"""
Function to read a SLUG2 cluster_cloudylines file.
"""
import numpy as np
from collections import namedtuple
import struct
from ..slug_open import slug_open
[docs]def read_cluster_cloudylines(model_name, output_dir=None, fmt=None,
verbose=False, read_info=None):
"""
Function to read a SLUG2 cluster_cloudylines file.
Parameters
model_name : string
The name of the model to be read
output_dir : string
The directory where the SLUG2 output is located; if set to None,
the current directory is searched, followed by the SLUG_DIR
directory if that environment variable is set
fmt : string
Format for the file to be read. Allowed values are 'ascii',
'bin' or 'binary, and 'fits'. If one of these is set, the code
will only attempt to open ASCII-, binary-, or FITS-formatted
output, ending in .txt., .bin, or .fits, respectively. If set
to None, the code will try to open ASCII files first, then if
it fails try binary files, and if it fails again try FITS
files.
verbose : bool
If True, verbose output is printed as code runs
read_info : dict
On return, this dict will contain the keys 'fname' and
'format', giving the name of the file read and the format it
was in; 'format' will be one of 'ascii', 'binary', or 'fits'
Returns
A namedtuple containing the following fields:
id : array, dtype uint
unique ID of cluster
trial: array, dtype uint
which trial was this cluster part of
time : array
times at which cluster spectra are output, in yr
cloudy_linelabel : array, dtype='S4', shape (N_lines)
labels for the lines, following cloudy's 4 character line label
notation
cloudy_linewl : array, shape (N_lines)
rest wavelength for each line, in Angstrom
cloudy_linelum : array, shape (N_cluster, N_lines)
luminosity of each line at each time for each trial, in erg/s
"""
# Open file
fp, fname = slug_open(model_name+"_cluster_cloudylines",
output_dir=output_dir,
fmt=fmt)
if read_info is not None:
read_info['fname'] = fname
# Print status
if verbose:
print("Reading cluster cloudy line luminosities for "
"model "+model_name)
if read_info is not None:
read_info['fname'] = fname
# Read data
if fname.endswith('.txt'):
# ASCII mode
if read_info is not None:
read_info['format'] = 'ascii'
# Prepare storage
cluster_id = []
time = []
trial = []
label = []
wl = []
lum = []
# Burn the three header lines
fp.readline()
fp.readline()
fp.readline()
# Read first line and store cluster data
trialptr = 0
entry = fp.readline()
cluster_id.append(int(entry[0:11]))
time.append(float(entry[14:25]))
label.append(entry[35:39])
wl.append(float(entry[42:53]))
lum.append(float(entry[56:67]))
trial.append(trialptr)
# Read the rest of the data for first cluster
while True:
entry = fp.readline()
# Check for EOF and separator lines
if entry == '':
break
if entry[:3] == '---':
trialptr = trialptr+1
break
# Get id, time, luminosity
id_tmp = int(entry[0:11])
time_tmp = float(entry[14:25])
lum.append(float(entry[56:67]))
# Stop when we find a different cluster or a different time
if id_tmp != cluster_id[0] or time_tmp != time[0]:
break
# Still the same cluster, so append to line label and
# wavelength lists
label.append(entry[35:39])
wl.append(float(entry[42:53]))
# We have now read one full chunk, so we know how many
# wavelength entries per cluster there are
nl = len(wl)
# Start of next chunk
ptr = 1
# Now read through rest of file
while True:
# Read a line
entry = fp.readline()
if entry == '':
break
if entry[:3] == '---':
trialptr = trialptr+1
continue
lum.append(float(entry[56:67]))
ptr = ptr+1
# When we get to the end of a chunk, push cluster ID,
# time, trial number list, then reset pointer
if ptr == nl:
cluster_id.append(int(entry[0:11]))
time.append(float(entry[14:25]))
trial.append(trialptr)
ptr = 0
# Convert to arrays
wl = np.array(wl)
label = np.array(label)
cluster_id = np.array(cluster_id, dtype='uint')
time = np.array(time)
trial = np.array(trial, dtype='uint')
lum = np.reshape(np.array(lum), (len(cluster_id), nl))
elif fname.endswith('.bin'):
# Binary mode
if read_info is not None:
read_info['format'] = 'binary'
# Read number of lines
nl = int(fp.readline())
# Read line labels
label = []
for i in range(nl):
label.append(fp.readline().split()[0])
# Read the line wavelengths
data = fp.read(struct.calcsize('d')*nl)
wl = np.array(struct.unpack('d'*nl, data))
# Prepare storage
cluster_id = []
time = []
trial = []
lum = []
# Go through the rest of the file
trialptr = 0
while True:
# Read number of clusters and time in next block, checking
# if we've hit eof
data = fp.read(struct.calcsize('LdL'))
if len(data) < struct.calcsize('LdL'):
break
trialptr, t, ncluster = struct.unpack('LdL', data)
# Skip if no clusters
if ncluster == 0:
continue
# Add to time and trial arrays
time.extend([t]*ncluster)
trial.extend([trialptr]*ncluster)
# Read the next block of clusters
data = fp.read(struct.calcsize('L')*ncluster +
struct.calcsize('d')*ncluster*nl)
data_list = struct.unpack(('L'+'d'*nl)*ncluster, data)
# Pack clusters into data list
cluster_id.extend(data_list[::nl+1])
lum.extend(
[data_list[(nl+1)*i+1:(nl+1)*i+1+nl]
for i in range(ncluster)])
# Convert to arrays
wl = np.array(wl)
label = np.array(label)
cluster_id = np.array(cluster_id, dtype='uint')
time = np.array(time)
trial = np.array(trial, dtype='uint')
lum = np.reshape(np.array(lum), (len(cluster_id), nl))
elif fname.endswith('.fits'):
# FITS mode
if read_info is not None:
read_info['format'] = 'fits'
# Get the line labels and wavelengths from the first HDU
label = fp[1].data.field('Line_Label').flatten()
wl = fp[1].data.field('Wavelength').flatten()
# Get time, trial, id, luminosity from second HDU
cluster_id = fp[2].data.field('UniqueID')
trial = fp[2].data.field('Trial')
time = fp[2].data.field('Time')
lum = fp[2].data.field('Line_luminosity')
# Build namedtuple to hold output
out_type = namedtuple('cluster_cloudylines',
['id', 'trial', 'time', 'cloudy_linelist',
'cloudy_linewl', 'cloudy_linelum'])
out = out_type(cluster_id, trial, time, label, wl, lum)
# Return
return out