def handle(self,tbl,opt):
if opt.diff is None:
return
if os.path.isdir(opt.diff):
trylocs = [os.path.join(opt.diff,os.path.basename(opt.input))]
if not os.path.isabs(opt.input) and os.path.dirname(opt.input):
trylocs.append(os.path.join(opt.diff,opt.input))
for other in trylocs:
if os.path.exists(other):
break
else:
other = opt.diff
# prepare labels, giving preference to basenames if unique
filename = [other,opt.input]
label = [os.path.basename(x) for x in filename]
if label[0] == label[1]:
label = filename
ref = UniTable().fromfile(other)
out = ref.diff(tbl,label1=label[0],label2=label[1],dump_sample=opt.diff_dump)
if out:
print '***** files differ: %s %s' % tuple(filename)
print out
print
# allow multi-file processing
# sys.exit(1)
return False
def handle(self, tbl, opt):
if opt.diff is None:
return
if os.path.isdir(opt.diff):
trylocs = [os.path.join(opt.diff, os.path.basename(opt.input))]
if not os.path.isabs(opt.input) and os.path.dirname(opt.input):
trylocs.append(os.path.join(opt.diff, opt.input))
for other in trylocs:
if os.path.exists(other):
break
else:
other = opt.diff
# prepare labels, giving preference to basenames if unique
filename = [other, opt.input]
label = [os.path.basename(x) for x in filename]
if label[0] == label[1]:
label = filename
ref = UniTable().fromfile(other)
out = ref.diff(tbl,
label1=label[0],
label2=label[1],
dump_sample=opt.diff_dump)
if out:
print '***** files differ: %s %s' % tuple(filename)
print out
print
# allow multi-file processing
# sys.exit(1)
return False
def run_benchmark_iter(self,sizes=None):
'''return a table of runtimes for all functions for a range of data sizes'''
group = self.group
if sizes is None:
sizes = group.bench_sizes
rawdata = self.make_data(group.nin,group.itypes,max(sizes))
out = group._prep_testdata(*rawdata)
if isinstance(out,tuple):
alldata,kwargs = out
else:
alldata,kwargs = out,{}
names = [name for (name,func) in self.testfuncs]
perfseen = UniTable(keys=['_n_']+names)
skiplist = []
for i,size in enumerate(sizes):
perf = {'_n_':size}
reference = None
for name,func in self.testfuncs:
if name in skiplist:
perf[name] = self.NA
continue
timings,results = self.run_tests(func,alldata,[size],kwargs=kwargs)
rate = timings[0]
perf[name] = rate
if self.too_long(rate=rate):
skiplist.append(name)
if reference is None:
reference = results
elif results is not None:
self.compare_data(reference,results,name)
perfseen.append(perf)
yield perfseen.copy()
def __init__(self, rules=None, *args, **kwargs):
UniTable.__init__(self, *args, **kwargs)
if not isinstance(rules, Rules):
rules = Rules(rules)
self._rules = rules
self._rulearg = self.as_mapping()
self._made = {}
self._inprog = []
def handle(self, tbl, opt):
if opt.add_index is None:
return
out = UniTable()
out[opt.add_index] = range(len(tbl))
out.update(tbl)
return out
def __init__(self, rules=None, *args, **kwargs):
UniTable.__init__(self, *args, **kwargs)
if not isinstance(rules, Rules):
rules = Rules(rules)
self._rules = rules
self._rulearg = self.as_mapping()
self._made = {}
self._inprog = []
def handle(self,tbl,opt):
if opt.add_index is None:
return
out = UniTable()
out[opt.add_index] = range(len(tbl))
out.update(tbl)
return out
def run_benchmark(self,sizes=None):
'''return a table of runtimes for all functions for a range of data sizes'''
group = self.group
if sizes is None:
sizes = group.bench_sizes
performance = UniTable()
performance['_n_'] = sizes
rawdata = self.make_data(group.nin,group.itypes,max(sizes))
out = group._prep_testdata(*rawdata)
if isinstance(out,tuple):
alldata,kwargs = out
else:
alldata,kwargs = out,{}
reference = None
for (name,func) in self.testfuncs:
timings,results = self.run_tests(func,alldata,sizes,kwargs=kwargs)
if len(timings) == len(performance):
performance[name] = timings
else:
missing = len(performance)-len(timings)
performance[name] = list(timings) + [self.NA]*missing
results = list(results) + [None]*missing
if reference is None:
reference = results
elif results is not None:
self.compare_data(reference,results,name)
return performance
def __init__(self,filename=None,keys=[]):
self.keys = keys
self.data = data = {'':0} # try to pre-assign empty string value
self.filename = filename
if filename and os.path.exists(filename):
from unitable import UniTable
tbl = UniTable().fromfile(filename)
for i,value in it.izip(tbl['index'],tbl['data']):
data[value] = i
del tbl
def fld_summary(tbl):
out = UniTable()
keys = tbl.keys()
out['@'] = range(1,len(keys)+1)
out['field'] = keys
out['typecode'] = [re.sub(r'^a(.*)',r'Char(\1)',fmt) for fmt in tbl.get_type_codes()]
out['size'] = [col.itemsize() for col in tbl.values()]
nmasked = [count_masked(col) for col in tbl.values()]
if sum(nmasked):
out['masked'] = [str(n) for n in nmasked]
masked = out['masked']
masked[masked == '0'] = ''
return out
def flush(self):
if self.filename and len(self.data) > 1:
from unitable import UniTable
tbl = UniTable(keys=['index','data'])
tmp = self.data.items()
tbl['index'] = [x[1] for x in tmp]
tbl['data'] = [x[0] for x in tmp]
del tmp
tbl.sort_on('index')
tbl.to_csv_file(self.filename)
del tbl
def handle(tbl, opt):
if opt.expand is None:
return
cntkey = opt.expand
keys = tbl.keys()[:]
try:
keys.remove(cntkey)
except ValueError:
log.warning('cannot expand table, has no count column: %r', cntkey)
return
ntbl = UniTable(keys=keys)
xcnt = tbl[cntkey]
for key in keys:
oval = tbl[key]
nval = []
for cnt, val in it.izip(xcnt, oval):
for i in range(cnt):
nval.append(val)
ntbl[key] = nval
return ntbl