def read_data_files(pattern):
files = glob.glob(pattern)
if not files:
raise ValueError("cannot find any matching files: " % pattern)
keys, times = zip(*[stats.read_raw_data(fname) for fname in files])
validate_keys(keys)
means = np.mean(times, axis=0)
variances = np.var(times, axis=0)
return Data(keys[0], np.array(times), means, variances)
def read_data_files(pattern):
files = glob.glob(pattern)
if not files:
raise ValueError("cannot find any matching files: " % pattern)
keys, times = zip(*[stats.read_raw_data(fname) for fname in files])
validate_keys(keys)
means = np.mean(times, axis=0)
variances = np.var(times, axis=0)
return Data(keys[0], np.array(times), means, variances)
def read_data_files(pattern):
files = glob.glob(pattern)
# print "processing {} file(s)".format(len(files))
if not files:
raise ValueError("cannot find any matching files: %s" % pattern)
keys, times = zip(*[stats.read_raw_data(fname) for fname in files])
for idx, i in enumerate(keys):
if keys[0] != i:
print files[idx]
raise ValueError("inconsistent data files")
means = np.mean(times, axis=0)
variances = np.var(times, axis=0)
return Data(keys[0], np.array(times), means, variances)
def read_data_files(pattern):
files = glob.glob(pattern)
# print "processing {} file(s)".format(len(files))
if not files:
raise ValueError("cannot find any matching files: %s" % pattern)
keys, times = zip(*[stats.read_raw_data(fname) for fname in files])
for idx, i in enumerate(keys):
if keys[0] != i:
print files[idx]
raise ValueError("inconsistent data files")
means = np.mean(times, axis=0)
variances = np.var(times, axis=0)
return Data(keys[0], np.array(times), means, variances)