[LNT] r371945 - [LNT] Python 3 support: Update `<>` and `None` comparisons
Hubert Tong via llvm-commits
llvm-commits at lists.llvm.org
Sun Sep 15 16:21:52 PDT 2019
Author: hubert.reinterpretcast
Date: Sun Sep 15 16:21:52 2019
New Revision: 371945
URL: http://llvm.org/viewvc/llvm-project?rev=371945&view=rev
Log:
[LNT] Python 3 support: Update `<>` and `None` comparisons
Summary:
Replaces comparisons using `!=`, `<>`, and `==` against `None` with the
corresponding version of `is not None` and `is None`.
Replaces `<>` with `!=`.
As requested by reviewers, add spaces around operators on lines touched.
Reviewers: cmatthews, thopre, kristof.beyls
Reviewed By: thopre
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D67535
Modified:
lnt/trunk/lnt/external/stats/pstat.py
lnt/trunk/lnt/external/stats/stats.py
lnt/trunk/lnt/server/ui/filters.py
lnt/trunk/tests/server/db/ImportV4TestSuiteInstance.py
Modified: lnt/trunk/lnt/external/stats/pstat.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/external/stats/pstat.py?rev=371945&r1=371944&r2=371945&view=diff
==============================================================================
--- lnt/trunk/lnt/external/stats/pstat.py (original)
+++ lnt/trunk/lnt/external/stats/pstat.py Sun Sep 15 16:21:52 2019
@@ -256,7 +256,7 @@ Returns: a list of lists with all unique
keepcols = [keepcols]
if not isinstance(collapsecols, (list, tuple)):
collapsecols = [collapsecols]
- if cfcn == None:
+ if cfcn is None:
cfcn = collmean
if keepcols == []:
means = [0]*len(collapsecols)
@@ -291,13 +291,13 @@ Returns: a list of lists with all unique
for col in collapsecols:
avgcol = colex(tmprows,col)
item.append(cfcn(avgcol))
- if fcn1 <> None:
+ if fcn1 is not None:
try:
test = fcn1(avgcol)
except:
test = 'N/A'
item.append(test)
- if fcn2 <> None:
+ if fcn2 is not None:
try:
test = fcn2(avgcol)
except:
@@ -585,7 +585,7 @@ Usage: recode (inlist,listmap,cols=Non
Returns: inlist with the appropriate values replaced with new ones
"""
lst = copy.deepcopy(inlist)
- if cols != None:
+ if cols is not None:
if not isinstance(cols, (list, tuple)):
cols = [cols]
for col in cols:
@@ -785,18 +785,18 @@ Returns: unique 'conditions' specified b
if not isinstance(collapsecols, (list, tuple, N.ndarray)):
collapsecols = [collapsecols]
- if cfcn == None:
+ if cfcn is None:
cfcn = acollmean
if keepcols == []:
avgcol = acolex(a,collapsecols)
means = N.sum(avgcol)/float(len(avgcol))
- if fcn1<>None:
+ if fcn1 is not None:
try:
test = fcn1(avgcol)
except:
test = N.array(['N/A']*len(means))
means = aabut(means,test)
- if fcn2<>None:
+ if fcn2 is not None:
try:
test = fcn2(avgcol)
except:
@@ -817,13 +817,13 @@ Returns: unique 'conditions' specified b
for col in collapsecols:
avgcol = acolex(tmprows,col)
item.append(acollmean(avgcol))
- if fcn1<>None:
+ if fcn1 is not None:
try:
test = fcn1(avgcol)
except:
test = 'N/A'
item.append(test)
- if fcn2<>None:
+ if fcn2 is not None:
try:
test = fcn2(avgcol)
except:
Modified: lnt/trunk/lnt/external/stats/stats.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/external/stats/stats.py?rev=371945&r1=371944&r2=371945&view=diff
==============================================================================
--- lnt/trunk/lnt/external/stats/stats.py (original)
+++ lnt/trunk/lnt/external/stats/stats.py Sun Sep 15 16:21:52 2019
@@ -511,7 +511,7 @@ spanning all the numbers in the inlist.
Usage: lhistogram (inlist, numbins=10, defaultreallimits=None,suppressoutput=0)
Returns: list of bin values, lowerreallimit, binsize, extrapoints
"""
- if (defaultreallimits <> None):
+ if (defaultreallimits is not None):
if not isinstance(defaultreallimits, (list, tuple)) or len(defaultreallimits) == 1: # only one limit given, assumed to be lower one & upper is calc'd
lowerreallimit = defaultreallimits
upperreallimit = 1.000001 * max(inlist)
@@ -598,7 +598,7 @@ Returns: transformed data for use in an
for j in range(k):
if v[j] - mean(nargs[j]) > TINY:
check = 0
- if check <> 1:
+ if check != 1:
raise ValueError, 'Problem in obrientransform.'
else:
return nargs
@@ -841,7 +841,7 @@ Usage: lpearsonr(x,y) where x and
Returns: Pearson's r value, two-tailed p-value
"""
TINY = 1.0e-30
- if len(x) <> len(y):
+ if len(x) != len(y):
raise ValueError, 'Input values not paired in pearsonr. Aborting.'
n = len(x)
x = map(float,x)
@@ -880,7 +880,7 @@ Usage: lspearmanr(x,y) where x an
Returns: Spearman's r, two-tailed p-value
"""
TINY = 1e-30
- if len(x) <> len(y):
+ if len(x) != len(y):
raise ValueError, 'Input values not paired in spearmanr. Aborting.'
n = len(x)
rankx = rankdata(x)
@@ -905,11 +905,11 @@ Usage: lpointbiserialr(x,y) where
Returns: Point-biserial r, two-tailed p-value
"""
TINY = 1e-30
- if len(x) <> len(y):
+ if len(x) != len(y):
raise ValueError, 'INPUT VALUES NOT PAIRED IN pointbiserialr. ABORTING.'
data = pstat.abut(x,y)
categories = pstat.unique(x)
- if len(categories) <> 2:
+ if len(categories) != 2:
raise ValueError, "Exactly 2 categories required for pointbiserialr()."
else: # there are 2 categories, continue
codemap = pstat.abut(categories,range(2))
@@ -970,7 +970,7 @@ Usage: llinregress(x,y) x,y are e
Returns: slope, intercept, r, two-tailed prob, sterr-of-estimate
"""
TINY = 1.0e-20
- if len(x) <> len(y):
+ if len(x) != len(y):
raise ValueError, 'Input values not paired in linregress. Aborting.'
n = len(x)
x = map(float,x)
@@ -1012,7 +1012,7 @@ Returns: t-value, two-tailed prob
t = (x-popmean)/math.sqrt(svar*(1.0/n))
prob = betai(0.5*df,0.5,float(df)/(df+t*t))
- if printit <> 0:
+ if printit != 0:
statname = 'Single-sample T-test.'
outputpairedstats(printit,writemode,
'Population','--',popmean,0,0,0,
@@ -1043,7 +1043,7 @@ Returns: t-value, two-tailed prob
t = (x1-x2)/math.sqrt(svar*(1.0/n1 + 1.0/n2))
prob = betai(0.5*df,0.5,df/(df+t*t))
- if printit <> 0:
+ if printit != 0:
statname = 'Independent samples T-test.'
outputpairedstats(printit,writemode,
name1,n1,x1,v1,min(a),max(a),
@@ -1063,7 +1063,7 @@ and prob.
Usage: lttest_rel(a,b,printit=0,name1='Sample1',name2='Sample2',writemode='a')
Returns: t-value, two-tailed prob
"""
- if len(a)<>len(b):
+ if len(a) != len(b):
raise ValueError, 'Unequal length lists in ttest_rel.'
x1 = mean(a)
x2 = mean(b)
@@ -1079,7 +1079,7 @@ Returns: t-value, two-tailed prob
t = (x1-x2)/sd
prob = betai(0.5*df,0.5,df/(df+t*t))
- if printit <> 0:
+ if printit != 0:
statname = 'Related samples T-test.'
outputpairedstats(printit,writemode,
name1,n,x1,v1,min(a),max(a),
@@ -1098,7 +1098,7 @@ Usage: lchisquare(f_obs, f_exp=None)
Returns: chisquare-statistic, associated p-value
"""
k = len(f_obs) # number of groups
- if f_exp == None:
+ if f_exp is None:
f_exp = [sum(f_obs)/float(k)] * len(f_obs) # create k bins with = freq.
chisq = 0
for i in range(len(f_obs)):
@@ -1229,12 +1229,12 @@ result. A non-parametric T-test.
Usage: lwilcoxont(x,y)
Returns: a t-statistic, two-tail probability estimate
"""
- if len(x) <> len(y):
+ if len(x) != len(y):
raise ValueError, 'Unequal N in wilcoxont. Aborting.'
d=[]
for i in range(len(x)):
diff = x[i] - y[i]
- if diff <> 0:
+ if diff != 0:
d.append(diff)
count = len(d)
absd = map(abs,d)
@@ -1711,7 +1711,7 @@ length lists.
Usage: lsummult(list1,list2)
"""
- if len(list1) <> len(list2):
+ if len(list1) != len(list2):
raise ValueError, "Lists not equal length in summult."
s = 0
for item1,item2 in pstat.abut(list1,list2):
@@ -1787,7 +1787,7 @@ Returns: a list of length equal to inlis
for i in range(n):
sumranks = sumranks + i
dupcount = dupcount + 1
- if i==n-1 or svec[i] <> svec[i+1]:
+ if i == n - 1 or svec[i] != svec[i + 1]:
averank = sumranks / float(dupcount) + 1
for j in range(i-dupcount+1,i+1):
newlist[ivec[j]] = averank
@@ -2013,7 +2013,7 @@ Usage: ageometricmean(inarray,dimensio
Returns: geometric mean computed over dim(s) listed in dimension
"""
inarray = N.array(inarray,N.float_)
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
size = len(inarray)
mult = N.power(inarray,1.0/size)
@@ -2056,7 +2056,7 @@ Usage: aharmonicmean(inarray,dimension
Returns: harmonic mean computed over dim(s) in dimension
"""
inarray = inarray.astype(N.float_)
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
size = len(inarray)
s = N.add.reduce(1.0 / inarray)
@@ -2085,7 +2085,7 @@ Returns: harmonic mean computed over dim
idx[0] = -1
loopcap = N.array(tinarray.shape[0:len(nondims)]) -1
s = N.zeros(loopcap+1,N.float_)
- while incr(idx,loopcap) <> -1:
+ while incr(idx, loopcap) != -1:
s[idx] = asum(1.0/tinarray[idx])
size = N.multiply.reduce(N.take(inarray.shape,dims))
if keepdims == 1:
@@ -2111,7 +2111,7 @@ Returns: arithematic mean calculated ove
"""
if inarray.dtype in [N.int_, N.short,N.ubyte]:
inarray = inarray.astype(N.float_)
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
sum = N.add.reduce(inarray)
denom = float(len(inarray))
@@ -2172,7 +2172,7 @@ be None, to pre-flatten the array, or el
Usage: amedianscore(inarray,dimension=None)
Returns: 'middle' score of the array, or the mean of the 2 middle scores
"""
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
dimension = 0
inarray = N.sort(inarray,dimension)
@@ -2198,7 +2198,7 @@ Usage: amode(a, dimension=None)
Returns: array of bin-counts for mode(s), array of corresponding modal values
"""
- if dimension == None:
+ if dimension is None:
a = N.ravel(a)
dimension = 0
scores = pstat.aunique(N.ravel(a)) # get ALL unique values
@@ -2227,7 +2227,7 @@ Usage: atmean(a,limits=None,inclusive=
"""
if a.dtype in [N.int_, N.short,N.ubyte]:
a = a.astype(N.float_)
- if limits == None:
+ if limits is None:
return mean(a)
assert isinstance(limits, (list, tuple, N.ndarray)), "Wrong type for limits in atmean"
if inclusive[0]: lowerfcn = N.greater_equal
@@ -2236,11 +2236,11 @@ Usage: atmean(a,limits=None,inclusive=
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)."
- elif limits[0]==None and limits[1]<>None:
+ elif limits[0] is None and limits[1] is not None:
mask = upperfcn(a,limits[1])
- elif limits[0]<>None and limits[1]==None:
+ elif limits[0] is not None and limits[1] is None:
mask = lowerfcn(a,limits[0])
- elif limits[0]<>None and limits[1]<>None:
+ elif limits[0] is not None and limits[1] is not None:
mask = lowerfcn(a,limits[0])*upperfcn(a,limits[1])
s = float(N.add.reduce(N.ravel(a*mask)))
n = float(N.add.reduce(N.ravel(mask)))
@@ -2259,7 +2259,7 @@ closed/inclusive (1). ASSUMES A FLAT ARR
Usage: atvar(a,limits=None,inclusive=(1,1))
"""
a = a.astype(N.float_)
- if limits == None or limits == [None,None]:
+ if limits is None or limits == [None, None]:
return avar(a)
assert isinstance(limits, (list, tuple, N.ndarray)), "Wrong type for limits in atvar"
if inclusive[0]: lowerfcn = N.greater_equal
@@ -2268,11 +2268,11 @@ Usage: atvar(a,limits=None,inclusive=(
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)."
- elif limits[0]==None and limits[1]<>None:
+ elif limits[0] is None and limits[1] is not None:
mask = upperfcn(a,limits[1])
- elif limits[0]<>None and limits[1]==None:
+ elif limits[0] is not None and limits[1] is None:
mask = lowerfcn(a,limits[0])
- elif limits[0]<>None and limits[1]<>None:
+ elif limits[0] is not None and limits[1] is not None:
mask = lowerfcn(a,limits[0])*upperfcn(a,limits[1])
a = N.compress(mask,a) # squish out excluded values
@@ -2289,10 +2289,10 @@ Usage: atmin(a,lowerlimit=None,dimensi
"""
if inclusive: lowerfcn = N.greater
else: lowerfcn = N.greater_equal
- if dimension == None:
+ if dimension is None:
a = N.ravel(a)
dimension = 0
- if lowerlimit == None:
+ if lowerlimit is None:
lowerlimit = N.minimum.reduce(N.ravel(a))-11
biggest = N.maximum.reduce(N.ravel(a))
ta = N.where(lowerfcn(a,lowerlimit),a,biggest)
@@ -2309,10 +2309,10 @@ Usage: atmax(a,upperlimit,dimension=No
"""
if inclusive: upperfcn = N.less
else: upperfcn = N.less_equal
- if dimension == None:
+ if dimension is None:
a = N.ravel(a)
dimension = 0
- if upperlimit == None:
+ if upperlimit is None:
upperlimit = N.maximum.reduce(N.ravel(a))+1
smallest = N.minimum.reduce(N.ravel(a))
ta = N.where(upperfcn(a,upperlimit),a,smallest)
@@ -2344,7 +2344,7 @@ open/exclusive (0) or closed/inclusive (
Usage: atsem(a,limits=None,inclusive=(1,1))
"""
sd = tstdev(a,limits,inclusive)
- if limits == None or limits == [None,None]:
+ if limits is None or limits == [None, None]:
n = float(len(N.ravel(a)))
limits = [min(a)-1, max(a)+1]
assert isinstance(limits, (list, tuple, N.ndarray)), "Wrong type for limits in atsem"
@@ -2354,11 +2354,11 @@ Usage: atsem(a,limits=None,inclusive=(
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)."
- elif limits[0]==None and limits[1]<>None:
+ elif limits[0] is None and limits[1] is not None:
mask = upperfcn(a,limits[1])
- elif limits[0]<>None and limits[1]==None:
+ elif limits[0] is not None and limits[1] is None:
mask = lowerfcn(a,limits[0])
- elif limits[0]<>None and limits[1]<>None:
+ elif limits[0] is not None and limits[1] is not None:
mask = lowerfcn(a,limits[0])*upperfcn(a,limits[1])
term1 = N.add.reduce(N.ravel(a*a*mask))
n = float(N.add.reduce(N.ravel(mask)))
@@ -2380,7 +2380,7 @@ multiple dimensions).
Usage: amoment(a,moment=1,dimension=None)
Returns: appropriate moment along given dimension
"""
- if dimension == None:
+ if dimension is None:
a = N.ravel(a)
dimension = 0
if moment == 1:
@@ -2416,7 +2416,7 @@ Returns: skew of vals in a along dimensi
"""
denom = N.power(amoment(a,2,dimension),1.5)
zero = N.equal(denom,0)
- if isinstance(denom, N.ndarray) and asum(zero) <> 0:
+ if isinstance(denom, N.ndarray) and asum(zero) != 0:
print("Number of zeros in askew: ", asum(zero))
denom = denom + zero # prevent divide-by-zero
return N.where(zero, 0, amoment(a,3,dimension)/denom)
@@ -2435,7 +2435,7 @@ Returns: kurtosis of values in a along d
"""
denom = N.power(amoment(a,2,dimension),2)
zero = N.equal(denom,0)
- if isinstance(denom, N.ndarray) and asum(zero) <> 0:
+ if isinstance(denom, N.ndarray) and asum(zero) != 0:
print("Number of zeros in akurtosis: ", asum(zero))
denom = denom + zero # prevent divide-by-zero
return N.where(zero,0,amoment(a,4,dimension)/denom)
@@ -2450,7 +2450,7 @@ which to operate), or a sequence (operat
Usage: adescribe(inarray,dimension=None)
Returns: n, (min,max), mean, standard deviation, skew, kurtosis
"""
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
dimension = 0
n = inarray.shape[dimension]
@@ -2476,7 +2476,7 @@ over multiple dimensions).
Usage: askewtest(a,dimension=None)
Returns: z-score and 2-tail z-probability
"""
- if dimension == None:
+ if dimension is None:
a = N.ravel(a)
dimension = 0
b2 = askew(a,dimension)
@@ -2501,7 +2501,7 @@ or a sequence (operate over multiple dim
Usage: akurtosistest(a,dimension=None)
Returns: z-score and 2-tail z-probability, returns 0 for bad pixels
"""
- if dimension == None:
+ if dimension is None:
a = N.ravel(a)
dimension = 0
n = float(a.shape[dimension])
@@ -2533,7 +2533,7 @@ operate), or a sequence (operate over mu
Usage: anormaltest(a,dimension=None)
Returns: z-score and 2-tail probability
"""
- if dimension == None:
+ if dimension is None:
a = N.ravel(a)
dimension = 0
s,p = askewtest(a,dimension)
@@ -2607,7 +2607,7 @@ Usage: ahistogram(inarray,numbins=10,d
Returns: (array of bin counts, bin-minimum, min-width, #-points-outside-range)
"""
inarray = N.ravel(inarray) # flatten any >1D arrays
- if (defaultlimits <> None):
+ if (defaultlimits is not None):
lowerreallimit = defaultlimits[0]
upperreallimit = defaultlimits[1]
binsize = (upperreallimit-lowerreallimit) / float(numbins)
@@ -2697,7 +2697,7 @@ Returns: transformed data for use in an
for j in range(k):
if v[j] - mean(nargs[j]) > TINY:
check = 0
- if check <> 1:
+ if check != 1:
raise ValueError, 'Lack of convergence in obrientransform.'
else:
return N.array(nargs)
@@ -2713,7 +2713,7 @@ with the same number of dimensions as in
Usage: asamplevar(inarray,dimension=None,keepdims=0)
"""
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
dimension = 0
if dimension == 1:
@@ -2769,7 +2769,7 @@ same number of dimensions as inarray.
Usage: acov(x,y,dimension=None,keepdims=0)
"""
- if dimension == None:
+ if dimension is None:
x = N.ravel(x)
y = N.ravel(y)
dimension = 0
@@ -2797,7 +2797,7 @@ same number of dimensions as inarray.
Usage: avar(inarray,dimension=None,keepdims=0)
"""
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
dimension = 0
mn = amean(inarray,dimension,1)
@@ -2835,7 +2835,7 @@ an array with the same number of dimensi
Usage: asterr(inarray,dimension=None,keepdims=0)
"""
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
dimension = 0
return astdev(inarray,dimension,keepdims) / float(N.sqrt(inarray.shape[dimension]))
@@ -2851,7 +2851,7 @@ same number of dimensions as inarray.
Usage: asem(inarray,dimension=None, keepdims=0)
"""
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
dimension = 0
if isinstance(dimension, list):
@@ -2917,9 +2917,9 @@ Usage: athreshold(a,threshmin=None,thr
Returns: a, with values <threshmin or >threshmax replaced with newval
"""
mask = N.zeros(a.shape)
- if threshmin <> None:
+ if threshmin is not None:
mask = mask + N.where(a<threshmin,1,0)
- if threshmax <> None:
+ if threshmax is not None:
mask = mask + N.where(a>threshmax,1,0)
mask = N.clip(mask,0,1)
return N.where(mask,newval,a)
@@ -2972,7 +2972,7 @@ Computes the covariance matrix of a matr
Usage: acovariance(X)
Returns: covariance matrix of X
"""
- if len(X.shape) <> 2:
+ if len(X.shape) != 2:
raise TypeError, "acovariance requires 2D matrices"
n = X.shape[0]
mX = amean(X,0)
@@ -3169,7 +3169,7 @@ Returns: Point-biserial r, two-tailed p-
TINY = 1e-30
categories = pstat.aunique(x)
data = pstat.aabut(x,y)
- if len(categories) <> 2:
+ if len(categories) != 2:
raise ValueError, "Exactly 2 categories required (in x) for pointbiserialr()."
else: # there are 2 categories, continue
codemap = pstat.aabut(categories,N.arange(2))
@@ -3328,7 +3328,7 @@ Returns: t-value, two-tailed prob
t = (x-popmean)/math.sqrt(svar*(1.0/n))
prob = abetai(0.5*df,0.5,df/(df+t*t))
- if printit <> 0:
+ if printit != 0:
statname = 'Single-sample T-test.'
outputpairedstats(printit,writemode,
'Population','--',popmean,0,0,0,
@@ -3351,7 +3351,7 @@ Usage: attest_ind (a,b,dimension=None,
Name1='Samp1',Name2='Samp2',writemode='a')
Returns: t-value, two-tailed p-value
"""
- if dimension == None:
+ if dimension is None:
a = N.ravel(a)
b = N.ravel(b)
dimension = 0
@@ -3374,7 +3374,7 @@ Returns: t-value, two-tailed p-value
if probs.shape == (1,):
probs = probs[0]
- if printit <> 0:
+ if printit != 0:
if isinstance(t, N.ndarray):
t = t[0]
if isinstance(probs, N.ndarray):
@@ -3432,11 +3432,11 @@ Usage: attest_rel(a,b,dimension=None,p
name1='Samp1',name2='Samp2',writemode='a')
Returns: t-value, two-tailed p-value
"""
- if dimension == None:
+ if dimension is None:
a = N.ravel(a)
b = N.ravel(b)
dimension = 0
- if len(a)<>len(b):
+ if len(a) != len(b):
raise ValueError, 'Unequal length arrays.'
x1 = amean(a,dimension)
x2 = amean(b,dimension)
@@ -3457,7 +3457,7 @@ Returns: t-value, two-tailed p-value
if probs.shape == (1,):
probs = probs[0]
- if printit <> 0:
+ if printit != 0:
statname = 'Related samples T-test.'
outputpairedstats(printit,writemode,
name1,n,x1,v1,N.minimum.reduce(N.ravel(a)),
@@ -3481,7 +3481,7 @@ Returns: chisquare-statistic, associated
"""
k = len(f_obs)
- if f_exp == None:
+ if f_exp is None:
f_exp = N.array([sum(f_obs)/float(k)] * len(f_obs),N.float_)
f_exp = f_exp.astype(N.float_)
chisq = N.add.reduce((f_obs-f_exp)**2 / f_exp)
@@ -3611,7 +3611,7 @@ result. A non-parametric T-test.
Usage: awilcoxont(x,y) where x,y are equal-length arrays for 2 conditions
Returns: t-statistic, two-tailed p-value
"""
- if len(x) <> len(y):
+ if len(x) != len(y):
raise ValueError, 'Unequal N in awilcoxont. Aborting.'
d = x-y
d = N.compress(N.not_equal(d,0),d) # Keep all non-zero differences
@@ -3748,7 +3748,7 @@ Usage: achisqprob(chisq,df) chisq=c
a_big = N.greater(a,BIG)
a_big_frozen = -1 *N.ones(probs.shape,N.float_)
totalelements = N.multiply.reduce(N.array(probs.shape))
- while asum(mask)<>totalelements:
+ while asum(mask) != totalelements:
e = N.log(z) + e
s = s + ex(c*z-a-e)
z = z + 1.0
@@ -3765,7 +3765,7 @@ Usage: achisqprob(chisq,df) chisq=c
c = 0.0
mask = N.zeros(probs.shape)
a_notbig_frozen = -1 *N.ones(probs.shape,N.float_)
- while asum(mask)<>totalelements:
+ while asum(mask) != totalelements:
e = e * (a/z.astype(N.float_))
c = c + e
z = z + 1.0
@@ -3934,7 +3934,7 @@ Usage: abetacf(a,b,x,verbose=1)
frozen = N.where(newmask*N.equal(mask,0), az, frozen)
mask = N.clip(mask+newmask,0,1)
noconverge = asum(N.equal(frozen,-1))
- if noconverge <> 0 and verbose:
+ if noconverge != 0 and verbose:
print('a or b too big, or ITMAX too small in Betacf for ', noconverge, ' elements')
if arrayflag:
return frozen
@@ -3978,7 +3978,7 @@ 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:
+ if asum(N.less(x, 0) + N.greater(x, 1)) != 0:
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)
@@ -4019,7 +4019,7 @@ from:
Usage: aglm(data,para)
Returns: statistic, p-value ???
"""
- if len(para) <> len(data):
+ if len(para) != len(data):
print("data and para must be same length in aglm")
return
n = len(para)
@@ -4155,7 +4155,7 @@ Returns: array summed along 'dimension'(
"""
if isinstance(a, N.ndarray) and a.dtype in [N.int_, N.short, N.ubyte]:
a = a.astype(N.float_)
- if dimension == None:
+ if dimension is None:
s = N.sum(N.ravel(a))
elif isinstance(dimension, (int, float)):
s = N.add.reduce(a, dimension)
@@ -4187,7 +4187,7 @@ over multiple dimensions, but this last
Usage: acumsum(a,dimension=None)
"""
- if dimension == None:
+ if dimension is None:
a = N.ravel(a)
dimension = 0
if isinstance(dimension, (list, tuple, N.ndarray)):
@@ -4213,7 +4213,7 @@ of dimensions.
Usage: ass(inarray, dimension=None, keepdims=0)
Returns: sum-along-'dimension' for (inarray*inarray)
"""
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
dimension = 0
return asum(inarray*inarray,dimension,keepdims)
@@ -4229,7 +4229,7 @@ dimensions). A trivial function, but in
Usage: asummult(array1,array2,dimension=None,keepdims=0)
"""
- if dimension == None:
+ if dimension is None:
array1 = N.ravel(array1)
array2 = N.ravel(array2)
dimension = 0
@@ -4247,7 +4247,7 @@ NUMBER of dimensions as the original.
Usage: asquare_of_sums(inarray, dimension=None, keepdims=0)
Returns: the square of the sum over dim(s) in dimension
"""
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(inarray)
dimension = 0
s = asum(inarray,dimension,keepdims)
@@ -4268,7 +4268,7 @@ keepdims=1 means the return shape = len(
Usage: asumdiffsquared(a,b)
Returns: sum[ravel(a-b)**2]
"""
- if dimension == None:
+ if dimension is None:
inarray = N.ravel(a)
dimension = 0
return asum((a-b)**2,dimension,keepdims)
@@ -4316,7 +4316,7 @@ Returns: array of length equal to inarra
for i in range(n):
sumranks = sumranks + i
dupcount = dupcount + 1
- if i==n-1 or svec[i] <> svec[i+1]:
+ if i == n - 1 or svec[i] != svec[i + 1]:
averank = sumranks / float(dupcount) + 1
for j in range(i-dupcount+1,i+1):
newarray[ivec[j]] = averank
Modified: lnt/trunk/lnt/server/ui/filters.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/server/ui/filters.py?rev=371945&r1=371944&r2=371945&view=diff
==============================================================================
--- lnt/trunk/lnt/server/ui/filters.py (original)
+++ lnt/trunk/lnt/server/ui/filters.py Sun Sep 15 16:21:52 2019
@@ -63,7 +63,7 @@ def filter_filesize(value):
def filter_print_value(value, field_unit, field_unit_abbrev, default = '-'):
- if value == None:
+ if value is None:
return default
if field_unit == 'bytes' and field_unit_abbrev == 'B':
Modified: lnt/trunk/tests/server/db/ImportV4TestSuiteInstance.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/tests/server/db/ImportV4TestSuiteInstance.py?rev=371945&r1=371944&r2=371945&view=diff
==============================================================================
--- lnt/trunk/tests/server/db/ImportV4TestSuiteInstance.py (original)
+++ lnt/trunk/tests/server/db/ImportV4TestSuiteInstance.py Sun Sep 15 16:21:52 2019
@@ -151,9 +151,9 @@ assert sample_a_0.compile_status == lnt.
assert sample_a_0.execution_time == 0.3
assert sample_a_0.execution_status == lnt.testing.PASS
assert sample_a_1.compile_time == 0.0189
-assert sample_a_1.compile_status == None
+assert sample_a_1.compile_status is None
assert sample_a_1.execution_time == 0.29
-assert sample_a_1.execution_status == None
+assert sample_a_1.execution_status is None
assert sample_b.compile_time == 0.022
assert sample_b.compile_status == lnt.testing.PASS
assert sample_b.execution_time == 0.32
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