurvey
Usually SDSS or BOSS
flg_bal : int, optional
Maximum BAL flag (0=No signature, 1=Weak BAL, 2=BAL)
s2n : float, optional
Minimum S/N as defined in some manner
DX : float, optional
Restrict on DX
zmin : float, optional
Minimum redshift for zem
update_zem : bool, optional
Update zem in sightlines?
Returns
-------
final : Table
astropy Table of good sightlines
sdict : dict
dict describing the sightlines
"""
#1) REMOVE 910, 526 z=2.88; NHI=21.19
import warnings
warnings.warn("Someday remove 910, 526 which has a *strong* DLA")
igmsp = IgmSpec()
# Init
if dlasurvey is None:
print("Using the DR5 sample for the sightlines")
dlasurvey = DLASurvey.load_SDSS_DR5(sample='all')
igmsp_survey = 'SDSS_DR7'
nsight = len(dlasurvey.sightlines)
keep = np.array([True] * nsight)
meta = igmsp[igmsp_survey].meta
# Avoid DLAs
dla_coord = dlasurvey.coord
sl_coord = SkyCoord(ra=dlasurvey.sightlines['RA'],
dec=dlasurvey.sightlines['DEC'])
idx, d2d, d3d = match_coordinates_sky(sl_coord, dla_coord, nthneighbor=1)
clear = d2d > 1 * u.arcsec
keep = keep & clear
# BAL
if flg_bal is not None:
gd_bal = dlasurvey.sightlines['FLG_BAL'] <= flg_bal
keep = keep & gd_bal
# S/N
if s2n > 0.:
gd_s2n = dlasurvey.sightlines['S2N'] > s2n
keep = keep & gd_s2n
# Cut on DX
if DX > 0.:
gd_DX = dlasurvey.sightlines['DX'] > DX
keep = keep & gd_DX
# igmsp
qso_coord = SkyCoord(ra=meta['RA_GROUP'],
dec=meta['DEC_GROUP'],
unit='deg')
idxq, d2dq, d3dq = match_coordinates_sky(sl_coord,
qso_coord,
nthneighbor=1)
in_igmsp = d2dq < 1 * u.arcsec
keep = keep & in_igmsp
# Check zem and dz
#igm_id = meta['IGM_ID'][idxq]
#cat_rows = match_ids(igm_id, igmsp.cat['IGM_ID'])
#zem = igmsp.cat['zem'][cat_rows]
zem = meta['zem_GROUP'][idxq]
dz = np.abs(zem - dlasurvey.sightlines['ZEM'])
gd_dz = dz < 0.1
keep = keep & gd_dz #& gd_zlim
if zmin is not None:
gd_zmin = zem > zmin
keep = keep & gd_zmin #& gd_zlim
#gd_zlim = (zem-dlasurvey.sightlines['Z_START']) > 0.1
#pdb.set_trace()
# Assess
final = dlasurvey.sightlines[keep]
#final_coords = SkyCoord(ra=final['RA'], dec=final['DEC'], unit='deg')
#matches, meta = igmsp.meta_from_coords(final_coords, groups=['SDSS_DR7'], tol=1*u.arcsec)
#idxq2, d2dq2, d3dq2 = match_coordinates_sky(final_coords, qso_coord, nthneighbor=1)
#in_igmsp2 = d2dq2 < 1*u.arcsec
#pdb.set_trace()
sdict = {}
sdict['n'] = len(final)
print("We have {:d} sightlines for analysis".format(sdict['n']))
def qck_stats(idict, tbl, istr, key):
idict[istr + 'min'] = np.min(tbl[key])
idict[istr + 'max'] = np.max(tbl[key])
idict[istr + 'median'] = np.median(tbl[key])
qck_stats(sdict, final, 'z', 'ZEM')
qck_stats(sdict, final, 'i', 'MAG')
print("Min z = {:g}, Median z = {:g}, Max z = {:g}".format(
sdict['zmin'], sdict['zmedian'], sdict['zmax']))
# Return
return final, sdict
def test_read_h100_nosys():
h100 = DLASurvey.load_H100(load_sys=False)
assert h100.nsys == 100
def main(flg_tst, sdss=None, ml_survey=None):
import os
# Sightlines
flg_tst = int(flg_tst)
if (flg_tst % 2**1) >= 2**0:
if sdss is None:
sdss = DLASurvey.load_SDSS_DR5(sample='all')
slines, sdict = grab_sightlines(sdss, flg_bal=0)
# Test case of 100 sightlines
if (flg_tst % 2**2) >= 2**1:
# Make training set
_, _ = make_set(100, slines, outroot='results/training_100')
# Production runs
if (flg_tst % 2**3) >= 2**2:
#training_prod(123456, 5, 10, outpath=os.getenv('DROPBOX_DIR')+'/MachineLearning/DLAs/') # TEST
#training_prod(123456, 10, 500, outpath=os.getenv('DROPBOX_DIR')+'/MachineLearning/DLAs/') # TEST
training_prod(12345,
10,
5000,
outpath=os.getenv('DROPBOX_DIR') +
'/MachineLearning/DLAs/')
# Production runs -- 100k more
if (flg_tst % 2**4) >= 2**3:
# python src/training_set.py
training_prod(22345,
10,
10000,
outpath=os.getenv('DROPBOX_DIR') +
'/MachineLearning/DLAs/')
# Production runs -- 100k more
if flg_tst & (2**4):
# python src/training_set.py
if False:
if sdss is None:
sdss = DLASurvey.load_SDSS_DR5(sample='all')
slines, sdict = grab_sightlines(sdss, flg_bal=0)
_, _ = make_set(100,
slines,
outroot='results/slls_training_100',
slls=True)
#training_prod(22343, 10, 100, slls=True, outpath=os.getenv('DROPBOX_DIR')+'/MachineLearning/SLLSs/')
training_prod(22343,
10,
5000,
slls=True,
outpath=os.getenv('DROPBOX_DIR') +
'/MachineLearning/SLLSs/')
# Mixed systems for testing
if flg_tst & (2**5):
# python src/training_set.py
if sdss is None:
sdss = DLASurvey.load_SDSS_DR5(sample='all')
slines, sdict = grab_sightlines(sdss, flg_bal=0)
ntrials = 10000
seed = 23559
_, _ = make_set(
ntrials,
slines,
seed=seed,
mix=True,
outroot=os.getenv('DROPBOX_DIR') +
'/MachineLearning/Mix/mix_test_{:d}_{:d}'.format(seed, ntrials))
# DR5 DLA-free sightlines
if flg_tst & (2**6):
write_sdss_sightlines()
# High NHI systems for testing
if flg_tst & (2**7):
# python src/training_set.py
if sdss is None:
sdss = DLASurvey.load_SDSS_DR5(sample='all')
slines, sdict = grab_sightlines(sdss, flg_bal=0)
ntrials = 20000
seed = 83559
_, _ = make_set(ntrials,
slines,
seed=seed,
high=True,
outroot=os.getenv('DROPBOX_DIR') +
'/MachineLearning/HighNHI/high_train_{:d}_{:d}'.format(
seed, ntrials))
# Low S/N
if flg_tst & (2**8):
# python src/training_set.py
if sdss is None:
sdss = DLASurvey.load_SDSS_DR5(sample='all')
slines, sdict = grab_sightlines(sdss, flg_bal=0)
ntrials = 10000
seed = 83557
_, _ = make_set(
ntrials,
slines,
seed=seed,
low_s2n=True,
outroot=os.getenv('DROPBOX_DIR') +
'/MachineLearning/LowS2N/lows2n_train_{:d}_{:d}'.format(
seed, ntrials))
def test_read_HST():
""" Neeleman+16
"""
hst16 = DLASurvey.load_HST16()
assert hst16.nsys == 4
def dr5_false_positives(ml_dlasurvey=None, ml_llssurvey=None):
vette_file = 'vette_dr5.json'
from pyigm.surveys.dlasurvey import DLASurvey
from matplotlib import pyplot as plt
# Load ML
if (ml_dlasurvey is None):
_, ml_dlasurvey = load_ml_dr7()
# Load DR5
dr5 = DLASurvey.load_SDSS_DR5() # This is the statistical sample
# Vette
vette = ltu.loadjson(vette_file)
dr5_ml_idx = np.array(vette['dr5_idx'])
# Use coord to efficiently deal with sightlines
ml_dla_coord = ml_dlasurvey.coords
dr5_coord = SkyCoord(ra=dr5.sightlines['RA'], dec=dr5.sightlines['DEC'], unit='deg')
idx, d2d, d3d = match_coordinates_sky(ml_dla_coord, dr5_coord, nthneighbor=1)
in_dr5 = d2d < 2*u.arcsec
print("{:d} of the ML DLA were in the DR5 sightlines".format(np.sum(in_dr5)))
# False positives
fpos = np.array([True]*ml_dlasurvey.nsys)
fpos[~in_dr5] = False
# False positives
imatched = np.where(dr5_ml_idx >= 0)[0]
match_val = dr5_ml_idx[imatched]
fpos[match_val] = False
print("There are {:d} total false positives".format(np.sum(fpos)))
# This nearly matches David's. Will run with his analysis.
fpos_in_stat = fpos.copy()
# Restrict on DR5
plates = ml_dlasurvey.plate
fibers = ml_dlasurvey.fiber
zabs = ml_dlasurvey.zabs
zem = ml_dlasurvey.zem
for idx in np.where(fpos_in_stat)[0]:
# Finally, match to DR5
dr5_sl = np.where((dr5.sightlines['PLATE'] == plates[idx]) &
(dr5.sightlines['FIB'] == fibers[idx]))[0][0]
if (zabs[idx] >= dr5.sightlines['Z_START'][dr5_sl]) & \
(zabs[idx] <= dr5.sightlines['Z_END'][dr5_sl]):
pass
else:
fpos_in_stat[idx] = False
print("Number of FP in DR5 analysis region = {:d}".format(np.sum(fpos_in_stat)))
print("Number with NHI<20.45 = {:d}".format(np.sum(ml_dlasurvey.NHI[fpos_in_stat]< 20.45)))
# High NHI
highNHI = ml_dlasurvey.NHI[fpos_in_stat] > 21.
htbl = Table()
htbl['PLATE'] = plates[fpos_in_stat][highNHI]
htbl['FIBER'] = fibers[fpos_in_stat][highNHI]
htbl['zabs'] = zabs[fpos_in_stat][highNHI]
htbl['NHI'] = ml_dlasurvey.NHI[fpos_in_stat][highNHI]
htbl.write("FP_DR5_highNHI.ascii", format='ascii.fixed_width', overwrite=True)
# Medium NHI
medNHI = (ml_dlasurvey.NHI[fpos_in_stat] > 20.6) & (ml_dlasurvey.NHI[fpos_in_stat] < 21)
mtbl = Table()
mtbl['PLATE'] = plates[fpos_in_stat][medNHI]
mtbl['FIBER'] = fibers[fpos_in_stat][medNHI]
mtbl['zabs'] = zabs[fpos_in_stat][medNHI]
mtbl['zem'] = zem[fpos_in_stat][medNHI]
mtbl['NHI'] = ml_dlasurvey.NHI[fpos_in_stat][medNHI]
mtbl.write("FP_DR5_medNHI.ascii", format='ascii.fixed_width', overwrite=True)
def chk_dr5_dla_to_ml(ml_dlasurvey=None, ml_llssurvey=None, dz_toler=0.015,
outfile='vette_dr5.json', write_again=True):
# Load ML
if (ml_dlasurvey is None) or (ml_llssurvey is None):
ml_llssurvey, ml_dlasurvey = load_ml_dr7()
# Load DR5
dr5 = DLASurvey.load_SDSS_DR5() # This is the statistical sample
# Use coord to efficiently deal with sightlines
ml_coord = SkyCoord(ra=ml_dlasurvey.sightlines['RA'], dec=ml_dlasurvey.sightlines['DEC'], unit='deg')
dr5_coord = SkyCoord(ra=dr5.sightlines['RA'], dec=dr5.sightlines['DEC'], unit='deg')
idx, d2d, d3d = match_coordinates_sky(dr5_coord, ml_coord, nthneighbor=1)
in_ml = d2d < 2*u.arcsec
print("{:d} of the DR5 sightlines were covered by ML out of {:d}".format(np.sum(in_ml), len(dr5.sightlines)))
# 7477 sightlines out of 7482
# Cut down
dr5.sightlines = dr5.sightlines[in_ml]
new_mask = dla_stat(dr5, dr5.sightlines) # 737 good DLAs
dr5.mask = new_mask
dr5_dla_coord = dr5.coord
dr5_dla_zabs = dr5.zabs
ndr5 = len(dr5_dla_coord)
ml_dla_coord = ml_dlasurvey.coords
ml_lls_coord = ml_llssurvey.coords
# Loop on DR5 DLAs and save indices of the matches
dr5_ml_idx = np.zeros(ndr5).astype(int) - 1
for ii in range(ndr5):
# Match to ML
dla_mts = np.where(dr5_dla_coord[ii].separation(ml_dla_coord) < 2*u.arcsec)[0]
nmt = len(dla_mts)
if nmt == 0: # No match
# Check for LLS
lls_mts = np.where(dr5_dla_coord[ii].separation(ml_lls_coord) < 2*u.arcsec)[0]
nmt2 = len(lls_mts)
if nmt2 == 0: # No match
pass
else:
zML = ml_llssurvey.zabs[lls_mts] # Redshifts of all DLAs on the sightline in ML
zdiff = np.abs(dr5_dla_zabs[ii]-zML)
if np.min(zdiff) < dz_toler:
dr5_ml_idx[ii] = -9 # SLLS match
else:
zML = ml_dlasurvey.zabs[dla_mts] # Redshifts of all DLAs on the sightline in ML
zdiff = np.abs(dr5_dla_zabs[ii]-zML)
if np.min(zdiff) < dz_toler:
#print("Match on {:d}!".format(ii))
# Match
imin = np.argmin(zdiff)
dr5_ml_idx[ii] = dla_mts[imin]
else: # Check for LLS
lls_mts = np.where(dr5_dla_coord[ii].separation(ml_lls_coord) < 2*u.arcsec)[0]
nmt2 = len(lls_mts)
if nmt2 == 0: # No match
pass
else:
zML = ml_llssurvey.zabs[lls_mts] # Redshifts of all DLAs on the sightline in ML
zdiff = np.abs(dr5_dla_zabs[ii]-zML)
if np.min(zdiff) < dz_toler:
dr5_ml_idx[ii] = -9 # SLLS match
dr5_coord = SkyCoord(ra=dr5.sightlines['RA'], dec=dr5.sightlines['DEC'], unit='deg')
# Write out misses
misses = np.where(dr5_ml_idx == -1)[0]
plates, fibers = [], []
for miss in misses:
imin = np.argmin(dr5_dla_coord[miss].separation(dr5_coord))
plates.append(dr5.sightlines['PLATE'][imin])
fibers.append(dr5.sightlines['FIB'][imin])
mtbl = Table()
mtbl['PLATE'] = plates
mtbl['FIBER'] = fibers
mtbl['NHI'] = dr5.NHI[misses]
mtbl['zabs'] = dr5.zabs[misses]
if write_again:
mtbl.write('DR5_misses.ascii', format='ascii.fixed_width', overwrite=True)
# Write out SLLS
sllss = np.where(dr5_ml_idx == -9)[0]
plates, fibers = [], []
for slls in sllss:
imin = np.argmin(dr5_dla_coord[slls].separation(dr5_coord))
plates.append(dr5.sightlines['PLATE'][imin])
fibers.append(dr5.sightlines['FIB'][imin])
mtbl = Table()
mtbl['PLATE'] = plates
mtbl['FIBER'] = fibers
mtbl['NHI'] = dr5.NHI[sllss]
mtbl['zabs'] = dr5.zabs[sllss]
if write_again:
mtbl.write('DR5_SLLS.ascii', format='ascii.fixed_width', overwrite=True)
pdb.set_trace()
# ML not matched by PW09?
ml_dla_coords = ml_dlasurvey.coords
idx2, d2d2, d3d = match_coordinates_sky(ml_dla_coords, dr5_dla_coord, nthneighbor=1)
not_in_dr5 = d2d2 > 2*u.arcsec # This doesn't match redshifts!
might_be_in_dr5 = np.where(~not_in_dr5)[0]
others_not_in = [] # this is some painful book-keeping
for idx in might_be_in_dr5: # Matching redshifts..
imt = ml_dla_coord[idx].separation(dr5_dla_coord) < 2*u.arcsec
# Match on dztoler
if np.min(np.abs(ml_dlasurvey.zabs[idx]-dr5.zabs[imt])) > dz_toler:
others_not_in.append(idx)
# Save
out_dict = {}
out_dict['in_ml'] = in_ml
out_dict['dr5_idx'] = dr5_ml_idx # -1 are misses, -9 are SLLS
out_dict['not_in_dr5'] = np.concatenate([np.where(not_in_dr5)[0], np.array(others_not_in)])
ltu.savejson(outfile, ltu.jsonify(out_dict), overwrite=True)
def test_read_xq100():
""" XQ-100 """
xq100 = DLASurvey.load_XQ100(sample='stat')
assert xq100.nsys == 36
def test_init():
dlas = DLASurvey(ref='null')
assert dlas.abs_type == 'DLA'
def load_ml_file(pred_file):
""" Load the search results from the CNN into a DLASurvey object
Parameters
----------
pred_file
Returns
-------
ml_llssurvey: LLSSurvey
ml_dlasusrvey: DLASurvey
"""
print("Loading {:s}. Please be patient..".format(pred_file))
# Read
ml_results = ltu.loadjson(pred_file)
use_platef = False
if 'plate' in ml_results[0].keys():
use_platef = True
else:
if 'id' in ml_results[0].keys():
use_id = True
# Init
idict = dict(ra=[], dec=[], plate=[], fiber=[])
if use_platef:
for key in ['plate', 'fiber', 'mjd']:
idict[key] = []
dlasystems = []
llssystems = []
# Generate coords to speed things up
for obj in ml_results:
for key in ['ra', 'dec']:
idict[key].append(obj[key])
ml_coords = SkyCoord(ra=idict['ra'], dec=idict['dec'], unit='deg')
ra_names = ml_coords.icrs.ra.to_string(unit=u.hour, sep='', pad=True)
dec_names = ml_coords.icrs.dec.to_string(sep='', pad=True, alwayssign=True)
vlim = [-500., 500.] * u.km / u.s
dcoord = SkyCoord(ra=0., dec=0., unit='deg')
# Loop on list
didx, lidx = [], []
print("Looping on sightlines..")
for tt, obj in enumerate(ml_results):
#if (tt % 100) == 0:
# print('tt: {:d}'.format(tt))
# Sightline
if use_id:
plate, fiber = [int(spl) for spl in obj['id'].split('-')]
idict['plate'].append(plate)
idict['fiber'].append(fiber)
# Systems
for ss, syskey in enumerate(['dlas', 'subdlas']):
for idla in obj[syskey]:
name = 'J{:s}{:s}_z{:.3f}'.format(ra_names[tt], dec_names[tt],
idla['z_dla'])
if ss == 0:
isys = DLASystem(dcoord,
idla['z_dla'],
vlim,
NHI=idla['column_density'],
zem=obj['z_qso'],
name=name)
else:
isys = LLSSystem(dcoord,
idla['z_dla'],
vlim,
NHI=idla['column_density'],
zem=obj['z_qso'],
name=name)
isys.confidence = idla['dla_confidence']
isys.s2n = idla['s2n']
if use_platef:
isys.plate = obj['plate']
isys.fiber = obj['fiber']
elif use_id:
isys.plate = plate
isys.fiber = fiber
# Save
if ss == 0:
didx.append(tt)
dlasystems.append(isys)
else:
lidx.append(tt)
llssystems.append(isys)
# Generate sightline tables
sightlines = Table()
sightlines['RA'] = idict['ra']
sightlines['DEC'] = idict['dec']
sightlines['PLATE'] = idict['plate']
sightlines['FIBERID'] = idict['fiber']
# Surveys
ml_llssurvey = LLSSurvey()
ml_llssurvey.sightlines = sightlines.copy()
ml_llssurvey._abs_sys = llssystems
ml_llssurvey.coords = ml_coords[np.array(lidx)]
ml_dlasurvey = DLASurvey()
ml_dlasurvey.sightlines = sightlines.copy()
ml_dlasurvey._abs_sys = dlasystems
ml_dlasurvey.coords = ml_coords[np.array(didx)]
# Return
return ml_llssurvey, ml_dlasurvey
def examine_false_pos(test_file='data/test_dlas_96629_10000.json.gz',
pred_file='data/test_dlas_96629_predictions.json.gz',
vette_file='vette_10k.json'):
""" Examine false positives in the Test set (held out)
"""
from pyigm.surveys.dlasurvey import DLASurvey
import h5py
import json
from matplotlib import pyplot as plt
# Load Test
test_dlas = test_to_tbl(test_file)
ntest = len(test_dlas)
# Load hdf5
CNN_result_path = '/home/xavier/Projects/ML_DLA_results/CNN/'
hdf5_datafile = CNN_result_path + 'gensample_hdf5_files/test_dlas_96629_10000.hdf5'
hdf = h5py.File(hdf5_datafile, 'r')
headers = json.loads(hdf['meta'].value)['headers']
# Load ML
ml_abs = pred_to_tbl(pred_file)
# Vette
vette = ltu.loadjson(vette_file)
test_ml_idx = np.array(vette['test_idx'])
# Load DR5
dr5 = DLASurvey.load_SDSS_DR5()
all_dr5 = DLASurvey.load_SDSS_DR5(sample='all_sys')
# False positives
fpos = ml_abs['NHI'] >= 20.3 # Must be a DLA
imatched = np.where(test_ml_idx >= 0)[0]
match_val = test_ml_idx[imatched]
fpos[match_val] = False
print("There are {:d} total false positives".format(np.sum(fpos)))
# This nearly matches David's. Will run with his analysis.
fpos_in_dr5 = fpos.copy()
# Restrict on DR5
for idx in np.where(fpos_in_dr5)[0]:
# Convoluted indexing..
mlid = ml_abs['ids'][idx]
# Plate/Fiber
plate = headers[mlid]['PLATE']
fib = headers[mlid]['FIBER']
# Finally, match to DR5
dr5_sl = np.where((dr5.sightlines['PLATE'] == plate)
& (dr5.sightlines['FIB'] == fib))[0][0]
if (ml_abs['zabs'][idx] >= dr5.sightlines['Z_START'][dr5_sl]) & \
(ml_abs['zabs'][idx] <= dr5.sightlines['Z_END'][dr5_sl]):
pass
else:
fpos_in_dr5[idx] = False
print("Number of FP in DR5 analysis region = {:d}".format(
np.sum(fpos_in_dr5)))
# How many match to DR5 SLLS?
slls = all_dr5.NHI < 20.3
slls_coord = all_dr5.coord[slls]
slls_zabs = all_dr5.zabs[slls]
nslls = 0
for idx in np.where(fpos_in_dr5)[0]:
# Convoluted indexing..
mlid = ml_abs['ids'][idx]
# RA/DEC
ra = headers[mlid]['RA_GROUP']
dec = headers[mlid]['DEC_GROUP']
coord = SkyCoord(ra=ra, dec=dec, unit='deg')
# Match coord
mt = coord.separation(slls_coord) < 3 * u.arcsec
if np.any(mt):
# Match redshift
if np.min(np.abs(slls_zabs[mt] - ml_abs['zabs'][idx])) < 0.015:
nslls += 1
print("Number of FP that are SLLS in DR5 = {:d}".format(nslls))
low_NHI = ml_abs['NHI'][fpos_in_dr5] < 20.5
print("Number of FP that are NHI <= 20.5 = {:d}".format(np.sum(low_NHI)))
# Write out
fp_tbl = Table()
for key in ['ids', 'NHI', 'zabs', 'conf']:
fp_tbl[key] = ml_abs[key][fpos_in_dr5]
fp_tbl.write('test10k_false_pos.ascii',
format='ascii.fixed_width',
overwrite=True)
# Histogram
dr5_idx = np.where(fpos_in_dr5)
plt.clf()
ax = plt.gca()
ax.hist(ml_abs['conf'][dr5_idx])
plt.show()
def test_read_h100_nosys():
h100 = DLASurvey.load_H100(load_sys=False)
assert h100.nsys == 100