[LNT] r371946 - [LNT] Python 3 support: Replace `raise E, V` with `raise E(V)`
Hubert Tong via llvm-commits
llvm-commits at lists.llvm.org
Sun Sep 15 16:47:08 PDT 2019
Author: hubert.reinterpretcast
Date: Sun Sep 15 16:47:08 2019
New Revision: 371946
URL: http://llvm.org/viewvc/llvm-project?rev=371946&view=rev
Log:
[LNT] Python 3 support: Replace `raise E, V` with `raise E(V)`
Summary:
Mechanically changed `raise E, V` to `raise E(V)`. Split out from
D67535.
This patch covers the files found to be affected when running tests
(without result submission).
Reviewers: cmatthews, thopre, kristof.beyls
Reviewed By: thopre
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D67581
Modified:
lnt/trunk/lnt/external/stats/stats.py
Modified: lnt/trunk/lnt/external/stats/stats.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/external/stats/stats.py?rev=371946&r1=371945&r2=371946&view=diff
==============================================================================
--- lnt/trunk/lnt/external/stats/stats.py (original)
+++ lnt/trunk/lnt/external/stats/stats.py Sun Sep 15 16:47:08 2019
@@ -247,12 +247,12 @@ print stats.amean.__doc__ or whatever.
for func, types in tuples:
for t in types:
if t in self._dispatch:
- raise ValueError, "can't have two dispatches on "+str(t)
+ raise ValueError("can't have two dispatches on " + str(t))
self._dispatch[t] = func
def __call__(self, arg1, *args, **kw):
if type(arg1) not in self._dispatch:
- raise TypeError, "don't know how to dispatch %s arguments" % type(arg1)
+ raise TypeError("don't know how to dispatch %s arguments" % type(arg1))
return apply(self._dispatch[type(arg1)], (arg1,) + args, kw)
@@ -599,7 +599,7 @@ Returns: transformed data for use in an
if v[j] - mean(nargs[j]) > TINY:
check = 0
if check != 1:
- raise ValueError, 'Problem in obrientransform.'
+ raise ValueError('Problem in obrientransform.')
else:
return nargs
@@ -842,7 +842,7 @@ Returns: Pearson's r value, two-tailed p
"""
TINY = 1.0e-30
if len(x) != len(y):
- raise ValueError, 'Input values not paired in pearsonr. Aborting.'
+ raise ValueError('Input values not paired in pearsonr. Aborting.')
n = len(x)
x = map(float,x)
y = map(float,y)
@@ -881,7 +881,7 @@ Returns: Spearman's r, two-tailed p-valu
"""
TINY = 1e-30
if len(x) != len(y):
- raise ValueError, 'Input values not paired in spearmanr. Aborting.'
+ raise ValueError('Input values not paired in spearmanr. Aborting.')
n = len(x)
rankx = rankdata(x)
ranky = rankdata(y)
@@ -906,11 +906,11 @@ Returns: Point-biserial r, two-tailed p-
"""
TINY = 1e-30
if len(x) != len(y):
- raise ValueError, 'INPUT VALUES NOT PAIRED IN pointbiserialr. ABORTING.'
+ raise ValueError('INPUT VALUES NOT PAIRED IN pointbiserialr. ABORTING.')
data = pstat.abut(x,y)
categories = pstat.unique(x)
if len(categories) != 2:
- raise ValueError, "Exactly 2 categories required for pointbiserialr()."
+ raise ValueError("Exactly 2 categories required for pointbiserialr().")
else: # there are 2 categories, continue
codemap = pstat.abut(categories,range(2))
recoded = pstat.recode(data,codemap,0)
@@ -971,7 +971,7 @@ Returns: slope, intercept, r, two-tailed
"""
TINY = 1.0e-20
if len(x) != len(y):
- raise ValueError, 'Input values not paired in linregress. Aborting.'
+ raise ValueError('Input values not paired in linregress. Aborting.')
n = len(x)
x = map(float,x)
y = map(float,y)
@@ -1064,7 +1064,7 @@ Usage: lttest_rel(a,b,printit=0,name1=
Returns: t-value, two-tailed prob
"""
if len(a) != len(b):
- raise ValueError, 'Unequal length lists in ttest_rel.'
+ raise ValueError('Unequal length lists in ttest_rel.')
x1 = mean(a)
x2 = mean(b)
v1 = var(a)
@@ -1168,7 +1168,7 @@ Returns: u-statistic, one-tailed p-value
smallu = min(u1,u2)
T = math.sqrt(tiecorrect(ranked)) # correction factor for tied scores
if T == 0:
- raise ValueError, 'All numbers are identical in lmannwhitneyu'
+ raise ValueError('All numbers are identical in lmannwhitneyu')
sd = math.sqrt(T*n1*n2*(n1+n2+1)/12.0)
z = abs((bigu-n1*n2/2.0) / sd) # normal approximation for prob calc
return smallu, 1.0 - zprob(z)
@@ -1230,7 +1230,7 @@ Usage: lwilcoxont(x,y)
Returns: a t-statistic, two-tail probability estimate
"""
if len(x) != len(y):
- raise ValueError, 'Unequal N in wilcoxont. Aborting.'
+ raise ValueError('Unequal N in wilcoxont. Aborting.')
d=[]
for i in range(len(x)):
diff = x[i] - y[i]
@@ -1284,7 +1284,7 @@ Returns: H-statistic (corrected for ties
h = 12.0 / (totaln*(totaln+1)) * ssbn - 3*(totaln+1)
df = len(args) - 1
if T == 0:
- raise ValueError, 'All numbers are identical in lkruskalwallish'
+ raise ValueError('All numbers are identical in lkruskalwallish')
h = h / float(T)
return h, chisqprob(h,df)
@@ -1303,7 +1303,7 @@ Returns: chi-square statistic, associate
"""
k = len(args)
if k < 3:
- raise ValueError, 'Less than 3 levels. Friedman test not appropriate.'
+ raise ValueError('Less than 3 levels. Friedman test not appropriate.')
n = len(args[0])
data = apply(pstat.abut,tuple(args))
for i in range(len(data)):
@@ -1538,7 +1538,7 @@ using the betacf function. (Adapted fro
Usage: lbetai(a,b,x)
"""
if (x<0.0 or x>1.0):
- raise ValueError, 'Bad x in lbetai'
+ raise ValueError('Bad x in lbetai')
if (x==0.0 or x==1.0):
bt = 0.0
else:
@@ -1712,7 +1712,7 @@ length lists.
Usage: lsummult(list1,list2)
"""
if len(list1) != len(list2):
- raise ValueError, "Lists not equal length in summult."
+ raise ValueError("Lists not equal length in summult.")
s = 0
for item1,item2 in pstat.abut(list1,list2):
s = s + item1*item2
@@ -2235,7 +2235,7 @@ Usage: atmean(a,limits=None,inclusive=
if inclusive[1]: upperfcn = N.less_equal
else: upperfcn = N.less
if limits[0] > N.maximum.reduce(N.ravel(a)) or limits[1] < N.minimum.reduce(N.ravel(a)):
- raise ValueError, "No array values within given limits (atmean)."
+ raise ValueError("No array values within given limits (atmean).")
elif limits[0] is None and limits[1] is not None:
mask = upperfcn(a,limits[1])
elif limits[0] is not None and limits[1] is None:
@@ -2267,7 +2267,7 @@ Usage: atvar(a,limits=None,inclusive=(
if inclusive[1]: upperfcn = N.less_equal
else: upperfcn = N.less
if limits[0] > N.maximum.reduce(N.ravel(a)) or limits[1] < N.minimum.reduce(N.ravel(a)):
- raise ValueError, "No array values within given limits (atvar)."
+ raise ValueError("No array values within given limits (atvar).")
elif limits[0] is None and limits[1] is not None:
mask = upperfcn(a,limits[1])
elif limits[0] is not None and limits[1] is None:
@@ -2353,7 +2353,7 @@ Usage: atsem(a,limits=None,inclusive=(
if inclusive[1]: upperfcn = N.less_equal
else: upperfcn = N.less
if limits[0] > N.maximum.reduce(N.ravel(a)) or limits[1] < N.minimum.reduce(N.ravel(a)):
- raise ValueError, "No array values within given limits (atsem)."
+ raise ValueError("No array values within given limits (atsem).")
elif limits[0] is None and limits[1] is not None:
mask = upperfcn(a,limits[1])
elif limits[0] is not None and limits[1] is None:
@@ -2698,7 +2698,7 @@ Returns: transformed data for use in an
if v[j] - mean(nargs[j]) > TINY:
check = 0
if check != 1:
- raise ValueError, 'Lack of convergence in obrientransform.'
+ raise ValueError('Lack of convergence in obrientransform.')
else:
return N.array(nargs)
@@ -2973,7 +2973,7 @@ Usage: acovariance(X)
Returns: covariance matrix of X
"""
if len(X.shape) != 2:
- raise TypeError, "acovariance requires 2D matrices"
+ raise TypeError("acovariance requires 2D matrices")
n = X.shape[0]
mX = amean(X,0)
return N.dot(N.transpose(X),X) / float(n) - N.multiply.outer(mX,mX)
@@ -3170,7 +3170,7 @@ Returns: Point-biserial r, two-tailed p-
categories = pstat.aunique(x)
data = pstat.aabut(x,y)
if len(categories) != 2:
- raise ValueError, "Exactly 2 categories required (in x) for pointbiserialr()."
+ raise ValueError("Exactly 2 categories required (in x) for pointbiserialr().")
else: # there are 2 categories, continue
codemap = pstat.aabut(categories,N.arange(2))
recoded = pstat.arecode(data,codemap,0)
@@ -3437,7 +3437,7 @@ Returns: t-value, two-tailed p-value
b = N.ravel(b)
dimension = 0
if len(a) != len(b):
- raise ValueError, 'Unequal length arrays.'
+ raise ValueError('Unequal length arrays.')
x1 = amean(a,dimension)
x2 = amean(b,dimension)
v1 = avar(a,dimension)
@@ -3550,7 +3550,7 @@ Returns: u-statistic, one-tailed p-value
smallu = min(u1,u2)
T = math.sqrt(tiecorrect(ranked)) # correction factor for tied scores
if T == 0:
- raise ValueError, 'All numbers are identical in amannwhitneyu'
+ raise ValueError('All numbers are identical in amannwhitneyu')
sd = math.sqrt(T*n1*n2*(n1+n2+1)/12.0)
z = abs((bigu-n1*n2/2.0) / sd) # normal approximation for prob calc
return smallu, 1.0 - azprob(z)
@@ -3612,7 +3612,7 @@ Usage: awilcoxont(x,y) where x,y a
Returns: t-statistic, two-tailed p-value
"""
if len(x) != len(y):
- raise ValueError, 'Unequal N in awilcoxont. Aborting.'
+ raise ValueError('Unequal N in awilcoxont. Aborting.')
d = x-y
d = N.compress(N.not_equal(d,0),d) # Keep all non-zero differences
count = len(d)
@@ -3665,7 +3665,7 @@ Returns: H-statistic (corrected for ties
h = 12.0 / (totaln*(totaln+1)) * ssbn - 3*(totaln+1)
df = len(args) - 1
if T == 0:
- raise ValueError, 'All numbers are identical in akruskalwallish'
+ raise ValueError('All numbers are identical in akruskalwallish')
h = h / float(T)
return h, chisqprob(h,df)
@@ -3684,7 +3684,7 @@ Returns: chi-square statistic, associate
"""
k = len(args)
if k < 3:
- raise ValueError, '\nLess than 3 levels. Friedman test not appropriate.\n'
+ raise ValueError('\nLess than 3 levels. Friedman test not appropriate.\n')
n = len(args[0])
data = apply(pstat.aabut,args)
data = data.astype(N.float_)
@@ -3979,7 +3979,7 @@ Usage: abetai(a,b,x,verbose=1)
TINY = 1e-15
if isinstance(a, N.ndarray):
if asum(N.less(x, 0) + N.greater(x, 1)) != 0:
- raise ValueError, 'Bad x in abetai'
+ raise ValueError('Bad x in abetai')
x = N.where(N.equal(x,0),TINY,x)
x = N.where(N.equal(x,1.0),1-TINY,x)
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