def get_simulation_data(self):
"""Extract simulation data.
Extracts the simulation data from the SAGE parameter file and
returns a dictionary containing the values.
"""
if not self.args.box_size:
raise tao.ConversionError('Must specify a box-size')
if not self.args.parameters:
raise tao.ConversionError('Must specify a SAGE parameter file')
par = open(self.args.parameters, 'r').read()
hubble = np.float32(re.search(r'Hubble_h\s+(\d*\.?\d*)',
par, re.I).group(1))
if hubble < 1.0:
hubble = hubble * 100.0
msg = 'Hubble parameter must be in physical units (not little h)'
assert hubble > 1.0, msg
hubble = str(hubble)
omega_m = re.search(r'Omega\s+(\d*\.?\d*)', par, re.I).group(1)
omega_l = re.search(r'OmegaLambda\s+(\d*\.?\d*)', par, re.I).group(1)
return {
'box_size': self.args.box_size,
'hubble': hubble,
'omega_m': omega_m,
'omega_l': omega_l,
}
def read_file_possibly_gzipped(file,max_chunks):
if not hasattr(file,'__read__'):
try:
f = open(file,'r')
except IOError:
f = gzip.open(file+'.gz','rb')
except:
raise tao.ConversionError('Could not open file. Neither as ascii or gz')
try:
chunks = f.read(max_chunks)
except EOFError:
## not enough bytes. Just read-in the entire file then
chunks = f.read()
except:
raise tao.ConversionError('Could not read in from the file')
f.close
return chunks
def get_simulation_data(self):
"""Extract simulation data.
Extracts the simulation data from the SAGE parameter file and
returns a dictionary containing the values.
"""
if not self.args.box_size:
raise tao.ConversionError('Must specify a box-size')
if not self.args.parameters:
raise tao.ConversionError('Must specify a SAGE parameter file')
par = open(self.args.parameters, 'r').read()
hubble = re.search(r'Hubble_h\s+(\d*\.?\d*)', par, re.I).group(1)
omega_m = re.search(r'Omega\s+(\d*\.?\d*)', par, re.I).group(1)
omega_l = re.search(r'OmegaLambda\s+(\d*\.?\d*)', par, re.I).group(1)
return {
'box_size': self.args.box_size,
'hubble': hubble,
'omega_m': omega_m,
'omega_l': omega_l,
}
def get_snapshot_redshifts(self):
"""Parse and convert the expansion factors.
Uses the expansion factors to calculate snapshot redshifts. Returns
a list of redshifts in order of snapshots.
"""
if not self.args.a_list:
raise tao.ConversionError('Must specify a filename for the a-list')
redshifts = []
with open(self.args.a_list, 'r') as file:
for line in file:
line = line.strip()
if line:
redshifts.append(1.0 / float(line) - 1.0)
if len(redshifts) == 0:
msg = "Could not parse any redshift values in file {0}"\
.format(self.args.a_list)
raise tao.ConversionError(msg)
print("Found {0} redshifts in file {1}".format(len(redshifts),
self.args.a_list))
return redshifts
def get_snapshot_redshifts(self):
"""Parse and convert the expansion factors.
Uses the expansion factors to calculate snapshot redshifts. Returns
a list of redshifts in order of snapshots.
"""
if not self.args.a_list:
raise tao.ConversionError('Must specify a filename for the a-list')
redshifts = []
with open(self.args.a_list, 'r') as file:
for line in file:
redshifts.append(1.0 / float(line) - 1.0)
return redshifts
def get_simulation_data(self,trees_dir=None):
"""Extract simulation data.
Extracts the simulation data from the CTREES parameter file and
returns a dictionary containing the values.
"""
if trees_dir is None:
if self.args.parameters:
par = open(self.args.parameters, 'r').read()
hubble = re.search(r'h0\s*=\s*(\d*\.?\d*)', par, re.I).group(1)
omega_m = re.search(r'Om\s*=\s*(\d*\.?\d*)', par, re.I).group(1)
omega_l = re.search(r'Ol\s*=\s*(\d*\.?\d*)', par, re.I).group(1)
box_size = re.search(r'BOX_WIDTH\s*=\s*(\d*\.?\d*)', par, re.I).group(1)
else:
if not self.args.trees_dir:
raise tao.ConversionError('Must specify either the CTREES config or the trees location')
## read in the first chunk bytes. (DO NOT read in entire file)
chunk_guess = 5000
first_tree = read_file_possibly_gzipped(self.args.trees_dir+'/tree_0_0_0.dat',chunk_guess)
hubble = re.search(r'h0\s*=\s*(\d*\.?\d*)', first_tree, re.I).group(1)
omega_m = re.search(r'Omega_M\s*=\s*(\d*\.?\d*)', first_tree, re.I).group(1)
omega_l = re.search(r'Omega_L\s*=\s*(\d*\.?\d*)', first_tree, re.I).group(1)
box_size = re.search(r'Full\s+box\s+size\s+=\s+(\d*\.?\d*)', first_tree, re.I).group(1)
else:
## read in the first chunk bytes. (DO NOT read in entire file)
chunk_guess = 5000
first_tree = read_file_possibly_gzipped(trees_dir+'/tree_0_0_0.dat',chunk_guess)
hubble = re.search(r'h0\s*=\s*(\d*\.?\d*)', first_tree, re.I).group(1)
omega_m = re.search(r'Omega_M\s*=\s*(\d*\.?\d*)', first_tree, re.I).group(1)
omega_l = re.search(r'Omega_L\s*=\s*(\d*\.?\d*)', first_tree, re.I).group(1)
box_size = re.search(r'Full\s+box\s+size\s+=\s+(\d*\.?\d*)', first_tree, re.I).group(1)
return {
'box_size': box_size,
'hubble': hubble,
'omega_m': omega_m,
'omega_l': omega_l,
}
def get_snapshot_redshifts(self,scales_file=None):
"""Parse and convert the expansion factors.
Uses the expansion factors to calculate snapshot redshifts. Returns
a list of redshifts in order of snapshots.
"""
if scales_file is None:
if not self.args.a_list:
raise tao.ConversionError('Must specify a filename for the a-list')
else:
scales_file = self.args.a_list
redshifts = []
with open(scales_file, 'r') as file:
for line in file:
items = line.split()
## scales.txt contains snapshot number (items[0]) and scale factor (items[1])
redshifts.append(1.0/float(items[1]) - 1.0)
return redshifts