def score_article(sid):
"""
main evaluation
ranking function
"""
rank = 1 # initial rank
r = redis.Redis()
corpus_pref = get_preferences()
corpus_filter = get_filtrations()
corpus_story = story_words(sid)
# need a tokenize here TK
# need lemma_builder
pref_lemma = lemma_builder(corpus_pref)
filter_lemma = lemma_builder(corpus_filter)
story_lemma = lemma_builder(corpus_story)
intersect_pref = intersect1d(array(corpus_pref), array(corpus_story))
intersect_filter = intersect1d(array(corpus_filter), array(corpus_story))
intersect_lemma_pref = intersect1d(array(pref_lemma), array(story_lemma))
intersect_lemma_filter = intersect1d(array(filter_lemma), array(story_lemma))
# These may be adjusted with additional factors
filter_score = 2 * len(intersect_pref) + len(intersect_lemma_pref)
prefer_score = -(2 * len(intersect_filter) + len(intersect_lemma_pref))
rank = filter_score + prefer_score
r.zincrby("site:storyroll", "story:%s" % sid, rank) # RANK IT
return rank
def dict_diff(dict1, dict2):
"""Return the difference between two dictionaries as a dictionary of key: [val1, val2] pairs.
Keys unique to either dictionary are included as key: [val1, '-'] or key: ['-', val2]."""
diff_keys = []
common_keys = pylab.intersect1d(dict1.keys(), dict2.keys())
for key in common_keys:
if pylab.iterable(dict1[key]):
if pylab.any(dict1[key] != dict2[key]):
diff_keys.append(key)
else:
if dict1[key] != dict2[key]:
diff_keys.append(key)
dict1_unique = [key for key in dict1.keys() if key not in common_keys]
dict2_unique = [key for key in dict2.keys() if key not in common_keys]
diff = {}
for key in diff_keys:
diff[key] = [dict1[key], dict2[key]]
for key in dict1_unique:
diff[key] = [dict1[key], '-']
for key in dict2_unique:
diff[key] = ['-', dict2[key]]
return diff
def solve(self):
"""
"""
s = "::: solving density :::"
text = colored(s, 'cyan')
print text
firn = self.firn
config = self.config
# newton's iterative method :
solve(self.delta == 0, firn.rho, firn.rhoBc, J=self.J,
solver_parameters=config['enthalpy']['solver_params'])
rhop = firn.rho.vector().array()
# update kc term in drhodt :
# if rho > 550, kc = kcHigh
# if rho <= 550, kc = kcLow
# with parameterizations given by ligtenberg et all 2011
A = firn.rhoi/firn.rhow * 1e3 * firn.adot
rhoCoefNew = ones(firn.n)
rhoHigh = where(rhop > 550)[0]
rhoLow = where(rhop <= 550)[0]
rhoCoefNew[rhoHigh] = firn.kcHh * (2.366 - 0.293*ln(A))
rhoCoefNew[rhoLow] = firn.kcLw * (1.435 - 0.151*ln(A))
firn.assign_variable(firn.rhoCoef, rhoCoefNew)
rhow = firn.rhow
rhoi = firn.rhoi
domega = firn.domega
# update density for water content :
domPos = where(domega > 0)[0] # water content inc.
domNeg = where(domega < 0)[0] # water content dec.
rhoNotLiq = where(rhop < rhow)[0] # density < water
rhoInc = intersect1d(domPos, rhoNotLiq) # where rho can inc.
rhop[rhoInc] = rhop[rhoInc] + domega[rhoInc]*rhow
rhop[domNeg] = rhop[domNeg] + domega[domNeg]*(rhow - rhoi)
#firn.assign_variable(firn.rho, rhop)
firn.print_min_max(firn.rho, 'rho')
def astausgleich(ab2org, mn2org, rhoaorg):
"""shifts the branches of a dc sounding to generate a matching curve."""
ab2 = P.asarray(ab2org)
mn2 = P.asarray(mn2org)
rhoa = P.asarray(rhoaorg)
um = P.unique(mn2)
for i in range(len(um) - 1):
r0, r1 = [], []
ac = P.intersect1d(ab2[mn2 == um[i]], ab2[mn2 == um[i + 1]])
for a in ac:
r0.append(rhoa[(ab2 == a) * (mn2 == um[i])][0])
r1.append(rhoa[(ab2 == a) * (mn2 == um[i + 1])][0])
if len(r0) > 0:
fak = P.mean(P.array(r0) / P.array(r1))
print(fak)
if P.isfinite(fak) and fak > 0.:
rhoa[mn2 == um[i + 1]] *= fak
return rhoa # formerly pg as vector
def astausgleich(ab2org, mn2org, rhoaorg):
"""shifts the branches of a dc sounding to generate a matching curve."""
ab2 = P.asarray(ab2org)
mn2 = P.asarray(mn2org)
rhoa = P.asarray(rhoaorg)
um = P.unique(mn2)
for i in range(len(um) - 1):
r0, r1 = [], []
ac = P.intersect1d(ab2[mn2 == um[i]], ab2[mn2 == um[i + 1]])
for a in ac:
r0.append(rhoa[(ab2 == a) * (mn2 == um[i])][0])
r1.append(rhoa[(ab2 == a) * (mn2 == um[i + 1])][0])
if len(r0) > 0:
fak = P.mean(P.array(r0) / P.array(r1))
print(fak)
if P.isfinite(fak) and fak > 0.:
rhoa[mn2 == um[i + 1]] *= fak
return rhoa # formerly pg as vector
