Source code for slugpy.read_cluster_sn

Function to read a SLUG2 cluster_sn file.

import numpy as np
from collections import namedtuple
import struct
import re
from .slug_open import slug_open

[docs]def read_cluster_sn(model_name, output_dir=None, fmt=None, verbose=False, read_info=None): """ Function to read a SLUG2 cluster_sn 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 : 'txt' | 'ascii' | 'bin' | 'binary' | 'fits' | 'fits2' Format for the file to be read. If one of these is set, the function will only attempt to open ASCII-('txt' or 'ascii'), binary ('bin' or 'binary'), or FITS ('fits' or 'fits2') 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 tot_sn : array, shape (N_cluster) total number of supernovae produced by each cluster up to the indicated time stoch_sn : array, shape (N_cluster) total number of supernovae produced by stars being treated stochastically in each cluster up to the indicated time Raises IOError, if no sn file can be opened """ # Open file fp, fname = slug_open(model_name+"_cluster_sn", output_dir=output_dir, fmt=fmt) # See if this file is a checkpoint file if len(re.findall('_chk\d\d\d\d', model_name)) != 0: checkpoint = True else: checkpoint = False # Print status if verbose: print("Reading cluster supernovae for model "+model_name) if read_info is not None: read_info['fname'] = fname # Prepare storage cluster_id = [] time = [] trial = [] tot_sn = [] stoch_sn = [] # Read ASCII or binary if fname.endswith('.txt'): # ASCII mode if read_info is not None: read_info['format'] = 'ascii' # If this is a checkpoint file, skip the line stating how many # trials it contains; this line is not guaranteed to be # accurate, and is intended for the C++ code, not for us if checkpoint: fp.readline() # Read the first header line hdr = fp.readline() # Burn the header lines fp.readline() fp.readline() # Read data trialptr = 0 for entry in fp: if entry[:3] == '---': # Separator line trialptr = trialptr + 1 continue data = entry.split() cluster_id.append(int(data[0])) trial.append(trialptr) time.append(float(data[1])) tot_sn.append(float(data[2])) stoch_sn.append(int(data[3])) elif fname.endswith('.bin'): # Binary mode if read_info is not None: read_info['format'] = 'binary' # If this is a checkpoint, skip the bytes specifying how many # trials we have; this is inteded for the C++ code, not for # us, since we will determine that on our own if checkpoint: data ='i')) # Go through file while True: # Read number of clusters and time in next block, checking # if we've hit eof data ='LdL')) if len(data) < struct.calcsize('LdL'): break trialptr, t, ncluster = struct.unpack('LdL', data) # Skip if no clusters if ncluster == 0: continue # Read the next block of clusters datastr = 'LdL' data =*ncluster) data_list = struct.unpack(datastr*ncluster, data) # Pack these clusters into the data list cluster_id.extend(data_list[0::3]) time.extend([t]*ncluster) trial.extend([trialptr]*ncluster) tot_sn.extend(data_list[1::3]) stoch_sn.extend(data_list[2::3]) elif fname.endswith('.fits'): # FITS mode if read_info is not None: read_info['format'] = 'fits' cluster_id = fp[1].data.field('UniqueID') trial = fp[1].data.field('Trial') time = fp[1].data.field('Time') tot_sn = fp[1].data.field('TotSN') stoch_sn = fp[1].data.field('StochSN') # Close file fp.close() # Convert lists to arrays cluster_id = np.array(cluster_id, dtype='uint') trial = np.array(trial, dtype='uint') time = np.array(time) tot_sn = np.array(tot_sn) stoch_sn = np.array(stoch_sn, dtype='uint') # Build the namedtuple to hold output out_list = ['id', 'trial', 'time', 'tot_sn', 'stoch_sn'] out_dat = [cluster_id, trial, time, tot_sn, stoch_sn] out_type = namedtuple('cluster_int', out_list) out = out_type(*out_dat) # Return return out