def get_all_isochrones(filename):
#first go through the file and find how many total rows and columns
#of data you have, how many isochrones you have, and the starting
#index & metadata of each isochrone
# pretend these are the values you get
f=open(filename,'r')
lines = f.readlines()
f.close()
start_indices = []
metallicities = []
ages = []
Nrows = 0
N_isochrones = 0
for i in range(len(lines)):
# The start of the column head # log age/yr
if lines[i].startswith('# l'):
start_indices.append(i+1)
if not lines[i].startswith('#'):
Nrows += 1
if lines[i].startswith('#\tI'):
N_isochrones += 1
line = lines[i]
line = line.split()
metallicities.append(float(line[4]))
ages.append(float(line[7]))
colhead = lines[start_indices[0]-1].strip()
colhead = colhead.replace('#','')
colhead = colhead.replace('/','')
col_keys = colhead.split()
col_keys[0] = 'LogAge'
Ncols = len(col_keys)
#now go back through the file and read in all the rows and columns of data
data = np.ndarray(shape=(Nrows,Ncols), dtype=float)
row = 0
for i in range(len(lines)):
#print i
if lines[i].startswith('#'): continue
items = [is_numeric(m) for m in lines[i].split()]
if len(lines[i].split()) != Ncols: items.append('')
data[row] = items
row += 1
IsoDict = {}
#this will help with slicing: see below
start_indices = np.concatenate((start_indices,[-1]))
#create your dictionary of isochrone objects
for i in range(N_isochrones):
key = create_key(metallicities[i],ages[i])
IsoDict[key] = Isochrone(metallicities[i],
ages[i],
data[start_indices[i]:start_indices[i+1]],
col_keys)
return IsoDict, col_keys
def get_all_isochrones(filename):
#first go through the file and find how many total rows and columns
#of data you have, how many isochrones you have, and the starting
#index & metadata of each isochrone
# pretend these are the values you get
f = open(filename, 'r')
lines = f.readlines()
f.close()
start_indices = []
metallicities = []
ages = []
Nrows = 0
N_isochrones = 0
for i in range(len(lines)):
# The start of the column head # log age/yr
if lines[i].startswith('# l'):
start_indices.append(i + 1)
if not lines[i].startswith('#'):
Nrows += 1
if lines[i].startswith('#\tI'):
N_isochrones += 1
line = lines[i]
line = line.split()
metallicities.append(float(line[4]))
ages.append(float(line[7]))
colhead = lines[start_indices[0] - 1].strip()
colhead = colhead.replace('#', '')
colhead = colhead.replace('/', '')
col_keys = colhead.split()
col_keys[0] = 'LogAge'
Ncols = len(col_keys)
#now go back through the file and read in all the rows and columns of data
data = np.ndarray(shape=(Nrows, Ncols), dtype=float)
row = 0
for i in range(len(lines)):
#print i
if lines[i].startswith('#'): continue
items = [is_numeric(m) for m in lines[i].split()]
if len(lines[i].split()) != Ncols: items.append('')
data[row] = items
row += 1
IsoDict = {}
#this will help with slicing: see below
start_indices = np.concatenate((start_indices, [-1]))
#create your dictionary of isochrone objects
for i in range(N_isochrones):
key = create_key(metallicities[i], ages[i])
IsoDict[key] = Isochrone(metallicities[i], ages[i],
data[start_indices[i]:start_indices[i + 1]],
col_keys)
return IsoDict, col_keys
def read_table_mixed_type(file,delimiter=','):
# writen to read a csv file with mixed float/str
# add funcionality for comment lines?
lines = open(file,'r').readlines()
col_keys = lines[0].split(delimiter)
Ncols = len(col_keys)
Nrows = len(lines)-1
data = {}
for key in col_keys:
data[key] = []
for line in lines[1::]:
item = line.strip().split(delimiter)
for j,key in enumerate(col_keys):
data[key].append(is_numeric(item[j]))
return data
def read_table_mixed_type(file, delimiter=','):
# writen to read a csv file with mixed float/str
# add funcionality for comment lines?
lines = open(file, 'r').readlines()
col_keys = lines[0].split(delimiter)
Ncols = len(col_keys)
Nrows = len(lines) - 1
data = {}
for key in col_keys:
data[key] = []
for line in lines[1::]:
item = line.strip().split(delimiter)
for j, key in enumerate(col_keys):
data[key].append(is_numeric(item[j]))
return data
def get_data(filename):
print 'dont use get_data, use GenUtils.read_table'
sys.exit()
f = open(filename, 'r')
lines = f.readlines()
f.close()
d = {}
col_heads = lines[0].replace('#', '')
col_keys = col_heads.split()
for key in col_keys:
d[key] = []
for i in range(len(lines)):
if lines[i].startswith('#'): continue
data = lines[i].split()
for key, i in zip(col_keys, range(len(col_keys))):
d[key].append(is_numeric(data[i]))
return d
def get_data(filename):
print 'dont use get_data, use GenUtils.read_table'
sys.exit()
f = open(filename,'r')
lines = f.readlines()
f.close()
d = {}
col_heads = lines[0].replace('#','')
col_keys = col_heads.split()
for key in col_keys:
d[key]=[]
for i in range(len(lines)):
if lines[i].startswith('#'): continue
data = lines[i].split()
for key,i in zip(col_keys,range(len(col_keys))):
d[key].append(is_numeric(data[i]))
return d
def read_tagged_data(filename):
# Hopefully you pasted the line with the fiter names on the header.
f = open(filename, 'r')
lines = f.readlines()
f.close()
i = 0
d = {}
for line in lines:
if line.startswith('#'):
line = line.replace('#', '')
col_keys = line.split()
i += 1
for key in col_keys:
d[key] = []
if i == 0:
print 'I don' 't know the names of the damn filters.'
for line in lines:
if line.startswith('#'): continue
row = line.split()
for key, i in zip(col_keys, range(len(col_keys))):
d[key].append(is_numeric(row[i]))
return d
def read_tagged_data(filename):
# Hopefully you pasted the line with the fiter names on the header.
f = open(filename,'r')
lines = f.readlines()
f.close()
i = 0
d = {}
for line in lines:
if line.startswith('#'):
line = line.replace('#','')
col_keys = line.split()
i += 1
for key in col_keys:
d[key] = []
if i == 0:
print 'I don''t know the names of the damn filters.'
for line in lines:
if line.startswith('#'): continue
row = line.split()
for key,i in zip(col_keys,range(len(col_keys))):
d[key].append( is_numeric(row[i]))
return d
