[LNT] r371944 - Subject: [LNT] Python 3 support: Update type comparisons and type names
Hubert Tong via llvm-commits
llvm-commits at lists.llvm.org
Sun Sep 15 16:01:06 PDT 2019
Author: hubert.reinterpretcast
Date: Sun Sep 15 16:01:06 2019
New Revision: 371944
URL: http://llvm.org/viewvc/llvm-project?rev=371944&view=rev
Log:
Subject: [LNT] Python 3 support: Update type comparisons and type names
Summary:
This patch is split out from D67535, updating type names (or, in one
case, `type(1.4)`-style goodness) and updating type comparisons with use
of `isinstance`. Additionally, changed a dictionary key lookup for a
type mapping.
As requested by reviewers in D67535, spaces are added after commas in
what are now tuples of types.
Reviewers: cmatthews, thopre, kristof.beyls
Reviewed By: thopre
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D67587
Modified:
lnt/trunk/lnt/external/stats/pstat.py
lnt/trunk/lnt/external/stats/stats.py
lnt/trunk/lnt/server/db/regression.py
lnt/trunk/lnt/server/ui/api.py
lnt/trunk/lnt/server/ui/app.py
lnt/trunk/lnt/tests/nt.py
Modified: lnt/trunk/lnt/external/stats/pstat.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/external/stats/pstat.py?rev=371944&r1=371943&r2=371944&view=diff
==============================================================================
--- lnt/trunk/lnt/external/stats/pstat.py (original)
+++ lnt/trunk/lnt/external/stats/pstat.py Sun Sep 15 16:01:06 2019
@@ -131,10 +131,10 @@ Returns: a list of lists as long as the
'left', lists in <args> attached consecutively on the 'right'
"""
- if type(source) not in [ListType,TupleType]:
+ if not isinstance(source, (list, tuple)):
source = [source]
for addon in args:
- if type(addon) not in [ListType,TupleType]:
+ if not isinstance(addon, (list, tuple)):
addon = [addon]
if len(addon) < len(source): # is source list longer?
if len(source) % len(addon) == 0: # are they integer multiples?
@@ -177,21 +177,21 @@ Usage: simpleabut(source,addon) where
Returns: a list of lists as long as source, with source on the 'left' and
addon on the 'right'
"""
- if type(source) not in [ListType,TupleType]:
+ if not isinstance(source, (list, tuple)):
source = [source]
- if type(addon) not in [ListType,TupleType]:
+ if not isinstance(addon, (list, tuple)):
addon = [addon]
minlen = min(len(source),len(addon))
list = copy.deepcopy(source) # start abut process
- if type(source[0]) not in [ListType,TupleType]:
- if type(addon[0]) not in [ListType,TupleType]:
+ if not isinstance(source[0], (list, tuple)):
+ if not isinstance(addon[0], (list, tuple)):
for i in range(minlen):
list[i] = [source[i]] + [addon[i]] # source/addon = column
else:
for i in range(minlen):
list[i] = [source[i]] + addon[i] # addon=list-of-lists
else:
- if type(addon[0]) not in [ListType,TupleType]:
+ if not isinstance(addon[0], (list, tuple)):
for i in range(minlen):
list[i] = source[i] + [addon[i]] # source=list-of-lists
else:
@@ -214,13 +214,13 @@ Returns: a list-of-lists corresponding t
"""
global index
column = 0
- if type(cnums) in [ListType,TupleType]: # if multiple columns to get
+ if isinstance(cnums, (list, tuple)): # if multiple columns to get
index = cnums[0]
column = map(lambda x: x[index], listoflists)
for col in cnums[1:]:
index = col
column = abut(column,map(lambda x: x[index], listoflists))
- elif type(cnums) == StringType: # if an 'x[3:]' type expr.
+ elif isinstance(cnums, str): # if an 'x[3:]' type expr.
evalstring = 'map(lambda x: x'+cnums+', listoflists)'
column = eval(evalstring)
else: # else it's just 1 col to get
@@ -252,9 +252,9 @@ Returns: a list of lists with all unique
s = s + item
return s/float(len(inlist))
- if type(keepcols) not in [ListType,TupleType]:
+ if not isinstance(keepcols, (list, tuple)):
keepcols = [keepcols]
- if type(collapsecols) not in [ListType,TupleType]:
+ if not isinstance(collapsecols, (list, tuple)):
collapsecols = [collapsecols]
if cfcn == None:
cfcn = collmean
@@ -284,9 +284,9 @@ Returns: a list of lists with all unique
uniques = unique(values)
uniques.sort()
newlist = []
- if type(keepcols) not in [ListType,TupleType]: keepcols = [keepcols]
+ if not isinstance(keepcols, (list, tuple)): keepcols = [keepcols]
for item in uniques:
- if type(item) not in [ListType,TupleType]: item =[item]
+ if not isinstance(item, (list, tuple)): item =[item]
tmprows = linexand(listoflists,keepcols,item)
for col in collapsecols:
avgcol = colex(tmprows,col)
@@ -345,13 +345,13 @@ len(columnlist) must equal len(valuelist
Usage: linexand (listoflists,columnlist,valuelist)
Returns: the rows of listoflists where columnlist[i]=valuelist[i] for ALL i
"""
- if type(columnlist) not in [ListType,TupleType]:
+ if not isinstance(columnlist, (list, tuple)):
columnlist = [columnlist]
- if type(valuelist) not in [ListType,TupleType]:
+ if not isinstance(valuelist, (list, tuple)):
valuelist = [valuelist]
criterion = ''
for i in range(len(columnlist)):
- if type(valuelist[i])==StringType:
+ if isinstance(valuelist[i], str):
critval = '\'' + valuelist[i] + '\''
else:
critval = str(valuelist[i])
@@ -373,15 +373,15 @@ valuelist values are all assumed to pert
Usage: linexor (listoflists,columnlist,valuelist)
Returns: the rows of listoflists where columnlist[i]=valuelist[i] for ANY i
"""
- if type(columnlist) not in [ListType,TupleType]:
+ if not isinstance(columnlist, (list, tuple)):
columnlist = [columnlist]
- if type(valuelist) not in [ListType,TupleType]:
+ if not isinstance(valuelist, (list, tuple)):
valuelist = [valuelist]
criterion = ''
if len(columnlist) == 1 and len(valuelist) > 1:
columnlist = columnlist*len(valuelist)
for i in range(len(columnlist)): # build an exec string
- if type(valuelist[i])==StringType:
+ if isinstance(valuelist[i], str):
critval = '\'' + valuelist[i] + '\''
else:
critval = str(valuelist[i])
@@ -402,7 +402,7 @@ Usage: linedelimited (inlist,delimiter
"""
outstr = ''
for item in inlist:
- if type(item) <> StringType:
+ if not isinstance(item, str):
item = str(item)
outstr = outstr + item + delimiter
outstr = outstr[0:-1]
@@ -418,7 +418,7 @@ Usage: lineincols (inlist,colsize) w
"""
outstr = ''
for item in inlist:
- if type(item) <> StringType:
+ if not isinstance(item, str):
item = str(item)
size = len(item)
if size <= colsize:
@@ -442,7 +442,7 @@ Returns: formatted string created from i
"""
outstr = ''
for i in range(len(inlist)):
- if type(inlist[i]) <> StringType:
+ if not isinstance(inlist[i], str):
item = str(inlist[i])
else:
item = inlist[i]
@@ -483,7 +483,7 @@ Returns: if l = [1,2,'hi'] then returns
def makestr (x):
- if type(x) <> StringType:
+ if not isinstance(x, str):
x = str(x)
return x
@@ -498,7 +498,7 @@ respectively.
Usage: printcc (lst,extra=2)
Returns: None
"""
- if type(lst[0]) not in [ListType,TupleType]:
+ if not isinstance(lst[0], (list, tuple)):
lst = [lst]
rowstokill = []
list2print = copy.deepcopy(lst)
@@ -568,7 +568,7 @@ Usage: replace (inlst,oldval,newval)
"""
lst = inlst*1
for i in range(len(lst)):
- if type(lst[i]) not in [ListType,TupleType]:
+ if not isinstance(lst[i], (list, tuple)):
if lst[i]==oldval: lst[i]=newval
else:
lst[i] = replace(lst[i],oldval,newval)
@@ -586,7 +586,7 @@ Returns: inlist with the appropriate val
"""
lst = copy.deepcopy(inlist)
if cols != None:
- if type(cols) not in [ListType,TupleType]:
+ if not isinstance(cols, (list, tuple)):
cols = [cols]
for col in cols:
for row in range(len(lst)):
@@ -623,17 +623,17 @@ Returns: remapped version of listoflists
def roundlist (inlist,digits):
"""
Goes through each element in a 1D or 2D inlist, and applies the following
-function to all elements of FloatType ... round(element,digits).
+function to all elements of float ... round(element,digits).
Usage: roundlist(inlist,digits)
Returns: list with rounded floats
"""
- if type(inlist[0]) in [IntType, FloatType]:
+ if isinstance(inlist[0], (int, float)):
inlist = [inlist]
l = inlist*1
for i in range(len(l)):
for j in range(len(l[i])):
- if type(l[i][j])==FloatType:
+ if isinstance(l[i][j], float):
l[i][j] = round(l[i][j],digits)
return l
@@ -756,7 +756,7 @@ column-array (and that the whole array w
Usage: acolex (a,indices,axis=1)
Returns: the columns of a specified by indices
"""
- if type(indices) not in [ListType,TupleType,N.ndarray]:
+ if not isinstance(indices, (list, tuple, N.ndarray)):
indices = [indices]
if len(N.shape(a)) == 1:
cols = N.resize(a,[a.shape[0],1])
@@ -780,9 +780,9 @@ Returns: unique 'conditions' specified b
def acollmean (inarray):
return N.sum(N.ravel(inarray))
- if type(keepcols) not in [ListType,TupleType,N.ndarray]:
+ if not isinstance(keepcols, (list, tuple, N.ndarray)):
keepcols = [keepcols]
- if type(collapsecols) not in [ListType,TupleType,N.ndarray]:
+ if not isinstance(collapsecols, (list, tuple, N.ndarray)):
collapsecols = [collapsecols]
if cfcn == None:
@@ -804,14 +804,14 @@ Returns: unique 'conditions' specified b
means = aabut(means,test)
return means
else:
- if type(keepcols) not in [ListType,TupleType,N.ndarray]:
+ if not isinstance(keepcols, (list, tuple, N.ndarray)):
keepcols = [keepcols]
values = colex(a,keepcols) # so that "item" can be appended (below)
uniques = unique(values) # get a LIST, so .sort keeps rows intact
uniques.sort()
newlist = []
for item in uniques:
- if type(item) not in [ListType,TupleType,N.ndarray]:
+ if not isinstance(item, (list, tuple, N.ndarray)):
item =[item]
tmprows = alinexand(a,keepcols,item)
for col in collapsecols:
@@ -854,7 +854,7 @@ Usage: adm (a,criterion) where crite
def isstring(x):
- if type(x)==StringType:
+ if isinstance(x, str):
return 1
else:
return 0
@@ -868,13 +868,13 @@ Returns the rows of an array where col (
Usage: alinexand (a,columnlist,valuelist)
Returns: the rows of a where columnlist[i]=valuelist[i] for ALL i
"""
- if type(columnlist) not in [ListType,TupleType,N.ndarray]:
+ if not isinstance(columnlist, (list, tuple, N.ndarray)):
columnlist = [columnlist]
- if type(valuelist) not in [ListType,TupleType,N.ndarray]:
+ if not isinstance(valuelist, (list, tuple, N.ndarray)):
valuelist = [valuelist]
criterion = ''
for i in range(len(columnlist)):
- if type(valuelist[i])==StringType:
+ if isinstance(valuelist[i], str):
critval = '\'' + valuelist[i] + '\''
else:
critval = str(valuelist[i])
@@ -894,9 +894,9 @@ other list.
Usage: alinexor (a,columnlist,valuelist)
Returns: the rows of a where columnlist[i]=valuelist[i] for ANY i
"""
- if type(columnlist) not in [ListType,TupleType,N.ndarray]:
+ if not isinstance(columnlist, (list, tuple, N.ndarray)):
columnlist = [columnlist]
- if type(valuelist) not in [ListType,TupleType,N.ndarray]:
+ if not isinstance(valuelist, (list, tuple, N.ndarray)):
valuelist = [valuelist]
criterion = ''
if len(columnlist) == 1 and len(valuelist) > 1:
@@ -904,7 +904,7 @@ Returns: the rows of a where columnlist[
elif len(valuelist) == 1 and len(columnlist) > 1:
valuelist = valuelist*len(columnlist)
for i in range(len(columnlist)):
- if type(valuelist[i])==StringType:
+ if isinstance(valuelist[i], str):
critval = '\'' + valuelist[i] + '\''
else:
critval = str(valuelist[i])
@@ -939,7 +939,7 @@ Returns: a version of array a where list
work = acolex(a,col)
work = work.ravel()
for pair in listmap:
- if type(pair[1]) == StringType or work.dtype.char=='O' or a.dtype.char=='O':
+ if isinstance(pair[1], str) or work.dtype.char == 'O' or a.dtype.char == 'O':
work = N.array(work,dtype='O')
a = N.array(a,dtype='O')
for i in range(len(work)):
Modified: lnt/trunk/lnt/external/stats/stats.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/external/stats/stats.py?rev=371944&r1=371943&r2=371944&view=diff
==============================================================================
--- lnt/trunk/lnt/external/stats/stats.py (original)
+++ lnt/trunk/lnt/external/stats/stats.py Sun Sep 15 16:01:06 2019
@@ -246,13 +246,12 @@ print stats.amean.__doc__ or whatever.
self._dispatch = {}
for func, types in tuples:
for t in types:
- if t in self._dispatch.keys():
+ if t in self._dispatch:
raise ValueError, "can't have two dispatches on "+str(t)
self._dispatch[t] = func
- self._types = self._dispatch.keys()
def __call__(self, arg1, *args, **kw):
- if type(arg1) not in self._types:
+ if type(arg1) not in self._dispatch:
raise TypeError, "don't know how to dispatch %s arguments" % type(arg1)
return apply(self._dispatch[type(arg1)], (arg1,) + args, kw)
@@ -513,7 +512,7 @@ Usage: lhistogram (inlist, numbins=10,
Returns: list of bin values, lowerreallimit, binsize, extrapoints
"""
if (defaultreallimits <> None):
- if type(defaultreallimits) not in [ListType,TupleType] or len(defaultreallimits)==1: # only one limit given, assumed to be lower one & upper is calc'd
+ 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)
else: # assume both limits given
@@ -1618,7 +1617,7 @@ to specified file. File-overwrite is th
Usage: writecc (listoflists,file,writetype='w',extra=2)
Returns: None
"""
- if type(listoflists[0]) not in [ListType,TupleType]:
+ if not isinstance(listoflists[0], (list, tuple)):
listoflists = [listoflists]
outfile = open(file,writetype)
rowstokill = []
@@ -1821,7 +1820,7 @@ Returns: None
title = [['Name','N','Mean','SD','Min','Max']]
lofl = title+[[name1,n1,round(m1,3),round(math.sqrt(se1),3),min1,max1],
[name2,n2,round(m2,3),round(math.sqrt(se2),3),min2,max2]]
- if type(fname)<>StringType or len(fname)==0:
+ if not isinstance(fname, str) or len(fname) == 0:
print()
print(statname)
print()
@@ -1886,89 +1885,89 @@ Usage: lfindwithin(data) data in |
#########################################################
## CENTRAL TENDENCY:
-geometricmean = Dispatch ( (lgeometricmean, (ListType, TupleType)), )
-harmonicmean = Dispatch ( (lharmonicmean, (ListType, TupleType)), )
-mean = Dispatch ( (lmean, (ListType, TupleType)), )
-median = Dispatch ( (lmedian, (ListType, TupleType)), )
-medianscore = Dispatch ( (lmedianscore, (ListType, TupleType)), )
-mode = Dispatch ( (lmode, (ListType, TupleType)), )
+geometricmean = Dispatch ( (lgeometricmean, (list, tuple)), )
+harmonicmean = Dispatch ( (lharmonicmean, (list, tuple)), )
+mean = Dispatch ( (lmean, (list, tuple)), )
+median = Dispatch ( (lmedian, (list, tuple)), )
+medianscore = Dispatch ( (lmedianscore, (list, tuple)), )
+mode = Dispatch ( (lmode, (list, tuple)), )
## MOMENTS:
-moment = Dispatch ( (lmoment, (ListType, TupleType)), )
-variation = Dispatch ( (lvariation, (ListType, TupleType)), )
-skew = Dispatch ( (lskew, (ListType, TupleType)), )
-kurtosis = Dispatch ( (lkurtosis, (ListType, TupleType)), )
-describe = Dispatch ( (ldescribe, (ListType, TupleType)), )
+moment = Dispatch ( (lmoment, (list, tuple)), )
+variation = Dispatch ( (lvariation, (list, tuple)), )
+skew = Dispatch ( (lskew, (list, tuple)), )
+kurtosis = Dispatch ( (lkurtosis, (list, tuple)), )
+describe = Dispatch ( (ldescribe, (list, tuple)), )
## FREQUENCY STATISTICS:
-itemfreq = Dispatch ( (litemfreq, (ListType, TupleType)), )
-scoreatpercentile = Dispatch ( (lscoreatpercentile, (ListType, TupleType)), )
-percentileofscore = Dispatch ( (lpercentileofscore, (ListType, TupleType)), )
-histogram = Dispatch ( (lhistogram, (ListType, TupleType)), )
-cumfreq = Dispatch ( (lcumfreq, (ListType, TupleType)), )
-relfreq = Dispatch ( (lrelfreq, (ListType, TupleType)), )
+itemfreq = Dispatch ( (litemfreq, (list, tuple)), )
+scoreatpercentile = Dispatch ( (lscoreatpercentile, (list, tuple)), )
+percentileofscore = Dispatch ( (lpercentileofscore, (list, tuple)), )
+histogram = Dispatch ( (lhistogram, (list, tuple)), )
+cumfreq = Dispatch ( (lcumfreq, (list, tuple)), )
+relfreq = Dispatch ( (lrelfreq, (list, tuple)), )
## VARIABILITY:
-obrientransform = Dispatch ( (lobrientransform, (ListType, TupleType)), )
-samplevar = Dispatch ( (lsamplevar, (ListType, TupleType)), )
-samplestdev = Dispatch ( (lsamplestdev, (ListType, TupleType)), )
-var = Dispatch ( (lvar, (ListType, TupleType)), )
-stdev = Dispatch ( (lstdev, (ListType, TupleType)), )
-sterr = Dispatch ( (lsterr, (ListType, TupleType)), )
-sem = Dispatch ( (lsem, (ListType, TupleType)), )
-z = Dispatch ( (lz, (ListType, TupleType)), )
-zs = Dispatch ( (lzs, (ListType, TupleType)), )
+obrientransform = Dispatch ( (lobrientransform, (list, tuple)), )
+samplevar = Dispatch ( (lsamplevar, (list, tuple)), )
+samplestdev = Dispatch ( (lsamplestdev, (list, tuple)), )
+var = Dispatch ( (lvar, (list, tuple)), )
+stdev = Dispatch ( (lstdev, (list, tuple)), )
+sterr = Dispatch ( (lsterr, (list, tuple)), )
+sem = Dispatch ( (lsem, (list, tuple)), )
+z = Dispatch ( (lz, (list, tuple)), )
+zs = Dispatch ( (lzs, (list, tuple)), )
## TRIMMING FCNS:
-trimboth = Dispatch ( (ltrimboth, (ListType, TupleType)), )
-trim1 = Dispatch ( (ltrim1, (ListType, TupleType)), )
+trimboth = Dispatch ( (ltrimboth, (list, tuple)), )
+trim1 = Dispatch ( (ltrim1, (list, tuple)), )
## CORRELATION FCNS:
-paired = Dispatch ( (lpaired, (ListType, TupleType)), )
-pearsonr = Dispatch ( (lpearsonr, (ListType, TupleType)), )
-spearmanr = Dispatch ( (lspearmanr, (ListType, TupleType)), )
-pointbiserialr = Dispatch ( (lpointbiserialr, (ListType, TupleType)), )
-kendalltau = Dispatch ( (lkendalltau, (ListType, TupleType)), )
-linregress = Dispatch ( (llinregress, (ListType, TupleType)), )
+paired = Dispatch ( (lpaired, (list, tuple)), )
+pearsonr = Dispatch ( (lpearsonr, (list, tuple)), )
+spearmanr = Dispatch ( (lspearmanr, (list, tuple)), )
+pointbiserialr = Dispatch ( (lpointbiserialr, (list, tuple)), )
+kendalltau = Dispatch ( (lkendalltau, (list, tuple)), )
+linregress = Dispatch ( (llinregress, (list, tuple)), )
## INFERENTIAL STATS:
-ttest_1samp = Dispatch ( (lttest_1samp, (ListType, TupleType)), )
-ttest_ind = Dispatch ( (lttest_ind, (ListType, TupleType)), )
-ttest_rel = Dispatch ( (lttest_rel, (ListType, TupleType)), )
-chisquare = Dispatch ( (lchisquare, (ListType, TupleType)), )
-ks_2samp = Dispatch ( (lks_2samp, (ListType, TupleType)), )
-mannwhitneyu = Dispatch ( (lmannwhitneyu, (ListType, TupleType)), )
-ranksums = Dispatch ( (lranksums, (ListType, TupleType)), )
-tiecorrect = Dispatch ( (ltiecorrect, (ListType, TupleType)), )
-wilcoxont = Dispatch ( (lwilcoxont, (ListType, TupleType)), )
-kruskalwallish = Dispatch ( (lkruskalwallish, (ListType, TupleType)), )
-friedmanchisquare = Dispatch ( (lfriedmanchisquare, (ListType, TupleType)), )
+ttest_1samp = Dispatch ( (lttest_1samp, (list, tuple)), )
+ttest_ind = Dispatch ( (lttest_ind, (list, tuple)), )
+ttest_rel = Dispatch ( (lttest_rel, (list, tuple)), )
+chisquare = Dispatch ( (lchisquare, (list, tuple)), )
+ks_2samp = Dispatch ( (lks_2samp, (list, tuple)), )
+mannwhitneyu = Dispatch ( (lmannwhitneyu, (list, tuple)), )
+ranksums = Dispatch ( (lranksums, (list, tuple)), )
+tiecorrect = Dispatch ( (ltiecorrect, (list, tuple)), )
+wilcoxont = Dispatch ( (lwilcoxont, (list, tuple)), )
+kruskalwallish = Dispatch ( (lkruskalwallish, (list, tuple)), )
+friedmanchisquare = Dispatch ( (lfriedmanchisquare, (list, tuple)), )
## PROBABILITY CALCS:
-chisqprob = Dispatch ( (lchisqprob, (IntType, FloatType)), )
-zprob = Dispatch ( (lzprob, (IntType, FloatType)), )
-ksprob = Dispatch ( (lksprob, (IntType, FloatType)), )
-fprob = Dispatch ( (lfprob, (IntType, FloatType)), )
-betacf = Dispatch ( (lbetacf, (IntType, FloatType)), )
-betai = Dispatch ( (lbetai, (IntType, FloatType)), )
-erfcc = Dispatch ( (lerfcc, (IntType, FloatType)), )
-gammln = Dispatch ( (lgammln, (IntType, FloatType)), )
+chisqprob = Dispatch ( (lchisqprob, (int, float)), )
+zprob = Dispatch ( (lzprob, (int, float)), )
+ksprob = Dispatch ( (lksprob, (int, float)), )
+fprob = Dispatch ( (lfprob, (int, float)), )
+betacf = Dispatch ( (lbetacf, (int, float)), )
+betai = Dispatch ( (lbetai, (int, float)), )
+erfcc = Dispatch ( (lerfcc, (int, float)), )
+gammln = Dispatch ( (lgammln, (int, float)), )
## ANOVA FUNCTIONS:
-F_oneway = Dispatch ( (lF_oneway, (ListType, TupleType)), )
-F_value = Dispatch ( (lF_value, (ListType, TupleType)), )
+F_oneway = Dispatch ( (lF_oneway, (list, tuple)), )
+F_value = Dispatch ( (lF_value, (list, tuple)), )
## SUPPORT FUNCTIONS:
-incr = Dispatch ( (lincr, (ListType, TupleType)), )
-sum = Dispatch ( (lsum, (ListType, TupleType)), )
-cumsum = Dispatch ( (lcumsum, (ListType, TupleType)), )
-ss = Dispatch ( (lss, (ListType, TupleType)), )
-summult = Dispatch ( (lsummult, (ListType, TupleType)), )
-square_of_sums = Dispatch ( (lsquare_of_sums, (ListType, TupleType)), )
-sumdiffsquared = Dispatch ( (lsumdiffsquared, (ListType, TupleType)), )
-shellsort = Dispatch ( (lshellsort, (ListType, TupleType)), )
-rankdata = Dispatch ( (lrankdata, (ListType, TupleType)), )
-findwithin = Dispatch ( (lfindwithin, (ListType, TupleType)), )
+incr = Dispatch ( (lincr, (list, tuple)), )
+sum = Dispatch ( (lsum, (list, tuple)), )
+cumsum = Dispatch ( (lcumsum, (list, tuple)), )
+ss = Dispatch ( (lss, (list, tuple)), )
+summult = Dispatch ( (lsummult, (list, tuple)), )
+square_of_sums = Dispatch ( (lsquare_of_sums, (list, tuple)), )
+sumdiffsquared = Dispatch ( (lsumdiffsquared, (list, tuple)), )
+shellsort = Dispatch ( (lshellsort, (list, tuple)), )
+rankdata = Dispatch ( (lrankdata, (list, tuple)), )
+findwithin = Dispatch ( (lfindwithin, (list, tuple)), )
#============= THE ARRAY-VERSION OF THE STATS FUNCTIONS ===============
@@ -2019,7 +2018,7 @@ Returns: geometric mean computed over di
size = len(inarray)
mult = N.power(inarray,1.0/size)
mult = N.multiply.reduce(mult)
- elif type(dimension) in [IntType,FloatType]:
+ elif isinstance(dimension, (int, float)):
size = inarray.shape[dimension]
mult = N.power(inarray,1.0/size)
mult = N.multiply.reduce(mult,dimension)
@@ -2061,7 +2060,7 @@ Returns: harmonic mean computed over dim
inarray = N.ravel(inarray)
size = len(inarray)
s = N.add.reduce(1.0 / inarray)
- elif type(dimension) in [IntType,FloatType]:
+ elif isinstance(dimension, (int, float)):
size = float(inarray.shape[dimension])
s = N.add.reduce(1.0/inarray, dimension)
if keepdims == 1:
@@ -2116,7 +2115,7 @@ Returns: arithematic mean calculated ove
inarray = N.ravel(inarray)
sum = N.add.reduce(inarray)
denom = float(len(inarray))
- elif type(dimension) in [IntType,FloatType]:
+ elif isinstance(dimension, (int, float)):
sum = asum(inarray,dimension)
denom = float(inarray.shape[dimension])
if keepdims == 1:
@@ -2230,7 +2229,7 @@ Usage: atmean(a,limits=None,inclusive=
a = a.astype(N.float_)
if limits == None:
return mean(a)
- assert type(limits) in [ListType,TupleType,N.ndarray], "Wrong type for limits in atmean"
+ assert isinstance(limits, (list, tuple, N.ndarray)), "Wrong type for limits in atmean"
if inclusive[0]: lowerfcn = N.greater_equal
else: lowerfcn = N.greater
if inclusive[1]: upperfcn = N.less_equal
@@ -2262,7 +2261,7 @@ Usage: atvar(a,limits=None,inclusive=(
a = a.astype(N.float_)
if limits == None or limits == [None,None]:
return avar(a)
- assert type(limits) in [ListType,TupleType,N.ndarray], "Wrong type for limits in atvar"
+ assert isinstance(limits, (list, tuple, N.ndarray)), "Wrong type for limits in atvar"
if inclusive[0]: lowerfcn = N.greater_equal
else: lowerfcn = N.greater
if inclusive[1]: upperfcn = N.less_equal
@@ -2348,7 +2347,7 @@ Usage: atsem(a,limits=None,inclusive=(
if limits == None or limits == [None,None]:
n = float(len(N.ravel(a)))
limits = [min(a)-1, max(a)+1]
- assert type(limits) in [ListType,TupleType,N.ndarray], "Wrong type for limits in atsem"
+ assert isinstance(limits, (list, tuple, N.ndarray)), "Wrong type for limits in atsem"
if inclusive[0]: lowerfcn = N.greater_equal
else: lowerfcn = N.greater
if inclusive[1]: upperfcn = N.less_equal
@@ -2417,7 +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 type(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)
@@ -2436,7 +2435,7 @@ Returns: kurtosis of values in a along d
"""
denom = N.power(amoment(a,2,dimension),2)
zero = N.equal(denom,0)
- if type(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)
@@ -2722,7 +2721,7 @@ Usage: asamplevar(inarray,dimension=No
else:
mn = amean(inarray,dimension,keepdims=1)
deviations = inarray - mn
- if type(dimension) == ListType:
+ if isinstance(dimension, list):
n = 1
for d in dimension:
n = n*inarray.shape[d]
@@ -2778,7 +2777,7 @@ Usage: acov(x,y,dimension=None,keepdim
xdeviations = x - xmn
ymn = amean(y,dimension,1) # keepdims
ydeviations = y - ymn
- if type(dimension) == ListType:
+ if isinstance(dimension, list):
n = 1
for d in dimension:
n = n*x.shape[d]
@@ -2803,7 +2802,7 @@ Usage: avar(inarray,dimension=None,kee
dimension = 0
mn = amean(inarray,dimension,1)
deviations = inarray - mn
- if type(dimension) == ListType:
+ if isinstance(dimension, list):
n = 1
for d in dimension:
n = n*inarray.shape[d]
@@ -2855,7 +2854,7 @@ Usage: asem(inarray,dimension=None, ke
if dimension == None:
inarray = N.ravel(inarray)
dimension = 0
- if type(dimension) == ListType:
+ if isinstance(dimension, list):
n = 1
for d in dimension:
n = n*inarray.shape[d]
@@ -3319,7 +3318,7 @@ using the given writemode (default=appen
Usage: attest_1samp(a,popmean,Name='Sample',printit=0,writemode='a')
Returns: t-value, two-tailed prob
"""
- if type(a) != N.ndarray:
+ if not isinstance(a, N.ndarray):
a = N.array(a)
x = amean(a)
v = avar(a)
@@ -3370,15 +3369,15 @@ Returns: t-value, two-tailed p-value
t = N.where(zerodivproblem,1.0,t) # replace NaN/wrong t-values with 1.0
probs = abetai(0.5*df,0.5,float(df)/(df+t*t))
- if type(t) == N.ndarray:
+ if isinstance(t, N.ndarray):
probs = N.reshape(probs,t.shape)
if probs.shape == (1,):
probs = probs[0]
if printit <> 0:
- if type(t) == N.ndarray:
+ if isinstance(t, N.ndarray):
t = t[0]
- if type(probs) == N.ndarray:
+ if isinstance(probs, N.ndarray):
probs = probs[0]
statname = 'Independent samples T-test.'
outputpairedstats(printit,writemode,
@@ -3453,7 +3452,7 @@ Returns: t-value, two-tailed p-value
t = N.add.reduce(d,dimension) / denom # N-D COMPUTATION HERE!!!!!!
t = N.where(zerodivproblem,1.0,t) # replace NaN/wrong t-values with 1.0
probs = abetai(0.5*df,0.5,float(df)/(df+t*t))
- if type(t) == N.ndarray:
+ if isinstance(t, N.ndarray):
probs = N.reshape(probs,t.shape)
if probs.shape == (1,):
probs = probs[0]
@@ -3714,7 +3713,7 @@ Usage: achisqprob(chisq,df) chisq=c
exponents = N.where(N.less(x,-BIG),-BIG,x)
return N.exp(exponents)
- if type(chisq) == N.ndarray:
+ if isinstance(chisq, N.ndarray):
arrayflag = 1
else:
arrayflag = 0
@@ -3842,7 +3841,7 @@ Adapted from Numerical Recipies. Can ha
Usage: aksprob(alam)
"""
- if type(alam) == N.ndarray:
+ if isinstance(alam, N.ndarray):
frozen = -1 *N.ones(alam.shape,N.float64)
alam = alam.astype(N.float64)
arrayflag = 1
@@ -3885,7 +3884,7 @@ of freedom for the denominator (dfF). C
Usage: afprob(dfnum, dfden, F) where usually dfnum=dfbn, dfden=dfwn
"""
- if type(F) == N.ndarray:
+ if isinstance(F, N.ndarray):
return abetai(0.5*dfden, 0.5*dfnum, dfden/(1.0*dfden+dfnum*F))
else:
return abetai(0.5*dfden, 0.5*dfnum, dfden/float(dfden+dfnum*F))
@@ -3903,7 +3902,7 @@ Usage: abetacf(a,b,x,verbose=1)
EPS = 3.0e-7
arrayflag = 1
- if type(x) == N.ndarray:
+ if isinstance(x, N.ndarray):
frozen = N.ones(x.shape,N.float_) *-1 #start out w/ -1s, should replace all
else:
arrayflag = 0
@@ -3978,7 +3977,7 @@ C.) Can handle multiple dimensions.
Usage: abetai(a,b,x,verbose=1)
"""
TINY = 1e-15
- if type(a) == N.ndarray:
+ if isinstance(a, N.ndarray):
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)
@@ -3990,7 +3989,7 @@ Usage: abetai(a,b,x,verbose=1)
# 746 (below) is the MAX POSSIBLE BEFORE OVERFLOW
exponents = N.where(N.less(exponents,-740),-740,exponents)
bt = N.exp(exponents)
- if type(x) == N.ndarray:
+ if isinstance(x, N.ndarray):
ans = N.where(N.less(x,(a+1)/(a+b+2.0)),
bt*abetacf(a,b,x,verbose)/float(a),
1.0-bt*abetacf(b,a,1.0-x,verbose)/float(b))
@@ -4116,9 +4115,9 @@ Returns an F-statistic given the followi
dfF = degrees of freedom associated with the Restricted model
where ER and EF are matrices from a multivariate F calculation.
"""
- if type(ER) in [IntType, FloatType]:
+ if isinstance(ER, (int, float)):
ER = N.array([[ER]])
- if type(EF) in [IntType, FloatType]:
+ if isinstance(EF, (int, float)):
EF = N.array([[EF]])
n_um = (LA.det(ER) - LA.det(EF)) / float(dfnum)
d_en = LA.det(EF) / float(dfden)
@@ -4135,7 +4134,7 @@ Usage: asign(a)
Returns: array shape of a, with -1 where a<0 and +1 where a>=0
"""
a = N.asarray(a)
- if ((type(a) == type(1.4)) or (type(a) == type(1))):
+ if isinstance(a, (int, float)):
return a-a-N.less(a,0)+N.greater(a,0)
else:
return N.zeros(N.shape(a))-N.less(a,0)+N.greater(a,0)
@@ -4154,11 +4153,11 @@ dimensions as the input array.
Usage: asum(a, dimension=None, keepdims=0)
Returns: array summed along 'dimension'(s), same _number_ of dims if keepdims=1
"""
- if type(a) == N.ndarray and a.dtype in [N.int_, N.short, N.ubyte]:
+ if isinstance(a, N.ndarray) and a.dtype in [N.int_, N.short, N.ubyte]:
a = a.astype(N.float_)
if dimension == None:
s = N.sum(N.ravel(a))
- elif type(dimension) in [IntType,FloatType]:
+ elif isinstance(dimension, (int, float)):
s = N.add.reduce(a, dimension)
if keepdims == 1:
shp = list(a.shape)
@@ -4191,7 +4190,7 @@ Usage: acumsum(a,dimension=None)
if dimension == None:
a = N.ravel(a)
dimension = 0
- if type(dimension) in [ListType, TupleType, N.ndarray]:
+ if isinstance(dimension, (list, tuple, N.ndarray)):
dimension = list(dimension)
dimension.sort()
dimension.reverse()
@@ -4252,7 +4251,7 @@ Returns: the square of the sum over dim(
inarray = N.ravel(inarray)
dimension = 0
s = asum(inarray,dimension,keepdims)
- if type(s) == N.ndarray:
+ if isinstance(s, N.ndarray):
return s.astype(N.float_)*s
else:
return float(s)*s
@@ -4350,17 +4349,17 @@ Usage: afindwithin(data) data in |
#########################################################
## CENTRAL TENDENCY:
- geometricmean = Dispatch ( (lgeometricmean, (ListType, TupleType)),
+ geometricmean = Dispatch ( (lgeometricmean, (list, tuple)),
(ageometricmean, (N.ndarray,)) )
- harmonicmean = Dispatch ( (lharmonicmean, (ListType, TupleType)),
+ harmonicmean = Dispatch ( (lharmonicmean, (list, tuple)),
(aharmonicmean, (N.ndarray,)) )
- mean = Dispatch ( (lmean, (ListType, TupleType)),
+ mean = Dispatch ( (lmean, (list, tuple)),
(amean, (N.ndarray,)) )
- median = Dispatch ( (lmedian, (ListType, TupleType)),
+ median = Dispatch ( (lmedian, (list, tuple)),
(amedian, (N.ndarray,)) )
- medianscore = Dispatch ( (lmedianscore, (ListType, TupleType)),
+ medianscore = Dispatch ( (lmedianscore, (list, tuple)),
(amedianscore, (N.ndarray,)) )
- mode = Dispatch ( (lmode, (ListType, TupleType)),
+ mode = Dispatch ( (lmode, (list, tuple)),
(amode, (N.ndarray,)) )
tmean = Dispatch ( (atmean, (N.ndarray,)) )
tvar = Dispatch ( (atvar, (N.ndarray,)) )
@@ -4368,151 +4367,151 @@ Usage: afindwithin(data) data in |
tsem = Dispatch ( (atsem, (N.ndarray,)) )
## VARIATION:
- moment = Dispatch ( (lmoment, (ListType, TupleType)),
+ moment = Dispatch ( (lmoment, (list, tuple)),
(amoment, (N.ndarray,)) )
- variation = Dispatch ( (lvariation, (ListType, TupleType)),
+ variation = Dispatch ( (lvariation, (list, tuple)),
(avariation, (N.ndarray,)) )
- skew = Dispatch ( (lskew, (ListType, TupleType)),
+ skew = Dispatch ( (lskew, (list, tuple)),
(askew, (N.ndarray,)) )
- kurtosis = Dispatch ( (lkurtosis, (ListType, TupleType)),
+ kurtosis = Dispatch ( (lkurtosis, (list, tuple)),
(akurtosis, (N.ndarray,)) )
- describe = Dispatch ( (ldescribe, (ListType, TupleType)),
+ describe = Dispatch ( (ldescribe, (list, tuple)),
(adescribe, (N.ndarray,)) )
## DISTRIBUTION TESTS
- skewtest = Dispatch ( (askewtest, (ListType, TupleType)),
+ skewtest = Dispatch ( (askewtest, (list, tuple)),
(askewtest, (N.ndarray,)) )
- kurtosistest = Dispatch ( (akurtosistest, (ListType, TupleType)),
+ kurtosistest = Dispatch ( (akurtosistest, (list, tuple)),
(akurtosistest, (N.ndarray,)) )
- normaltest = Dispatch ( (anormaltest, (ListType, TupleType)),
+ normaltest = Dispatch ( (anormaltest, (list, tuple)),
(anormaltest, (N.ndarray,)) )
## FREQUENCY STATS:
- itemfreq = Dispatch ( (litemfreq, (ListType, TupleType)),
+ itemfreq = Dispatch ( (litemfreq, (list, tuple)),
(aitemfreq, (N.ndarray,)) )
- scoreatpercentile = Dispatch ( (lscoreatpercentile, (ListType, TupleType)),
+ scoreatpercentile = Dispatch ( (lscoreatpercentile, (list, tuple)),
(ascoreatpercentile, (N.ndarray,)) )
- percentileofscore = Dispatch ( (lpercentileofscore, (ListType, TupleType)),
+ percentileofscore = Dispatch ( (lpercentileofscore, (list, tuple)),
(apercentileofscore, (N.ndarray,)) )
- histogram = Dispatch ( (lhistogram, (ListType, TupleType)),
+ histogram = Dispatch ( (lhistogram, (list, tuple)),
(ahistogram, (N.ndarray,)) )
- cumfreq = Dispatch ( (lcumfreq, (ListType, TupleType)),
+ cumfreq = Dispatch ( (lcumfreq, (list, tuple)),
(acumfreq, (N.ndarray,)) )
- relfreq = Dispatch ( (lrelfreq, (ListType, TupleType)),
+ relfreq = Dispatch ( (lrelfreq, (list, tuple)),
(arelfreq, (N.ndarray,)) )
## VARIABILITY:
- obrientransform = Dispatch ( (lobrientransform, (ListType, TupleType)),
+ obrientransform = Dispatch ( (lobrientransform, (list, tuple)),
(aobrientransform, (N.ndarray,)) )
- samplevar = Dispatch ( (lsamplevar, (ListType, TupleType)),
+ samplevar = Dispatch ( (lsamplevar, (list, tuple)),
(asamplevar, (N.ndarray,)) )
- samplestdev = Dispatch ( (lsamplestdev, (ListType, TupleType)),
+ samplestdev = Dispatch ( (lsamplestdev, (list, tuple)),
(asamplestdev, (N.ndarray,)) )
signaltonoise = Dispatch( (asignaltonoise, (N.ndarray,)),)
- var = Dispatch ( (lvar, (ListType, TupleType)),
+ var = Dispatch ( (lvar, (list, tuple)),
(avar, (N.ndarray,)) )
- stdev = Dispatch ( (lstdev, (ListType, TupleType)),
+ stdev = Dispatch ( (lstdev, (list, tuple)),
(astdev, (N.ndarray,)) )
- sterr = Dispatch ( (lsterr, (ListType, TupleType)),
+ sterr = Dispatch ( (lsterr, (list, tuple)),
(asterr, (N.ndarray,)) )
- sem = Dispatch ( (lsem, (ListType, TupleType)),
+ sem = Dispatch ( (lsem, (list, tuple)),
(asem, (N.ndarray,)) )
- z = Dispatch ( (lz, (ListType, TupleType)),
+ z = Dispatch ( (lz, (list, tuple)),
(az, (N.ndarray,)) )
- zs = Dispatch ( (lzs, (ListType, TupleType)),
+ zs = Dispatch ( (lzs, (list, tuple)),
(azs, (N.ndarray,)) )
## TRIMMING FCNS:
threshold = Dispatch( (athreshold, (N.ndarray,)),)
- trimboth = Dispatch ( (ltrimboth, (ListType, TupleType)),
+ trimboth = Dispatch ( (ltrimboth, (list, tuple)),
(atrimboth, (N.ndarray,)) )
- trim1 = Dispatch ( (ltrim1, (ListType, TupleType)),
+ trim1 = Dispatch ( (ltrim1, (list, tuple)),
(atrim1, (N.ndarray,)) )
## CORRELATION FCNS:
- paired = Dispatch ( (lpaired, (ListType, TupleType)),
+ paired = Dispatch ( (lpaired, (list, tuple)),
(apaired, (N.ndarray,)) )
- lincc = Dispatch ( (llincc, (ListType, TupleType)),
+ lincc = Dispatch ( (llincc, (list, tuple)),
(alincc, (N.ndarray,)) )
- pearsonr = Dispatch ( (lpearsonr, (ListType, TupleType)),
+ pearsonr = Dispatch ( (lpearsonr, (list, tuple)),
(apearsonr, (N.ndarray,)) )
- spearmanr = Dispatch ( (lspearmanr, (ListType, TupleType)),
+ spearmanr = Dispatch ( (lspearmanr, (list, tuple)),
(aspearmanr, (N.ndarray,)) )
- pointbiserialr = Dispatch ( (lpointbiserialr, (ListType, TupleType)),
+ pointbiserialr = Dispatch ( (lpointbiserialr, (list, tuple)),
(apointbiserialr, (N.ndarray,)) )
- kendalltau = Dispatch ( (lkendalltau, (ListType, TupleType)),
+ kendalltau = Dispatch ( (lkendalltau, (list, tuple)),
(akendalltau, (N.ndarray,)) )
- linregress = Dispatch ( (llinregress, (ListType, TupleType)),
+ linregress = Dispatch ( (llinregress, (list, tuple)),
(alinregress, (N.ndarray,)) )
## INFERENTIAL STATS:
- ttest_1samp = Dispatch ( (lttest_1samp, (ListType, TupleType)),
+ ttest_1samp = Dispatch ( (lttest_1samp, (list, tuple)),
(attest_1samp, (N.ndarray,)) )
- ttest_ind = Dispatch ( (lttest_ind, (ListType, TupleType)),
+ ttest_ind = Dispatch ( (lttest_ind, (list, tuple)),
(attest_ind, (N.ndarray,)) )
- ttest_rel = Dispatch ( (lttest_rel, (ListType, TupleType)),
+ ttest_rel = Dispatch ( (lttest_rel, (list, tuple)),
(attest_rel, (N.ndarray,)) )
- chisquare = Dispatch ( (lchisquare, (ListType, TupleType)),
+ chisquare = Dispatch ( (lchisquare, (list, tuple)),
(achisquare, (N.ndarray,)) )
- ks_2samp = Dispatch ( (lks_2samp, (ListType, TupleType)),
+ ks_2samp = Dispatch ( (lks_2samp, (list, tuple)),
(aks_2samp, (N.ndarray,)) )
- mannwhitneyu = Dispatch ( (lmannwhitneyu, (ListType, TupleType)),
+ mannwhitneyu = Dispatch ( (lmannwhitneyu, (list, tuple)),
(amannwhitneyu, (N.ndarray,)) )
- tiecorrect = Dispatch ( (ltiecorrect, (ListType, TupleType)),
+ tiecorrect = Dispatch ( (ltiecorrect, (list, tuple)),
(atiecorrect, (N.ndarray,)) )
- ranksums = Dispatch ( (lranksums, (ListType, TupleType)),
+ ranksums = Dispatch ( (lranksums, (list, tuple)),
(aranksums, (N.ndarray,)) )
- wilcoxont = Dispatch ( (lwilcoxont, (ListType, TupleType)),
+ wilcoxont = Dispatch ( (lwilcoxont, (list, tuple)),
(awilcoxont, (N.ndarray,)) )
- kruskalwallish = Dispatch ( (lkruskalwallish, (ListType, TupleType)),
+ kruskalwallish = Dispatch ( (lkruskalwallish, (list, tuple)),
(akruskalwallish, (N.ndarray,)) )
- friedmanchisquare = Dispatch ( (lfriedmanchisquare, (ListType, TupleType)),
+ friedmanchisquare = Dispatch ( (lfriedmanchisquare, (list, tuple)),
(afriedmanchisquare, (N.ndarray,)) )
## PROBABILITY CALCS:
- chisqprob = Dispatch ( (lchisqprob, (IntType, FloatType)),
+ chisqprob = Dispatch ( (lchisqprob, (int, float)),
(achisqprob, (N.ndarray,)) )
- zprob = Dispatch ( (lzprob, (IntType, FloatType)),
+ zprob = Dispatch ( (lzprob, (int, float)),
(azprob, (N.ndarray,)) )
- ksprob = Dispatch ( (lksprob, (IntType, FloatType)),
+ ksprob = Dispatch ( (lksprob, (int, float)),
(aksprob, (N.ndarray,)) )
- fprob = Dispatch ( (lfprob, (IntType, FloatType)),
+ fprob = Dispatch ( (lfprob, (int, float)),
(afprob, (N.ndarray,)) )
- betacf = Dispatch ( (lbetacf, (IntType, FloatType)),
+ betacf = Dispatch ( (lbetacf, (int, float)),
(abetacf, (N.ndarray,)) )
- betai = Dispatch ( (lbetai, (IntType, FloatType)),
+ betai = Dispatch ( (lbetai, (int, float)),
(abetai, (N.ndarray,)) )
- erfcc = Dispatch ( (lerfcc, (IntType, FloatType)),
+ erfcc = Dispatch ( (lerfcc, (int, float)),
(aerfcc, (N.ndarray,)) )
- gammln = Dispatch ( (lgammln, (IntType, FloatType)),
+ gammln = Dispatch ( (lgammln, (int, float)),
(agammln, (N.ndarray,)) )
## ANOVA FUNCTIONS:
- F_oneway = Dispatch ( (lF_oneway, (ListType, TupleType)),
+ F_oneway = Dispatch ( (lF_oneway, (list, tuple)),
(aF_oneway, (N.ndarray,)) )
- F_value = Dispatch ( (lF_value, (ListType, TupleType)),
+ F_value = Dispatch ( (lF_value, (list, tuple)),
(aF_value, (N.ndarray,)) )
## SUPPORT FUNCTIONS:
- incr = Dispatch ( (lincr, (ListType, TupleType, N.ndarray)), )
- sum = Dispatch ( (lsum, (ListType, TupleType)),
+ incr = Dispatch ( (lincr, (list, tuple, N.ndarray)), )
+ sum = Dispatch ( (lsum, (list, tuple)),
(asum, (N.ndarray,)) )
- cumsum = Dispatch ( (lcumsum, (ListType, TupleType)),
+ cumsum = Dispatch ( (lcumsum, (list, tuple)),
(acumsum, (N.ndarray,)) )
- ss = Dispatch ( (lss, (ListType, TupleType)),
+ ss = Dispatch ( (lss, (list, tuple)),
(ass, (N.ndarray,)) )
- summult = Dispatch ( (lsummult, (ListType, TupleType)),
+ summult = Dispatch ( (lsummult, (list, tuple)),
(asummult, (N.ndarray,)) )
- square_of_sums = Dispatch ( (lsquare_of_sums, (ListType, TupleType)),
+ square_of_sums = Dispatch ( (lsquare_of_sums, (list, tuple)),
(asquare_of_sums, (N.ndarray,)) )
- sumdiffsquared = Dispatch ( (lsumdiffsquared, (ListType, TupleType)),
+ sumdiffsquared = Dispatch ( (lsumdiffsquared, (list, tuple)),
(asumdiffsquared, (N.ndarray,)) )
- shellsort = Dispatch ( (lshellsort, (ListType, TupleType)),
+ shellsort = Dispatch ( (lshellsort, (list, tuple)),
(ashellsort, (N.ndarray,)) )
- rankdata = Dispatch ( (lrankdata, (ListType, TupleType)),
+ rankdata = Dispatch ( (lrankdata, (list, tuple)),
(arankdata, (N.ndarray,)) )
- findwithin = Dispatch ( (lfindwithin, (ListType, TupleType)),
+ findwithin = Dispatch ( (lfindwithin, (list, tuple)),
(afindwithin, (N.ndarray,)) )
###################### END OF NUMERIC FUNCTION BLOCK #####################
Modified: lnt/trunk/lnt/server/db/regression.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/server/db/regression.py?rev=371944&r1=371943&r2=371944&view=diff
==============================================================================
--- lnt/trunk/lnt/server/db/regression.py (original)
+++ lnt/trunk/lnt/server/db/regression.py Sun Sep 15 16:01:06 2019
@@ -47,7 +47,7 @@ def new_regression(session, ts, field_ch
session.add(regression)
new_ris = []
for fc_id in field_changes:
- if type(fc_id) == int:
+ if isinstance(fc_id, int):
fc = get_fieldchange(session, ts, fc_id)
else:
fc = fc_id
Modified: lnt/trunk/lnt/server/ui/api.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/server/ui/api.py?rev=371944&r1=371943&r2=371944&view=diff
==============================================================================
--- lnt/trunk/lnt/server/ui/api.py (original)
+++ lnt/trunk/lnt/server/ui/api.py Sun Sep 15 16:01:06 2019
@@ -30,10 +30,10 @@ def requires_auth_token(f):
def with_ts(obj):
"""For Url type fields to work, the objects we return must have a test-suite
and database attribute set, the function attempts to set them."""
- if type(obj) == list:
+ if isinstance(obj, list):
# For lists, set them on all elements.
return [with_ts(x) for x in obj]
- if type(obj) == dict:
+ if isinstance(obj, dict):
# If already a dict, just add the fields.
new_obj = obj
else:
Modified: lnt/trunk/lnt/server/ui/app.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/server/ui/app.py?rev=371944&r1=371943&r2=371944&view=diff
==============================================================================
--- lnt/trunk/lnt/server/ui/app.py (original)
+++ lnt/trunk/lnt/server/ui/app.py Sun Sep 15 16:01:06 2019
@@ -57,7 +57,7 @@ class LNTObjectJSONEncoder(flask.json.JS
def default(self, obj):
if hasattr(obj, '__json__'):
return obj.__json__()
- if type(obj) is datetime.datetime:
+ if isinstance(obj, datetime.datetime):
return obj.isoformat()
if isinstance(obj.__class__, DeclarativeMeta):
fields = {}
Modified: lnt/trunk/lnt/tests/nt.py
URL: http://llvm.org/viewvc/llvm-project/lnt/trunk/lnt/tests/nt.py?rev=371944&r1=371943&r2=371944&view=diff
==============================================================================
--- lnt/trunk/lnt/tests/nt.py (original)
+++ lnt/trunk/lnt/tests/nt.py Sun Sep 15 16:01:06 2019
@@ -100,7 +100,7 @@ class TestConfiguration(object):
opts -- the command line options object
start_time -- the time the program was invoked as a string
"""
- assert type(opts) == dict, "Options must be a dict."
+ assert isinstance(opts, dict), "Options must be a dict."
self.opts = opts
self.__dict__.update(opts)
self.start_time = start_time
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