def spec2vel(spec, basename, transition, z, f_col, sb_id):
"""
"""
specs = glob.glob(spec)
# Sort the SBs
crrls.natural_sort(specs)
for s in specs:
# Determine the subband name
try:
sb = re.findall('{0}\d+'.format(sb_id), s)[0]
except IndexError:
print "Could not find SB number."
print "Will use SB???"
sb = 'SB???'
# Load the data
data = np.loadtxt(s)
x = data[:,f_col]
y = data[:,f_col+1:]
qns, freqs = crrls.find_lines_sb(x, transition, z)
for i,n in enumerate(qns):
# Convert the frequency axis to velocity
vel = crrls.freq2vel(freqs[i]*(1+z), x)/1e3
# Save the spectrum with a velocity column
np.savetxt('{0}_{1}_n{2}.ascii'.format(basename, sb, int(n)), np.c_[x, vel, y])
def main(spec, out, freqf):
"""
"""
specs = glob.glob(spec)
# Sort the SBs
crrls.natural_sort(specs)
for s in specs:
## Determine the subband name
#try:
#sb = re.findall('SB\d+', s)[0]
#except IndexError:
#print "Could not find SB number."
#print "Will use SB???"
#sb = 'SB???'
data = np.loadtxt(s, comments='#')
freq = data[:,0]*freqf
tb = data[:,1]
data[:,0] = data[:,0]/1e6
# write the processed spectrum
#np.savetxt('{0}_{1}.ascii'.format(basename, sb), data)
tbtable = Table([freq, tb],
names=['FREQ MHz',
'Tb Jy/BEAM'])
ascii.write(tbtable, out, format='commented_header')
def find_good_lines(spec, basename, transition, transs, z, vel_shift, x_col):
"""
"""
specs = glob.glob(spec)
crrls.natural_sort(specs)
with open('{0}_good_lines.log'.format(transition), 'w') as log:
for s in specs:
# Determine the subband name
sb = re.findall('SB\d+', s)[0]
data = np.loadtxt(s)
x = data[:,x_col]
qns, freqs = crrls.find_lines_sb(x, transition, z)
of = []
for t in transs:
n, f = crrls.find_lines_sb(x, t, z)
of.append(list(f))
of = np.array(list(_f for of_ in of for _f in of_))
for i,freq in enumerate(freqs):
vel = crrls.freq2vel(freq*1e6, freq*1e6)/1e3
ovel = crrls.freq2vel(freq*1e6, of*1e6)/1e3
diff = [abs(v - vel) for v in ovel]
if all(d > vel_shift for d in diff):
log.write('{0}_{1}_n{2:.0f}.ascii\n'.format(basename, sb, qns[i]))
def find_good_lines(spec, basename, transition, transs, z, vel_shift, x_col):
"""
"""
specs = glob.glob(spec)
crrls.natural_sort(specs)
with open('{0}_good_lines.log'.format(transition), 'w') as log:
for s in specs:
# Determine the subband name
sb = re.findall('SB\d+', s)[0]
data = np.loadtxt(s)
x = data[:, x_col]
qns, freqs = crrls.find_lines_sb(x, transition, z)
of = []
for t in transs:
n, f = crrls.find_lines_sb(x, t, z)
of.append(list(f))
of = np.array(list(_f for of_ in of for _f in of_))
for i, freq in enumerate(freqs):
vel = crrls.freq2vel(freq * 1e6, freq * 1e6) / 1e3
ovel = crrls.freq2vel(freq * 1e6, of * 1e6) / 1e3
diff = [abs(v - vel) for v in ovel]
if all(d > vel_shift for d in diff):
log.write('{0}_{1}_n{2:.0f}.ascii\n'.format(
basename, sb, qns[i]))
def load_itau_all_norad(trans='alpha', n_max=1000):
"""
Loads all the available models.
"""
#LOCALDIR = os.path.dirname(os.path.realpath(__file__))
models = glob.glob('{0}/bbn/*_dat_bn_beta'.format(LOCALDIR))
natural_sort(models)
Te = np.zeros(len(models))
ne = np.zeros(len(models))
other = np.zeros(len(models), dtype='|S20')
data = np.zeros((len(models), 2, n_max))
for i, model in enumerate(models):
st = model[model.index('T') + 2:model.index('T') + 5]
Te[i] = str2val(st)
sn = model[model.index('ne') + 3:model.index('ne') + 7].split('_')[0]
ne[i] = str2val(sn)
other[i] = model.split('bn_beta')[-1]
n, int_tau = itau(st, sn, trans, n_max=1000, other=other[i])
data[i, 0] = n
data[i, 1] = int_tau
return [Te, ne, other, data]
def spec2vel(spec, basename, transition, z, f_col, sb_id):
"""
"""
specs = glob.glob(spec)
# Sort the SBs
crrls.natural_sort(specs)
for s in specs:
# Determine the subband name
try:
sb = re.findall('{0}\d+'.format(sb_id), s)[0]
except IndexError:
print "Could not find SB number."
print "Will use SB???"
sb = 'SB???'
# Load the data
data = np.loadtxt(s)
x = data[:, f_col]
y = data[:, f_col + 1:]
qns, freqs = crrls.find_lines_sb(x, transition, z)
for i, n in enumerate(qns):
# Convert the frequency axis to velocity
vel = crrls.freq2vel(freqs[i] * (1 + z), x) / 1e3
# Save the spectrum with a velocity column
np.savetxt('{0}_{1}_n{2}.ascii'.format(basename, sb, int(n)),
np.c_[x, vel, y])
def load_itau_all_hydrogen(trans='alpha',
n_max=1000,
verbose=False,
value='itau'):
"""
Loads all the available models for Hydrogen.
"""
#LOCALDIR = os.path.dirname(os.path.realpath(__file__))
models = glob.glob('{0}/bbn2_RRL_HI{1}/*'.format(LOCALDIR, trans))
natural_sort(models)
models = np.asarray(models)
models = sorted(models,
key=lambda x:
(str2val(x.split('_')[5]), float(x.split('_')[7]),
str2val(x.split('_')[11])
if len(x.split('_')) > 17 else 0))
Te = np.zeros(len(models))
ne = np.zeros(len(models))
other = np.zeros(len(models), dtype=object)
data = np.zeros((len(models), 2, n_max))
for i, model in enumerate(models):
if verbose:
print(model)
st = model.split('_')[5]
Te[i] = str2val(st)
sn = model.split('_')[7]
ne[i] = float(sn)
if len(model.split('_')) <= 18:
other[i] = '-'
else:
other[i] = '_'.join(model.split('_')[10:13])
if verbose:
print("Trying to load model: ne={0}, te={1}, tr={2}".format(
ne[i], Te[i], other[i]))
n, int_tau = itau_h(st,
sn,
trans,
n_max=n_max,
other=other[i],
verbose=verbose,
value=value)
data[i, 0] = n
data[i, 1] = int_tau
return [Te, ne, other, data]
def make_rms_list(spec, output, transitions, z, dv, mode, f_col, y_col):
"""
"""
specs = glob.glob(spec)
crrls.natural_sort(specs)
with open(output, 'w') as log:
for s in specs:
data = np.loadtxt(s)
x = data[:,f_col]
y = data[:,y_col]
# Catch NaNs and invalid values:
mask_x0 = np.ma.masked_equal(x, 1.0).mask
mask_x1 = np.isnan(x)
mask_y = np.isnan(y)
mask = np.array(reduce(np.logical_or, [mask_x0, mask_x1, mask_y]))
# Remove NaNs and invalid values
mx = x[~mask]
my = y[~mask]
if len(x) > 0:
trans = transitions.split(',')
bf = []
for o,t in enumerate(trans):
n, f = crrls.find_lines_sb(mx, t, z)
bf.append(list(f))
if len(bf) > 0:
bf = np.array(list(_f for _bf in bf for _f in _bf))
x_lf, y_lf = crrls.blank_lines2(mx, my, bf, dv)
else:
x_lf, y_lf = mx,my
else:
pass
rms = crrls.get_rms(y_lf)
mrms = set_weight(mode, rms)
# Get the SB frequency
freq = np.mean(mx)
log.write("{0} {1} {2}\n".format(s, mrms, freq))
def make_rms_list(spec, output, transitions, z, dv, mode, f_col, y_col):
"""
"""
specs = glob.glob(spec)
crrls.natural_sort(specs)
with open(output, 'w') as log:
for s in specs:
data = np.loadtxt(s)
x = data[:, f_col]
y = data[:, y_col]
# Catch NaNs and invalid values:
mask_x0 = np.ma.masked_equal(x, 1.0).mask
mask_x1 = np.isnan(x)
mask_y = np.isnan(y)
mask = np.array(reduce(np.logical_or, [mask_x0, mask_x1, mask_y]))
# Remove NaNs and invalid values
mx = x[~mask]
my = y[~mask]
if len(x) > 0:
trans = transitions.split(',')
bf = []
for o, t in enumerate(trans):
n, f = crrls.find_lines_sb(mx, t, z)
bf.append(list(f))
if len(bf) > 0:
bf = np.array(list(_f for _bf in bf for _f in _bf))
x_lf, y_lf = crrls.blank_lines2(mx, my, bf, dv)
else:
x_lf, y_lf = mx, my
else:
pass
rms = crrls.get_rms(y_lf)
mrms = set_weight(mode, rms)
# Get the SB frequency
freq = np.mean(mx)
log.write("{0} {1} {2}\n".format(s, mrms, freq))
def load_bn_all(n_min=5, n_max=1000, verbose=False, location=LOCALDIR):
"""
"""
models = glob.glob('{0}/bn2/*_dat'.format(location))
natural_sort(models)
models = np.asarray(models)
models_tr = sorted(models,
key=lambda x:
(str2val(x.split('_')[3]), float(x.split('_')[5]),
str2val(x.split('_')[10])
if len(x.split('_')) > 17 else 0))
models = models_tr
Te = np.zeros(len(models))
ne = np.zeros(len(models))
Tr = np.zeros(len(models), dtype='|S20')
data = np.zeros((len(models), 5, n_max - n_min))
for i, model in enumerate(models):
if verbose:
print(model)
st = model.split('_')[3]
Te[i] = str2val(st)
sn = model.split('_')[5].rstrip('0')
ne[i] = float(sn)
if len(model.split('_')) <= 17:
Tr[i] = '-'
else:
Tr[i] = '_'.join(model.split('_')[8:11])
if verbose:
print("Trying to load model: ne={0}, te={1}, tr={2}".format(
ne[i], Te[i], Tr[i]))
bn = load_bn(st,
sn,
Tr=Tr[i],
n_min=n_min,
n_max=n_max,
verbose=verbose)
data[i, 0] = bn[:, 0]
data[i, 1] = bn[:, 1]
data[i, 2] = bn[:, 2]
data[i, 3] = bn[:, 3]
data[i, 4] = bn[:, 4]
return [Te, ne, Tr, data]
def load_itau_nelim(temp,
dens,
trad,
trans,
n_max=1000,
verbose=False,
value='itau'):
"""
Loads models given a temperature, radiation field and an
upper limit for the electron density.
"""
#LOCALDIR = os.path.dirname(os.path.realpath(__file__))
models = glob.glob('{0}/bbn2_{1}/*_T_{2}_*_{3}_*'.format(
LOCALDIR, trans, temp, trad))
#print models
natural_sort(models)
models = np.asarray(models)
models_len = np.asarray([len(model.split('_')) for model in models])
models = sorted(models,
key=lambda x:
(str2val(x.split('_')[4]), float(x.split('_')[6]),
str2val(x.split('_')[11])
if len(x.split('_')) > 17 else 0))
models = np.asarray(models)
nes = np.asarray(
[float(model.split('_')[6].rstrip('0')) for model in models])
# Only select those models with a density equal or lower than the specified value: dens.
models = models[nes <= dens]
#print models
return load_models(models,
trans,
n_max=n_max,
verbose=verbose,
value=value)
def valid_ne(line):
"""
Checks all the available models and lists the available ne values.
"""
#LOCALDIR = os.path.dirname(os.path.realpath(__file__))
models = glob.glob('{0}/bbn2_{1}/*'.format(LOCALDIR, line))
natural_sort(models)
models = np.asarray(models)
models_len = np.asarray([len(model.split('_')) for model in models])
#models_tr = models[models_len>17]
#print models_tr[0].split('_')[11], models_tr[0].split('_')[4], models_tr[0].split('_')[6]
models = sorted(models,
key=lambda x:
(str2val(x.split('_')[4]), float(x.split('_')[6]),
str2val(x.split('_')[11])
if len(x.split('_')) > 17 else 0))
ne = np.asarray(
[float(model.split('_')[6].rstrip('0')) for model in models])
return np.unique(ne)
table = Table(rows=data, names=('n', 'f0 (MHz)', 'center (km/s)', 'center_err (km/s)',
'itau (Hz)', 'itau_err (Hz)', 'FWHM (km/s)', 'FWHM_err (km/s)',
'tau', 'tau_err', 'FWHM_gauss (km/s)', 'FWHM_gauss_err (km/s)',
'FWHM_lorentz (km/s)', 'FWHM_lorentz_err (km/s)', 'residuals'),
dtype=('i3', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4'))
table.write(log, format='ascii.fixed_width')
if __name__ == '__main__':
dD_fix0 = 3.4/2.#crrls.sigma2FWHM(1.4456523-0*0.0267016)/2.
trans = 'alpha'
frng = 'all'
stacks = glob.glob('CI{0}_only_n*.ascii'.format(trans))
crrls.natural_sort(stacks)
prop = {'n':['812-863',
'760-806',
'713-748',
'668-709',
'623-665',
'580-621'],
'ns':[37,36,36,36,37,37]}
vel = []
tau = []
wei = []
fit3 = []
res3 = []
n = []
f0 = []
def sbsplot(spec, output, show_lines, transitions, z, x_axis, x_col, x_min,
x_max, y_axis, y_col, identifier):
"""
"""
pdf = PdfPages(output)
specs = glob.glob(spec)
crrls.natural_sort(specs)
# If only one file is passed, it probably contains a list
if len(specs) == 1:
specs = np.genfromtxt(specs[0], dtype=str)
try:
specs.shape[1]
specs = glob.glob(spec)
# Or a single file is to be plotted
except IndexError:
pass
for s in specs:
data = np.loadtxt(s)
x = data[:, x_col]
y = data[:, y_col]
# Determine the subband name
try:
sb = re.findall('{0}\d+'.format(identifier), s)[0]
except IndexError:
print("Could not find SB number.")
print("Will use the file name.")
sb = s
# Begin ploting
fig = plt.figure(frameon=False)
fig.suptitle(sb)
ax = fig.add_subplot(1, 1, 1, adjustable='datalim')
ax.step(x, y, 'k-', lw=1, where='mid')
# Mark the transitions?
if show_lines:
trans = transitions.split(',')
for o, t in enumerate(trans):
if x[~np.isnan(x)][0] > x[~np.isnan(x)][1]:
r = -1
else:
r = 1
qns, freqs = crrls.find_lines_sb(x[~np.isnan(x)][::r], t, z)
ylbl = np.ma.masked_invalid(y).mean()
for label, i, j in zip(qns, freqs, [ylbl] * len(freqs)):
plt.annotate(label,
xy=(i, j),
xytext=(-10, 15 * o + 5),
size='x-small',
textcoords='offset points',
ha='right',
va='bottom',
bbox=dict(boxstyle='round,pad=0.5',
fc='yellow',
alpha=0.5),
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3,rad=0'))
#if len(qns) > 0:
plt.annotate(tprops[t][0],
xy=(i, j),
xytext=(-4, 0),
textcoords='offset points',
size='xx-small')
plt.plot(freqs, [ylbl] * len(freqs),
marker='|',
ls='none',
ms=25,
c=tprops[t][1],
mew=8,
alpha=0.8)
ax.set_xlabel(x_axis)
ax.set_ylabel(y_axis)
if x_max:
ax.set_xlim(x_min, x_max)
pdf.savefig(fig)
plt.close(fig)
pdf.close()
parser.add_argument('--clobber',
help="Overwrite existing fits files?",
action='store_true')
parser.add_argument('-v', '--verbose', action='store_true',
help="Verbose output?")
parser.add_argument('-l', '--logfile', type=str, default=None,
help="Where to store the logs.\n" \
"(string, Default: output to console)")
args = parser.parse_args()
if args.verbose:
loglev = logging.DEBUG
else:
loglev = logging.ERROR
# Prepare the logger
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
formatter = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
logging.basicConfig(filename=args.logfile, level=loglev, format=formatter)
logger = logging.getLogger(__name__)
logger.info('Will extract a spectrum from cube: {0}'.format(args.cube))
logger.info('Will extract region: {0}'.format(args.region))
outfits = args.outfits
fitslist = args.fitslist
natural_sort(fitslist)
main(outfits, fitslist, args.stokeslast, args.chan_id, args.nzeros, args.clobber)
def remove_stack(spec, model, basename, transition, z, x_col, y_col, freq,
plot, plot_file, mode):
"""
"""
specs = glob.glob(spec)
crrls.natural_sort(specs)
# If only one file is passed, it probably contains a list
if len(specs) == 1:
specs = np.genfromtxt(specs[0], dtype=str)
try:
specs.shape[1]
specs = glob.glob(spec)
# Or a single file is to be processed
except IndexError:
pass
if plot:
pdf = PdfPages(plot_file)
for s in specs:
# Determine the subband name
try:
sb = re.findall('SB\d+', s)[0]
except IndexError:
print "Could not find SB number."
print "Will use SB???"
sb = 'SB???'
data = np.loadtxt(s)
p = data[:, x_col].argsort()
x = np.copy(data[p, x_col])
y = np.copy(data[p, y_col])
mask = np.isnan(y)
# Load model
mod = np.loadtxt(model)
p = mod[:, 0].argsort()
xm = mod[p, 0]
ym = mod[p, 1]
# remove NaNs
ym = ym[~np.isnan(xm)]
xm = xm[~np.isnan(xm)]
qns, freqs = crrls.find_lines_sb(x[~np.isnan(x)], transition, z)
#print qns
y_mod = np.zeros(len(y))
ys = np.copy(y)
ys[mask] = 0
x[mask] = -9999 # This way it should be outside the boundaries
if not freq:
for i, n in enumerate(qns):
# Convert the model velocity axis to frequency
fm = crrls.vel2freq(freqs[i] * (1. + z), xm * 1e3)
p = fm.argsort()
ymod = ym[p]
fm = fm[p]
# Interpolate the model axis to the spectrum grid
interp_ym = interpolate.interp1d(fm,
ymod,
kind='linear',
bounds_error=False,
fill_value=0.0)
y_mod += interp_ym(x)
else:
# Interpolate the model axis to the spectrum grid
interp_ym = interpolate.interp1d(xm,
ym,
kind='linear',
bounds_error=False,
fill_value=0.0)
y_mod += interp_ym(x)
# Remove the model
if 'sub' in mode.lower():
ys = ys - y_mod
off = 0.
elif 'div' in mode.lower():
ys = ((ys + 10.) / y_mod - 1.) * 10.
off = 10.
# Return the masked values to their NaN values
ys[mask] = np.nan
x[mask] = np.nan
if plot:
fig = plt.figure(frameon=False)
fig.suptitle(sb)
ax = fig.add_subplot(1, 1, 1, adjustable='datalim')
ax.step(x,
y_mod - off,
'r-',
drawstyle='steps',
lw=1,
where='pre',
label='model')
ax.step(x,
y,
'b-',
drawstyle='steps',
lw=1,
where='pre',
label='in')
ax.step(x,
ys,
'g-',
drawstyle='steps',
lw=1,
where='pre',
label='out')
ax.legend(loc=0, numpoints=1, frameon=False)
pdf.savefig(fig)
plt.close(fig)
data[:, y_col] = ys
np.savetxt('{0}_{1}.ascii'.format(basename, sb), data)
if plot:
pdf.close()
'--verbose',
action='store_true',
help="Verbose output?")
parser.add_argument('-l', '--logfile', type=str, default=None,
help="Where to store the logs.\n" \
"(string, Default: output to console)")
args = parser.parse_args()
if args.verbose:
loglev = logging.DEBUG
else:
loglev = logging.ERROR
# Prepare the logger
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
formatter = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
logging.basicConfig(filename=args.logfile, level=loglev, format=formatter)
logger = logging.getLogger(__name__)
outfits = args.outfits
fitslist = args.fitslist
natural_sort(fitslist)
logger.debug(fitslist)
main(outfits, fitslist, args.stokeslast, args.chan_id, args.chan_end,
args.nzeros, args.overwrite)
def load_itau_all(line='RRL_CIalpha',
n_min=5,
n_max=1000,
verbose=False,
value='itau'):
"""
Loads all the available models for Carbon.
:param line: Which models should be loaded.
:type line: string
:param n_min: Minimum n number to include in the output.
:type n_min: int
:param n_max: Maximum n number to include in the output.
:type n_max: int
:param verbose: Verbose output?
:type verbose: bool
:param value: ['itau'\|'bbnMdn'\|None] Which value should be in the output.
:type value: string
"""
#LOCALDIR = os.path.dirname(os.path.realpath(__file__))
models = glob.glob('{0}/bbn2_{1}/*'.format(LOCALDIR, line))
natural_sort(models)
models = np.asarray(models)
models_len = np.asarray([len(model.split('_')) for model in models])
models_tr = sorted(models,
key=lambda x:
(str2val(x.split('_')[4]), float(x.split('_')[6]),
str2val(x.split('_')[11])
if len(x.split('_')) > 17 else 0))
models = models_tr
Te = np.zeros(len(models))
ne = np.zeros(len(models))
other = np.zeros(len(models), dtype='|S20')
data = np.zeros((len(models), 2, n_max - n_min))
for i, model in enumerate(models):
if verbose:
print(model)
st = model.split('_')[4]
Te[i] = str2val(st)
sn = model.split('_')[6].rstrip('0')
ne[i] = float(sn)
if len(model.split('_')) <= 17:
other[i] = '-'
else:
other[i] = '_'.join(model.split('_')[9:12])
if verbose:
print("Trying to load model: ne={0}, te={1}, tr={2}".format(
ne[i], Te[i], other[i]))
n, int_tau = itau(st,
ne[i],
line,
n_min=n_min,
n_max=n_max,
other=other[i],
verbose=verbose,
value=value)
data[i, 0] = n
data[i, 1] = int_tau
return [Te, ne, other, data]
'itau (Hz)', 'itau_err (Hz)', 'FWHM (km/s)', 'FWHM_err (km/s)',
'tau', 'tau_err', 'FWHM_gauss (km/s)', 'FWHM_gauss_err (km/s)',
'FWHM_lorentz (km/s)', 'FWHM_lorentz_err (km/s)', 'residuals'),
dtype=('i3', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4',
'f4', 'f4', 'f4', 'f4', 'f4'))
table.write(log, format='ascii.fixed_width')
if __name__ == '__main__':
dD_fix0 = 3.4 / 2. #crrls.sigma2FWHM(1.4456523-0*0.0267016)/2.
trans = 'alpha'
frng = 'all'
stacks = glob.glob('CI{0}_only_n*.ascii'.format(trans))
crrls.natural_sort(stacks)
prop = {
'n':
['812-863', '760-806', '713-748', '668-709', '623-665', '580-621'],
'ns': [37, 36, 36, 36, 37, 37]
}
vel = []
tau = []
wei = []
fit3 = []
res3 = []
n = []
f0 = []
data0 = np.empty((len(stacks), 15))