def answer(flow_id, question_id, answer_id):
flow = graph.flow(flow_id)
question = graph.question(flow_id, question_id)
answer = first([answer for answer in question.answers if answer["id"] == answer_id])
rewards = [rel.end for rel in answer.relationships.outgoing(["Reward"])]
next_question = first([rel.end for rel in answer.relationships.outgoing(["Next"])])
return render_template(
"answer.html", flow=flow, question=question, answer=answer, rewards=rewards, next_question=next_question
)
def main() :
parser = optparse.OptionParser()
parser.add_option("-n", "--histo-name")
parser.add_option("-o", "--output-dir", default='./')
parser.add_option("-s", "--suffix")
(options, args) = parser.parse_args()
histoName = options.histo_name
outputDir = options.output_dir
suffix = options.suffix
inputFileNames = args
print '\n'.join(inputFileNames)
sampleForFilename = dict((f, guessSampleFromFilename(f, samples)) for f in inputFileNames)
assert all(s for f,s in sampleForFilename.iteritems())," cannot identify sample for some inputs %s"%pformat(sampleForFilename)
filenames = collections.defaultdict(list)
for fn, s in sampleForFilename.iteritems() : filenames[s].append(fn)
assert all(len(fnames)==1 for s,fnames in filenames.iteritems()),"expected one file per sample, got %s"%pformat(filenames)
filenames = dict((s, first(fn)) for s, fn in filenames.iteritems())
files = dict((s, r.TFile.Open(fn)) for s, fn in filenames.iteritems())
if histoName :
print "plotting %s"%histoName
canvasname = histoName+("_%s"%suffix if suffix else '')
plotHisto(files, histoName, canvasname, outputDir)
else :
printHistoNames(first(files))
def weightedAverage(histosEff={}, histosWeight={}, histoName='', histoTitle='', verbose=False):
getBinIndices, getBinContents, getBinning = rootUtils.getBinIndices, rootUtils.getBinContents, rootUtils.getBinning
assert sorted(histosEff.keys())==sorted(histosWeight.keys()),"effs and weights must have the same keys:\n\teffs %s\n\tweights %s"%(str(histosEff.keys()), str(histosWeight.keys()))
hout = first(histosEff).Clone(histoName if histoName else 'weighted_average_eff')
hout.SetTitle(histoTitle)
hout.Reset()
def validateWeights(h):
bins, values = getBinIndices(h), getBinContents(h)
allValid = all(v>=0.0 and v<=1.0 for v in values)
if not allValid:
if verbose :
print "warning '%s' weights not in [0,1] : [%s]"%(h.GetName(), ', '.join(("%.3f"%v for v in values)))
print "setting them to 0.0 or 1.0"
for b, v in zip(bins, values) : h.SetBinContent(b, 0.0 if v<0.0 else 1.0 if v>1.0 else v)
return allValid
[validateWeights(hw) for hw in histosWeight.values()]
bins, binning = getBinIndices(first(histosEff)), getBinning(first(histosEff))
for h in histosWeight.values()+histosEff.values():
if getBinning(h)!=binning : print "warning: %s has binning %s, expecting %s"%(h.GetName(), str(getBinning(h)), str(binning))
groups = sorted(histosEff.keys())
epsilon = 1.0e-3
binWeightNormalizations = [sum(histosWeight[g].GetBinContent(b) for g in groups) for b in bins]
weightsAreNormalized = all(abs(1.0-norm)<epsilon for norm in binWeightNormalizations)
if not weightsAreNormalized : print "warning, compositions are not normalized : [%s]"%', '.join(("%.3f"%v for v in binWeightNormalizations))
print '-- ',histoName,'-- '
for g in groups:
bws, bcs = getBinContents(histosWeight[g]), getBinContents(histosEff[g])
print "adding %18s : %s"%(g, ' : '.join("%.4f*%.4f"%(bw, bc) for bw, bc in zip(bws, bcs)))
histoEff, histoWeight = histosEff[g], histosWeight[g]
histoEff.Multiply(histoWeight)
hout.Add(histoEff)
print "tot weight : %s"%' '.join(("%.4f"%v for v in (sum(histosWeight[g].GetBinContent(b) for g in groups) for b in bins)))
print "weighted avg : %s"%' '.join(("%.4f"%v for v in getBinContents(hout)))
return hout
def buildWeightedHisto(histos={}, fractions={}, histoName='', histoTitle='') :
"was getFinalRate"
hout = first(histos).Clone(histoName if histoName else 'final_rate') # should pick a better default
hout.SetTitle(histoTitle)
hout.Reset()
flatFraction = type(first(fractions)) is float
if flatFraction :
print "averaging flat ",histoName
print 'keys -> ',histos.keys()
for b in getBinIndices(hout) :
tot, err2 = binWeightedSum(histos, fractions, b)
hout.SetBinContent(b, tot)
hout.SetBinError(b, sqrt(err2))
else :
bH, bF = getBinning(first(histos)), getBinning(first(fractions))
assert bH == bF,"different binning %s: %s, %s: %s"%(first(histos).GetName(), bH, first(fractions).GetName(), bF)
weightedHistos = dict((p, h.Clone(h.GetName()+'_weighted_for_'+histoName)) for p,h in histos.iteritems()) # preserve originals
print "averaging 2d ",histoName
for b in getBinIndices(hout):
print "bin %d (w=%.1f): %.3f = %s"%(b,
sum(fractions[p].GetBinContent(b) for p in fractions.keys()),
sum(fractions[p].GetBinContent(b)*weightedHistos[p].GetBinContent(b) for p in fractions.keys()),
'+'.join("%.2f*%.2f"%(fractions[p].GetBinContent(b), weightedHistos[p].GetBinContent(b))
for p in fractions.keys()))
for p,h in weightedHistos.iteritems() :
h.Multiply(fractions[p])
hout.Add(h)
return hout
def plotStackedHistos(histosFlavorSlice={}, canvasName='', outputDir='./', frameTitle='stack', colors={}) :
"Plot the input histos used to compute the fractions"
histos = histosFlavorSlice
can = r.TCanvas(canvasName, canvasName, 800, 600)
can.cd()
stack = r.THStack('stack_'+canvasName, '')
leg = topRightLegend(can, 0.275, 0.475, shift=-0.025)
leg.SetBorderSize(0)
colors = SampleUtils.colors if not colors else colors
procs = sorted(histos.keys())
for p in procs:
h = histos[p]
h.SetFillColor(colors[p])
h.SetLineColor(h.GetFillColor())
h.SetDrawOption('bar')
stack.Add(h)
for s in procs[::-1] : leg.AddEntry(histos[s], s, 'F') # stack goes b-t, legend goes t-b
stack.Draw('hist e')
leg.Draw('same')
tex = r.TLatex()
tex.SetNDC(True)
tex.SetTextFont(first(histos).GetTitleFont())
tex.SetTextSize(first(histos).GetTitleSize())
tex.DrawLatex(0.1, 0.925, frameTitle.split(';')[0])
can.Update() # force stack to create canMaster
canMaster = stack.GetHistogram()
canMaster.SetTitle(frameTitle)
canMaster.Draw('axis same')
can._graphical_objects = [stack, canMaster, leg, tex] + [h for h in stack.GetStack()]
can.Update()
for ext in ['png','eps'] :
outFilename = outputDir+'/'+canvasName+'.'+ext
rmIfExists(outFilename)
can.SaveAs(outFilename)
def latest_obs_and_forecast(site_id):
result = memcache.get(site_id, "site_latest")
if result:
return result
site = Site.get_by_key_name(site_id)
if site is None:
return None
obs = ObservationTimestep.find_latest_by_site(site, limit=6)
result = None
if len(obs) > 0:
forecasts = ForecastTimestep.find_by_site_closest_by_date(site, first(obs).observation_datetime,
limit=50)
closest_forecast = first(forecasts)
if closest_forecast:
matching_obs = first(filter(lambda o: o.observation_datetime == closest_forecast.forecast_datetime, obs))
matching_forecasts = ifilter(lambda f: f.forecast_datetime == closest_forecast.forecast_datetime, forecasts)
if matching_obs:
#finally have both... a single obs report and multiple forecasts
obs_dict = to_dict_excl_sites(matching_obs)
obs_dict['best_forecast'] = map(to_dict_excl_sites, make_five_day_list(matching_forecasts))
result = {
'site': site.to_dict(),
'observation': obs_dict
}
memcache.set(site_id, result, 60 * 60, namespace='site_latest')
return result
def plotFractions(fractDict={}, outplotdir='./', prefix='') :
"""
input : fractDict[sr][lep_type][sample] = float
"""
outplotdir = outplotdir if outplotdir.endswith('/') else outplotdir+'/'
def isInterestingRegion(r) : return any(k in r for k in ['CR8', 'WHSS', 'SSInc', 'SsEwk'])
regions = [r for r in selectionRegions() if isInterestingRegion(r)]
leptypes = sorted(first(fractDict).keys())
samples = sorted(first(first(fractDict)).keys())
ind = np.arange(len(regions))
width = 0.5
colors = dict(zip(samples, ['b','g','r','c','m','y']))
for lt in leptypes :
fracPerSample = dict((s, np.array([fractDict[r][lt][s] for r in regions])) for s in samples)
below = np.zeros(len(regions))
plots = []
fig, ax = plt.subplots()
for s, frac in fracPerSample.iteritems() :
plots.append(plt.bar(ind, frac, width, color=colors[s], bottom=below))
below = below + frac
plt.ylabel('fractions')
plt.title(prefix+' '+lt+' compositions')
plt.xticks(ind+width/2., regions)
plt.ylim((0.0, 1.0))
plt.grid(True)
plt.yticks(np.arange(0.0, 1.0, 0.2))
labels = {'heavyflavor' : 'bb/cc', 'diboson' : 'VV', 'ttbar':'tt'}
labels = [labels[s] if s in labels else s for s in samples]
leg = plt.legend([p[0] for p in plots], labels, bbox_to_anchor=(1.135, 1.05))
leg.get_frame().set_alpha(0.5)
fig.autofmt_xdate(bottom=0.25, rotation=90, ha='center')
fig.savefig(outplotdir+prefix+'_'+lt+'.png')
def fetchSfHistos(inputSfFiles=[], lepton='', verbose=False):
from compute_fake_el_scale_factor import histoname_sf_vs_eta
fileNames = inputSfFiles
histos = dict()
print "fetchSfHistos: fileNames ",fileNames
if not (type(fileNames)==list and
len(fileNames) in [1, 2]):
print "fetchSfHistos expects one or two files (hflf+conv), got %s"%str(inputSfFiles)
print "returning ",histos
return histos
if verbose : print "retrieving scale factors from %s"%inputSfFiles
fname_hflf = first(filter(lambda _ : 'hflf' in _, fileNames))
fname_conv = first(filter(lambda _ : 'conv' in _, fileNames))
file_hflf = r.TFile.Open(fname_hflf) if fname_hflf else None
file_conv = r.TFile.Open(fname_conv) if fname_conv else None
hname = histoname_sf_vs_eta(lepton)
histo_hflf = file_hflf.Get(hname) if file_hflf else None
histo_conv = file_conv.Get(hname) if file_conv else None
if histo_hflf : histos['hflf'] = composeEtaHistosAs2dPtEta(input1Dhisto=histo_hflf,
outhistoname=hname+'_hflf')
if histo_conv : histos['conv'] = composeEtaHistosAs2dPtEta(input1Dhisto=histo_conv,
outhistoname=hname+'_conv')
for f in [file_hflf, file_conv] :
if f : f.Close()
return histos
def done(self, reels, display, line):
S = [r.symbol() for r in reels]
won = bool(len(set(S)) == 1)
amount = symbols[first(S)] if won else 0
if won and display:
print(winmsg % symbols[first(S)])
return line, amount
def frac2str(frac) :
flatFraction = type(first(frac)) is float
return ('\n'.join([''.join("%12s"%s for s in fakeProcesses()),
''.join("%12s"%("%.3f"%frac[s]) for s in fakeProcesses())])
if flatFraction
else '\n'.join([''.join("%12s"%s for s in fakeProcesses())]
+[''.join("%12s"%("%.3f"%frac[s].GetBinContent(b)) for s in fakeProcesses())
for b in getBinIndices(first(frac))]))
def plotHistos(bkgHistos, sigHistos, plotdir) :
llnjs = first (sigHistos).keys()
vars = first(first(sigHistos)).keys()
for llnj in llnjs :
for var in vars :
plotVar(dict((s, bkgHistos[s][llnj][var]) for s in bkgHistos.keys()),
dict((s, sigHistos[s][llnj][var]) for s in sigHistos.keys()),
llnj+'_'+var, plotdir)
def plotPerSourceEff(histosPerVar={}, outputDir='', lepton='', region='', sample='', verbose=False, zoomIn=True):
"plot efficiency for each source (and 'anysource') as a function of each var; expect histos[var][source][loose,tight]"
variables = histosPerVar.keys()
sources = [s for s in first(histosPerVar).keys() if s!='real'] # only fake eff really need a scale factor
colors = colorsLineSources
mkdirIfNeeded(outputDir)
for var in filter(lambda x : x in ['pt1', 'eta1'], histosPerVar.keys()):
histosPerSource = dict((s, histosPerVar[var][s]) for s in sources)
canvasBasename = region+'_efficiency_'+lepton+'_'+var+("_%s"%sample if sample else '')
missingSources = [s for s, h in histosPerSource.iteritems() if not h['loose'] or not h['tight']]
if missingSources:
if verbose : print "skip %s, missing histos for %s"%(var, str(missingSources))
continue
anySourceLoose = summedHisto([h['loose'] for h in histosPerSource.values()])
anySourceTight = summedHisto([h['tight'] for h in histosPerSource.values()])
anySourceLoose.SetName(histoNamePerSource(var, 'any', 'loose', region))
anySourceTight.SetName(histoNamePerSource(var, 'any', 'tight', region))
histosPerSource['any'] = { 'loose' : anySourceLoose, 'tight' : anySourceTight }
emptyBkg = anySourceLoose.Integral()==0 or anySourceTight.Integral()==0
if emptyBkg:
if verbose : print "empty backgrounds, skip %s"%canvasBasename
continue
def computeEfficiencies(histosPerSource={}) :
sources = histosPerSource.keys()
num = dict((s, histosPerSource[s]['tight']) for s in sources)
den = dict((s, histosPerSource[s]['loose']) for s in sources)
eff = dict((s, h.Clone(h.GetName().replace('tight', 'tight_over_loose')))
for s, h in num.iteritems())
[eff[s].Divide(den[s]) for s in sources]
return eff
effs = computeEfficiencies(histosPerSource)
can = r.TCanvas('c_'+canvasBasename, canvasBasename, 800, 600)
can.cd()
pm = first(effs) # pad master
pm.SetStats(False)
pm.Draw('axis')
can.Update()
for s, h in effs.iteritems() :
h.SetMarkerColor(colors[s] if s in colors else r.kBlack)
h.SetLineColor(h.GetMarkerColor())
h.SetLineWidth(2*h.GetLineWidth())
h.SetMarkerStyle(markersSources[s] if s in markersSources else r.kDot)
h.Draw('ep same')
h.SetDirectory(0)
#pprint.pprint(effs)
yMin, yMax = getMinMax(effs.values())
pm.SetMinimum(0.0)
pm.SetMaximum(0.25 if yMax < 0.5 and zoomIn else 1.1)
can.Update()
topRightLabel(can, canvasBasename, xpos=0.125, align=13)
drawLegendWithDictKeys(can, effs, opt='lp')
can.RedrawAxis()
can._histos = effs
can.Update()
outFname = os.path.join(outputDir, canvasBasename+'.png')
utils.rmIfExists(outFname)
can.SaveAs(outFname)
def plotStackedHistos(histosPerGroup={}, outputDir='', region='', verbose=False):
groups = histosPerGroup.keys()
variables = first(histosPerGroup).keys()
leptonTypes = first(first(histosPerGroup)).keys()
colors = getGroupColor()
mkdirIfNeeded(outputDir)
histosPerName = dict([(region+'_'+var+'_'+lt, # one canvas for each histo, so key with histoname w/out group
dict([(g, histosPerGroup[g][var][lt]) for g in groups]))
for var in variables for lt in leptonTypes])
for histoname, histosPerGroup in histosPerName.iteritems():
missingGroups = [g for g, h in histosPerGroup.iteritems() if not h]
if missingGroups:
if verbose : print "skip %s, missing histos for %s"%(histoname, str(missingGroups))
continue
bkgHistos = dict([(g, h) for g, h in histosPerGroup.iteritems() if g not in ['data', 'signal']])
totBkg = summedHisto(bkgHistos.values())
err_band = None # buildErrBandGraph(totBkg, computeStatErr2(totBkg))
emptyBkg = totBkg.Integral()==0
if emptyBkg:
if verbose : print "empty backgrounds, skip %s"%histoname
continue
can = r.TCanvas('c_'+histoname, histoname, 800, 600)
can.cd()
pm = totBkg # pad master
pm.SetStats(False)
pm.Draw('axis')
can.Update() # necessary to fool root's dumb object ownership
stack = r.THStack('stack_'+histoname,'')
can.Update()
r.SetOwnership(stack, False)
for s, h in bkgHistos.iteritems() :
h.SetFillColor(colors[s] if s in colors else r.kOrange)
h.SetDrawOption('bar')
h.SetDirectory(0)
stack.Add(h)
stack.Draw('hist same')
# err_band.Draw('E2 same')
data = histosPerGroup['data']
if data and data.GetEntries():
data.SetMarkerStyle(r.kFullDotLarge)
data.Draw('p same')
# yMin, yMax = getMinMax([h for h in [totBkg, data, err_band] if h]) # fixme with err_band
yMin, yMax = 0.0, data.GetMaximum()
pm.SetMinimum(0.0)
pm.SetMaximum(1.1*yMax)
can.Update()
topRightLabel(can, histoname, xpos=0.125, align=13)
# drawLegendWithDictKeys(can, dictSum(bkgHistos, {'stat err':err_band}), opt='f')
drawLegendWithDictKeys(can, bkgHistos, opt='f')
can.RedrawAxis()
can._stack = stack
can._histos = [h for h in stack.GetHists()]+[data]
can.Update()
outFname = os.path.join(outputDir, histoname+'.png')
utils.rmIfExists(outFname)
can.SaveAs(outFname)
def comp_sum(supposition_results, ar, rcdl, set_totaller, **kwargs):
if isinstance(ar, str):
add_remove_names = ('added', 'removed')
first_drill_down = first(lambda el: el[0] == ar[0], add_remove_names)
ar = lambda sr: getattr(sr, first_drill_down)
if isinstance(rcdl, str):
sets = ('reachability_set', 'coverage_set', 'dissimilarity_set', 'liability_set')
set_names = [first(lambda el: el[0] == letter, sets) for letter in rcdl]
rcdl = lambda profile: [getattr(profile, sn) for sn in set_names]
assert isinstance(supposition_results, SuppositionResults)
return sum(sum((set_totaller(_set, source, **kwargs) for _set in rcdl(ar(change))))
for (source, change) in supposition_results.iteritems())
def upstreamCommit(self):
"""The most recent commit this branch shares with its upstream.
`git log` and `git reflog` are used to detect rebases on the upstream
branch, in similar fashion to `git pull`.
"""
if self.upstream is None:
return None
commitHashes = set(c.hash for c in self.allCommits)
firstUpstreamReference = first(h.hash for h in self.upstream._refLog if h.hash in commitHashes)
upstreamCommitHashes = set(c.hash for c in self.upstream.allCommits)
return first(c for c in self.allCommits if c.hash in upstreamCommitHashes or c.hash == firstUpstreamReference)
def svn_info(path):
# get tag or last rev
cmd = 'svn info '+path
out = getCommandOutput(cmd)
url_origin = first([l for l in out['stdout'].splitlines() if 'URL: ' in l])
url_origin = url_origin.replace('/', ' ').split() if url_origin else []
tag = url_origin[url_origin.index('tags')+1] if 'tags' in url_origin else None
last_rev = first(first([l for l in out['stdout'].splitlines() if 'Last Changed Rev:' in l]).split()[::-1])
# get list of modified files
cmd = 'svn status '+path
out = getCommandOutput(cmd)
modified_files = [l for l in out['stdout'].splitlines() if l.startswith('M ')]
return "{0} {1}".format(tag if tag else last_rev,
('\n'.join(['']+modified_files) if modified_files else ''))
def compose2Dcompositions(inputSfFile=None, templateHistoName="%(proc)s_%(etabin)s", processes=[]) :
"take two 1D fractions histograms for one eta slice each, and compose them in a 2D fractions histogram"
etaBins = ['etaC', 'etaF']
histos1d = dict((e, dict((p, inputSfFile.Get(templateHistoName%{'proc':p, 'etabin':e})) for p in processes)) for e in etaBins)
assert all(v for ve in histos1d.values() for v in ve.values()),"missing compositions: %s"%histos1d
h1dC, h1dF = first(histos1d['etaC']), first(histos1d['etaF'])
nX, xMin, xMax = h1dC.GetNbinsX(), h1dC.GetXaxis().GetBinLowEdge(1), h1dC.GetXaxis().GetBinUpEdge(h1dC.GetNbinsX())
histos2d = dict((p, r.TH2F(histos1d['etaC'][p].GetName().replace('_etaC_','_vs_eta_'), '', nX, xMin, xMax, 2, 0.0, 2.0)) for p in histos1d['etaC'].keys())
for p in processes :
for iEta, eta in zip(range(1, 1+len(etaBins)), etaBins) :
hEta, hEtaPt = histos1d[eta][p], histos2d[p]
for iPt in range(1, 1+nX) :
hEtaPt.SetBinContent(iPt, iEta, hEta.GetBinContent(iPt))
hEtaPt.SetBinError (iPt, iEta, hEta.GetBinError (iPt))
return histos2d
def ai_move(self, player):
"""Randomly choose between returning the move closest to completing a tile or a random move."""
tiles = [t for t in self if self.valid_move(player, t)]
def to_max(t): return t.maxnum - t.num
tiles.sort(key=to_max)
return rndchoice( [first(tiles), rndchoice(tiles)] )
def closest(self, lst1, lst2):
dist_list = []
for r in lst1:
for r2 in lst2:
dist_list.append( (self.dist(r,r2), r, r2) )
close = first(sorted(dist_list))
return close[1], close[2]
def add(self, vehicle: Vehicle):
try:
spot = first(spot for spot in self._parking_spots
if spot.empty_space >= vehicle.size)
except ValueError:
raise OutOfSpaceError('No empty parking spot')
spot.vehicles.append(vehicle)
def get_book_markdown(item_id):
items = item_index["id"].get(item_id)
if items is None:
flask.abort(
http.client.NOT_FOUND,
"Item with id {item_id} was not found".format(item_id=item_id)
)
item = utils.first(items)
transcription = item.get("transcription")
if transcription is None:
flask.abort(
http.client.NOT_FOUND,
"Trascription for item {item_id} is not available".format(
item_id=item_id
)
)
markdown_file = os.path.join(
config.parser.markdown_dir,
transcription
)
return flask.render_template(
"markdown.html",
markdown_data=markdown_cache.get(markdown_file),
item=item
)
def run(self):
inp = TextInput()
mfiles = [fn for fn in os.listdir(tut_dir) if not fn.startswith('.')]
choices = [ (first(f.split('.')), f) for f in mfiles ]
while True:
Tutorial(inp.menu(choices)).play()
def __init__(self, fn, tpl):
self.commands = []
self.name = first(fn.split('.'))
self.tpl = tpl
with open(pjoin(tut_dir, fn), encoding="utf-8") as fp:
self.sections = re.split(cmdpat, fp.read())
def check_end(self):
pchars = set(sf.char for sf in stars+fleets if sf.char != neutral_char)
if len(pchars) == 1:
board.draw()
print(nl, self.winmsg % first(pchars))
sys.exit()
def test_messages_match(self):
"""
Tests if all the localizations have the same order
translation keys
"""
has_mismatches = False
messages = collections.defaultdict(list)
for locale, catalog in self.catalogs.items():
for message in catalog:
messages[locale].append(message.id)
first_locale = utils.first(messages.keys())
first_message_list = messages.pop(first_locale)
for locale, message_list in messages.items():
self.assertEqual(
len(first_message_list),
len(message_list)
)
self.assertEqual(
set(first_message_list),
set(message_list)
)
for index, message in enumerate(message_list):
if message != first_message_list[index]:
logging.debug("Mismatch at position {index}. {first_locale} has {first_message}, {locale} has {message}".format(
index=index,
first_locale=first_locale,
first_message=first_message_list[index],
locale=locale,
message=message
))
has_mismatches = True
self.assertFalse(has_mismatches)
def get_book_markdown(book_id, index):
items = item_index["id"].get(book_id, None)
if items is None:
flask.abort(http.client.NOT_FOUND, "Book with id {id} was not found".format(id=id))
item = utils.first(items)
index -= 1
transcription_url = item.get("transcription_url")
transcription_filename = item.get("transcription_filename")
if (
(transcription_url is None) or
(transcription_filename is None) or
(index < 0) or
(index > len(transcription_url)) or
(index > len(transcription_filename))
):
flask.abort(
http.client.NOT_FOUND,
"Markdowned trascription for book with id {id} is not available".format(
id=book_id
)
)
markdown_file = os.path.join(
config.parser.markdown_dir,
transcription_filename[index]
)
return flask.render_template(
"markdown.html",
markdown_data=markdown_cache.get(markdown_file),
item=item
)
def distribute_and_over_or(s):
"""Given a sentence s consisting of conjunctions and disjunctions
of literals, return an equivalent sentence in CNF.
>>> distribute_and_over_or((A & B) | C)
((A | C) & (B | C))
"""
s = expr(s)
if s.op == '|':
s = associate('|', s.args)
if s.op != '|':
return distribute_and_over_or(s)
if len(s.args) == 0:
return False
if len(s.args) == 1:
return distribute_and_over_or(s.args[0])
conj = first(arg for arg in s.args if arg.op == '&')
if not conj:
return s
others = [a for a in s.args if a is not conj]
rest = associate('|', others)
return associate('&', [distribute_and_over_or(c | rest)
for c in conj.args])
elif s.op == '&':
return associate('&', list(map(distribute_and_over_or, s.args)))
else:
return s
def get_plugin(name, config=None):
plugin = utils.first(x for x in PluginBase.plugins if x.get_name() == name)
if plugin == None:
print PluginBase.plugins
print "Not found"
return None
return plugin(config)
def normalizeHistos(histos) :
"Normalize the input histos so that in each bin the sum of the different processes amounts to 1.0"
basename = guessBaseHistoname([h.GetName() for h in histos.values() if h])
tot = first(histos).Clone(basename+'_tot')
tot.Reset()
for h in histos.values() : tot.Add(h)
for b in getBinIndices(tot) : tot.SetBinError(b, 0.0) # norm is a constraint, without error
for h in histos.values() : h.Divide(tot)
def get_random_move(self):
"""Return location of best move."""
def by_corner_score(loc):
return board.is_corner(loc), -len(board.get_captured(self, loc))
moves = board.get_valid_moves(self)
shuffle(moves)
return first(sorted(moves, key=by_corner_score))
def test_first__empty() -> None:
assert first([], "empty") == "empty"
def first_unassigned_variable(assignment, csp):
"The default variable order."
return first([var for var in csp.variables if var not in assignment])
def plotStackedHistos(histosPerGroup={},
outputDir='',
region='',
verbose=False):
groups = histosPerGroup.keys()
variables = first(histosPerGroup).keys()
leptonTypes = first(first(histosPerGroup)).keys()
colors = getGroupColor()
mkdirIfNeeded(outputDir)
histosPerName = dict([
(
region + '_' + var + '_' +
lt, # one canvas for each histo, so key with histoname w/out group
dict([(g, histosPerGroup[g][var][lt]) for g in groups]))
for var in variables for lt in leptonTypes
])
for histoname, histosPerGroup in histosPerName.iteritems():
missingGroups = [g for g, h in histosPerGroup.iteritems() if not h]
if missingGroups:
if verbose:
print "skip %s, missing histos for %s" % (histoname,
str(missingGroups))
continue
bkgHistos = dict([(g, h) for g, h in histosPerGroup.iteritems()
if g not in ['data', 'signal']])
totBkg = summedHisto(bkgHistos.values())
err_band = None # buildErrBandGraph(totBkg, computeStatErr2(totBkg))
emptyBkg = totBkg.Integral() == 0
if emptyBkg:
if verbose: print "empty backgrounds, skip %s" % histoname
continue
can = r.TCanvas('c_' + histoname, histoname, 800, 600)
can.cd()
pm = totBkg # pad master
pm.SetStats(False)
pm.Draw('axis')
can.Update() # necessary to fool root's dumb object ownership
stack = r.THStack('stack_' + histoname, '')
can.Update()
r.SetOwnership(stack, False)
for s, h in bkgHistos.iteritems():
h.SetFillColor(colors[s] if s in colors else r.kOrange)
h.SetDrawOption('bar')
h.SetDirectory(0)
stack.Add(h)
stack.Draw('hist same')
# err_band.Draw('E2 same')
data = histosPerGroup['data']
if data and data.GetEntries():
data.SetMarkerStyle(r.kFullDotLarge)
data.Draw('p same')
# yMin, yMax = getMinMax([h for h in [totBkg, data, err_band] if h]) # fixme with err_band
yMin, yMax = 0.0, data.GetMaximum()
pm.SetMinimum(0.0)
pm.SetMaximum(1.1 * yMax)
can.Update()
topRightLabel(can, histoname, xpos=0.125, align=13)
# drawLegendWithDictKeys(can, dictSum(bkgHistos, {'stat err':err_band}), opt='f')
drawLegendWithDictKeys(can, bkgHistos, opt='f')
can.RedrawAxis()
can._stack = stack
can._histos = [h for h in stack.GetHists()] + [data]
can.Update()
outFname = os.path.join(outputDir, histoname + '.png')
utils.rmIfExists(outFname)
can.SaveAs(outFname)
def test_first() -> None:
assert first([1, 2, 3], None) == 1
