def getData(url):
"""
Get tabular data from either a text file or HDF5 table, and return it as a
numpy array.
"""
path = getFromURL(url)
if use_hdf5 and tables.isHDF5File(path):
h5file = tables.openFile(path, mode="r")
assert len(h5file.listNodes(h5file.root, classname="Table")) == 1, "File contains more than one table."
table = h5file.listNodes(h5file.root, classname="Table")[0]
arr = numpy.zeros((table.nrows, len(table.colnames)), "float")
for i, row in enumerate(table):
for j, colname in enumerate(table.colnames):
arr[i, j] = row[colname]
elif os.path.isfile(path):
f = srblib.urlopen(url)
arr = numpy.array([l.split() for l in f.readlines() if l[0] != "#"]).astype("float")
f.close()
else:
raise Exception("Not a valid file.")
return arr
def getData(url):
"""
Get tabular data from either a text file or HDF5 table, and return it as a
numpy array.
"""
path = getFromURL(url)
if use_hdf5 and tables.isHDF5File(path):
h5file = tables.openFile(path, mode='r')
assert len(h5file.listNodes(
h5file.root,
classname="Table")) == 1, "File contains more than one table."
table = h5file.listNodes(h5file.root, classname="Table")[0]
arr = numpy.zeros((table.nrows, len(table.colnames)), 'float')
for i, row in enumerate(table):
for j, colname in enumerate(table.colnames):
arr[i, j] = row[colname]
elif os.path.isfile(path):
f = srblib.urlopen(url)
arr = numpy.array([l.split() for l in f.readlines()
if l[0] != '#']).astype('float')
f.close()
else:
raise Exception("Not a valid file.")
return arr
def txt_to_hdf5(input_url, output_url):
"""
Convert a data file in ascii format to a simple HDF5 file containing a
single table. The data file should contain numerical data in columns
separated by spaces and/or tabs.
Lines beginning with '#' are taken to be comments.
Column headings can be specified as a comment on the first line of the file.
Example:
# time voltage
0.0 -65.0
0.1 -64.8
"""
if not use_hdf5:
raise Exception("PyTables not installed.")
f = srblib.urlopen(input_url)
contents = f.readlines()
f.close()
# extract header information
firstline = contents[0]
headers = []
udict = {}
if firstline[0] == "#":
if '"' in firstline:
headers = [h.strip() for h in firstline[1:].split('"') if h and h.strip()]
else:
headers = firstline[1:].split()
for h, colname in enumerate(headers):
# check for units
p = colname.find("(")
if p > 0:
units = colname[p:]
colname = colname[:p].strip().replace(" ", "_")
udict[colname] = units
headers[h] = colname.strip().replace(" ", "_")
secondline = contents[1]
nfields = len(secondline.split())
if len(headers) != nfields:
headers = ["col%d" % i for i in range(1, nfields + 1)]
# create row format
class_str = "class DataRow(tables.IsDescription):\n"
for pos, colname in enumerate(headers):
class_str += " %s = tables.Float32Col(pos=%d)\n" % (colname, pos)
exec (class_str)
# create HDF5 file
protocol, path = output_url.split("://")
path, ext = os.path.splitext(path)
filename = os.path.basename(path)
assert protocol == "file", "For now, we only support writing to local files."
h5file = tables.openFile("%s.h5" % path, mode="w", title=filename)
# create and fill table
table = h5file.createTable(h5file.root, filename, DataRow, "Converted from %s" % input_url)
for k, v in udict.items():
table.attrs.__setattr__("units_%s" % k, v)
for line in contents[1:]:
line = line.strip()
data = [float(x) for x in line.split()]
row = table.row
for col, val in zip(headers, data):
row[col] = val
row.append()
table.flush()
h5file.close()
def txt_to_hdf5(input_url, output_url):
"""
Convert a data file in ascii format to a simple HDF5 file containing a
single table. The data file should contain numerical data in columns
separated by spaces and/or tabs.
Lines beginning with '#' are taken to be comments.
Column headings can be specified as a comment on the first line of the file.
Example:
# time voltage
0.0 -65.0
0.1 -64.8
"""
if not use_hdf5:
raise Exception("PyTables not installed.")
f = srblib.urlopen(input_url)
contents = f.readlines()
f.close()
# extract header information
firstline = contents[0]
headers = []
udict = {}
if firstline[0] == '#':
if '"' in firstline:
headers = [
h.strip() for h in firstline[1:].split('"') if h and h.strip()
]
else:
headers = firstline[1:].split()
for h, colname in enumerate(headers):
# check for units
p = colname.find("(")
if p > 0:
units = colname[p:]
colname = colname[:p].strip().replace(" ", "_")
udict[colname] = units
headers[h] = colname.strip().replace(" ", "_")
secondline = contents[1]
nfields = len(secondline.split())
if len(headers) != nfields:
headers = ['col%d' % i for i in range(1, nfields + 1)]
# create row format
class_str = "class DataRow(tables.IsDescription):\n"
for pos, colname in enumerate(headers):
class_str += " %s = tables.Float32Col(pos=%d)\n" % (colname, pos)
exec(class_str)
# create HDF5 file
protocol, path = output_url.split('://')
path, ext = os.path.splitext(path)
filename = os.path.basename(path)
assert protocol == 'file', "For now, we only support writing to local files."
h5file = tables.openFile('%s.h5' % path, mode='w', title=filename)
# create and fill table
table = h5file.createTable(h5file.root, filename, DataRow,
"Converted from %s" % input_url)
for k, v in udict.items():
table.attrs.__setattr__("units_%s" % k, v)
for line in contents[1:]:
line = line.strip()
data = [float(x) for x in line.split()]
row = table.row
for col, val in zip(headers, data):
row[col] = val
row.append()
table.flush()
h5file.close()