def writeIpol(fname, ipolDict, params, runs=[], summary="", runsdir=""):
PARAMNAMES = params[0]
PARAMSLIST = params[1]
import os, tempfile, zlib
if fname == "temp":
f = tempfile.NamedTemporaryFile(delete=False)
else:
f = open(fname, "w")
import professor2 as prof
f.write("Summary: %s\n" % summary)
f.write("DataDir: %s\n" % os.path.abspath(runsdir))
f.write("ProfVersion: %s\n" % prof.version())
f.write("Date: %s\n" % prof.mk_timestamp())
f.write(
"DataFormat: binned 3\n"
) # This tells the reader how to treat the coefficients that follow
# Format and write out parameter names
pstring = "ParamNames:"
for p in PARAMNAMES:
pstring += " %s" % p
f.write(pstring + "\n")
# Dimension (consistency check)
f.write("Dimension: %i\n" % len(PARAMNAMES))
# Interpolation validity (hypercube edges)
# TODO a lot of this is obsolete in the format 3
minstring = "MinParamVals:"
for v in prof.mk_minvals(PARAMSLIST):
minstring += " %f" % v
f.write(minstring + "\n")
maxstring = "MaxParamVals:"
for v in prof.mk_maxvals(PARAMSLIST):
maxstring += " %f" % v
f.write(maxstring + "\n")
f.write("DoParamScaling: 1\n")
# Number of inputs per bin
f.write("NumInputs: %i\n" % len(PARAMSLIST))
s_runs = "Runs:"
for r in runs:
s_runs += " %s" % r
f.write("%s\n" % s_runs)
f.write("---\n")
## Write out numerical data for all interpolations
s = ""
HNAMES = sorted(list(set([x[0] for x in ipolDict.keys()])))
for hn in sorted(HNAMES):
thisbins = sorted(filter(lambda x: x[0] == hn, ipolDict.keys()))
for ipolstring in [ipolDict[x] for x in thisbins]:
s += zlib.decompress(ipolstring)
f.write(s)
f.close()
if not fname == "temp":
print "\nOutput written to %s" % fname
else:
return f
def mk_ipolbin(rawP, rawV, rawE, xmin, xmax, CFG):
# TODO finally learn how to use kwargs
order = CFG["ORDER"]
errorder = CFG["ERR_ORDER"]
errmode = CFG["ERR_MODE"]
medfilt = CFG["MEDIAN_FILT"]
if medfilt > 0:
from numpy import median
# TODO figure out what to do with x=0
relErrs = [
rawE[num] / x if x != 0 else 1 for num, x in enumerate(rawV)
]
rem = medfilt * median(relErrs)
P, V, E = [], [], []
for num, x in enumerate(relErrs):
if x < rem:
P.append(rawP[num])
V.append(rawV[num])
E.append(rawE[num])
if CFG["DEBUG"]:
print "%i/%i survive median filter %f times %f" % (
len(P), len(rawP), medfilt, median(relErrs))
else:
P = rawP
V = rawV
E = rawE
import professor2 as prof
pmin = prof.mk_minvals(P)
pmax = prof.mk_maxvals(P)
if order == "auto":
valipol = mk_autoipol(P, V, CFG)
else:
valipol = Ipol(P, V, int(order))
## Check for NaN coeffs
import math
if any([math.isnan(x) for x in valipol.coeffs]):
raise NanError("NaN coefficient encountered in value ipol")
## Build the error interpolation(s)
if not errmode or errmode == "none":
erripols = None
## Build the error interpolation(s)
elif errmode == "mean":
meanerr = sum(E) / float(
len(E)
) #histos[run].bins[binnr].err for run in runs) / float(len(runs))
erripols = Ipol(P, [meanerr], 0) #< const 0th order interpolation
elif errmode == "median":
medianerr = E[len(E) // 2]
erripols = Ipol(P, [medianerr], 0) #< const 0th order interpolation
elif errmode == "symm":
if errorder == "auto":
erripols = mk_autoipol(P, E, CFG)
else:
erripols = Ipol(P, E, int(errorder))
elif errmode == "asymm":
raise Exception("Error interpolation mode 'asymm' not yet supported")
else:
raise Exception("Unknown error interpolation mode '%s'" % errmode)
if erripols is not None:
if any([math.isnan(x) for x in erripols.coeffs]):
raise NanError("NaN coefficient encountered in error ipol")
return IpolBin(xmin, xmax, valipol, erripols), pmin, pmax