def MissingValue(self, name):
"""
Return the value denoting "missing value" for the supplied field
"""
variable = self.Variable(name)
if hasattr(variable, "missing_value"):
assert (len(variable.missing_value) == 1)
return float(variable.missing_value)
else:
return calc.Nan()
def JoinStat(*args):
"""
Joins a series of stat files together. Useful for combining checkpoint .stat
files. Selects data in later stat files over earlier stat files. Assumes
data in stat files are sorted by ElapsedTime.
"""
nStat = len(args)
assert (nStat > 0)
times = [stat["ElapsedTime"] for stat in args]
startT = [t[0] for t in times]
permutation = utils.KeyedSort(startT, range(nStat))
stats = [args[index] for index in permutation]
startT = [startT[index] for index in permutation]
times = [times[index] for index in permutation]
endIndices = numpy.array([len(time) for time in times], dtype=int)
for i, t in enumerate(times[:-1]):
for j, time in enumerate(t):
if calc.AlmostEquals(startT[i + 1], time, tolerance=1.0e-6):
endIndices[i] = max(j - 1, 0)
break
elif startT[i + 1] < time:
endIndices[i] = j
break
debug.dprint("Time ranges:")
if len(times) > 0:
for i in range(nStat):
debug.dprint((startT[i], times[i][endIndices[i] - 1]))
else:
debug.dprint("No data")
dataIndices = numpy.empty(len(args) + 1, dtype=int)
dataIndices[0] = 0
for i, index in enumerate(endIndices):
dataIndices[i + 1] = dataIndices[i] + index
stat = stats[0]
data = {}
for key in stat.keys():
arr = stat[key]
shape = list(arr.shape)
shape[0] = dataIndices[-1]
data[key] = numpy.empty(shape, dtype=arr.dtype)
data[key][:dataIndices[1]] = arr[:endIndices[0]]
data[key][dataIndices[1]:] = calc.Nan()
delimiter = stat.GetDelimiter()
for i in range(1, nStat):
stat = stats[i]
for key in stat.keys():
arr = stat[key]
if not key in data:
shape = list(arr.shape)
shape[0] = dataIndices[-1]
data[key] = numpy.empty(shape, dtype=arr.dtype)
data[key][:dataIndices[i]] = calc.Nan()
data[key][dataIndices[i + 1]:] = calc.Nan()
data[key][dataIndices[i]:dataIndices[i + 1]] = arr[:endIndices[i]]
output = Stat(delimiter=delimiter)
for key in data.keys():
output[key] = numpy.array(data[key])
return output