def _handle_ts_or_scalar(op_name, ts1, ts2, all = True, fill = None, name = None):
'''
this is the main entry point for any arithmetic type function performed on a timeseries
and/or a scalar.
op_name - name of the function to be performed
ts1, ts2 - timeseries or scalars that the function is to performed over
all - whether all dates should be included in the result
fill - the value that should be used to represent "missing values"
name - the name of the resulting time series
'''
from dynts import istimeseries
op = _get_op(op_name)
fill = fill if fill is not None else settings.missing_value
if hasattr(fill,'__call__'):
fill_fn = fill
else:
fill_fn = lambda : fill
name = name or '%s(%s,%s)' % (op_name,ts1,ts2)
ts = None
if istimeseries(ts1):
ts = ts1
if istimeseries(ts2):
dts, data = _handle_ts_ts(op_name, op, ts1, ts2, all, fill_fn)
else:
dts, data = _handle_ts_scalar(op_name, op, ts1, ts2, fill_fn)
else:
if istimeseries(ts2):
ts = ts2
dts, data = _handle_scalar_ts(op_name, op, ts1, ts2, fill_fn)
else:
return op(ts1,ts2)
return ts.clone(date = dts, data = data, name = name)
def merge(self, tserie, fill=nan, **kwargs):
if dynts.istimeseries(tserie):
tserie = [tserie]
else:
tserie = tuple(tserie)
alldates = set(self.dates())
hash = self.ashash()
namespace = self.namespace
with_namespace = False
for ts in tserie:
if ts.namespace != namespace:
with_namespace = True
break
thashes = [(hash, np.array([fill]*self.count()))]
names = self.names(with_namespace)
for ts in tserie:
alldates = alldates.union(ts.dates())
names.extend(ts.names(with_namespace))
thashes.append((ts.ashash(),np.array([fill]*ts.count())))
hash.names = names
stack = np.hstack
mdt = lambda dt: stack((h.get(dt,ln) for h,ln in thashes))
for dt in alldates:
hash[dt] = mdt(dt)
return hash.getts()
def merge(self, ts, all = True):
if dynts.istimeseries(ts):
return self._mergesingle(ts, all = all)
else:
rs = self
for t in ts:
rs = rs._mergesingle(t, all = all)
return rs
def testNoParameters(self):
'''Test mean function with zero parameters'''
expression = '%s(GOOG)' % self.function
result = dynts.evaluate(expression)
self.assertEqual(str(result),expression)
self.assertEqual(len(result.data),1)
data = result.ts()
self.assertTrue(dynts.istimeseries(data))
self.assertEqual(data.count(),1)
def testTwoSeries(self):
e = '%s(GOOG,YHOO)' % self.function
result = dynts.evaluate(e)
symbols = result.expression.symbols()
self.assertEqual(len(symbols), 2)
self.assertEqual(len(result.data), 2)
data = result.ts()
self.assertTrue(dynts.istimeseries(data))
self.assertEqual(data.count(), 2)
def testNoParameters(self):
'''Test mean function with zero parameters'''
expression = '%s(GOOG)' % self.function
result = dynts.evaluate(expression)
self.assertEqual(str(result), expression)
self.assertEqual(len(result.data), 1)
data = result.ts()
self.assertTrue(dynts.istimeseries(data))
self.assertEqual(data.count(), 1)
def testTwoSeries(self):
e = '%s(GOOG, YHOO)' % self.function
result = dynts.evaluate(e)
symbols = result.expression.symbols()
self.assertEqual(len(symbols), 2)
self.assertEqual(len(result.data), 2)
data = result.ts()
self.assertTrue(dynts.istimeseries(data))
self.assertEqual(data.count(), 2)
def testWindowParameter(self):
'''Test mean function with one parameter'''
e = '%(f)s(GOOG,window=30),%(f)s(GOOG,window=60)' % {'f':self.function}
result = dynts.evaluate(e)
symbols = result.expression.symbols()
self.assertEqual(len(symbols),1)
self.assertEqual(len(result.data),1)
data = result.ts()
self.assertTrue(dynts.istimeseries(data))
self.assertEqual(data.count(),2)
def testDataProvider(self):
result = dynts.evaluate('2*GOOG, GOOG')
self.assertEqual(len(result.data), 1)
self.assertEqual(result.expression, dynts.parse('2*GOOG, GOOG'))
data = result.ts()
self.assertTrue(dynts.istimeseries(data))
self.assertEqual(data.count(), 2)
ts1 = data.serie(0)
ts2 = data.serie(1)
for v1, v2 in zip(ts1, ts2):
self.assertAlmostEqual(v1, 2. * v2)
def testDataProvider(self):
result = dynts.evaluate('2*GOOG,GOOG')
self.assertEqual(len(result.data),1)
self.assertEqual(result.expression,dynts.parse('2*GOOG,GOOG'))
data = result.ts()
self.assertTrue(dynts.istimeseries(data))
self.assertEqual(data.count(),2)
ts1 = data.serie(0)
ts2 = data.serie(1)
for v1,v2 in zip(ts1,ts2):
self.assertAlmostEqual(v1,2.*v2)
def testBinOpSerieFunction(self):
'''Get a timeseries and a function and check for consistency'''
res = dynts.parse('goog:yahoo-ma(goog:yahoo, window=30)')
self.assertEqual(res.symbols(), ['GOOG:YAHOO'])
self.assertEqual(len(res), 2)
self.assertEqual(str(res[0]), 'GOOG:YAHOO')
self.assertEqual(str(res[1]), 'ma(GOOG:YAHOO, window=30)')
result = dynts.evaluate(res)
self.assertEqual(len(result.data), 1)
data = result.ts()
self.assertTrue(dynts.istimeseries(data))
self.assertEqual(data.count(), 1)
def testBinOpSerieFunction(self):
'''Get a timeseries and a function and check for consistency'''
res = dynts.parse('goog:yahoo-ma(goog:yahoo,window=30)')
self.assertEqual(res.symbols(),['GOOG:YAHOO'])
self.assertEqual(len(res),2)
self.assertEqual(str(res[0]),'GOOG:YAHOO')
self.assertEqual(str(res[1]),'ma(GOOG:YAHOO,window=30)')
result = dynts.evaluate(res)
self.assertEqual(len(result.data),1)
data = result.ts()
self.assertTrue(dynts.istimeseries(data))
self.assertEqual(data.count(),1)
def testWindowParameter(self):
'''Test mean function with one parameter'''
e = '%(f)s(GOOG,window=30),%(f)s(GOOG,window=60)' % {'f':self.function}
result = dynts.evaluate(e)
symbols = result.expression.symbols()
self.assertEqual(len(symbols),1)
self.assertEqual(len(result.data),1)
ts = result.ts()
self.assertTrue(dynts.istimeseries(ts))
self.assertEqual(ts.count(),2)
back = ts[-1]
self.assertEqual(len(back),2)
self.assertEqual(back[0],back[0])
self.assertEqual(back[-1],back[-1])
def merge(self, tserie, fill = float("nan"), **kwargs):
if dynts.istimeseries(tserie):
tserie = [tserie]
alldates = set(self.dates())
hash = self.ashash()
thashes = [(hash,np.array([fill]*self.count()))]
for ts in tserie:
alldates = alldates.union(ts.dates())
hash.names.extend(ts.names())
thashes.append((ts.ashash(),np.array([fill]*ts.count())))
stack = np.hstack
mdt = lambda dt: stack((h.get(dt,ln) for h,ln in thashes))
for dt in alldates:
hash[dt] = mdt(dt)
return hash.getts()
def _unwind(self, values, backend, **kwargs):
sdata = values[self.value]
if istimeseries(sdata):
return sdata
else:
ts = timeseries(name=str(self),
date=sdata['date'],
data=sdata['value'],
backend=backend)
# Uses this hack to make sure timeseries are ordered
# Lots of room for performance improvement
hash = ts.ashash()
hash.modified = True
values[ts.name] = hash.getts()
return ts
def merge(self, tserie, fill=float("nan"), **kwargs):
if dynts.istimeseries(tserie):
tserie = [tserie]
alldates = set(self.dates())
hash = self.ashash()
thashes = [(hash, np.array([fill] * self.count()))]
for ts in tserie:
alldates = alldates.union(ts.dates())
hash.names.extend(ts.names())
thashes.append((ts.ashash(), np.array([fill] * ts.count())))
stack = np.hstack
mdt = lambda dt: stack((h.get(dt, ln) for h, ln in thashes))
for dt in alldates:
hash[dt] = mdt(dt)
return hash.getts()
def testWindowParameter(self):
'''Test mean function with one parameter'''
e = ('%(f)s(GOOG, window=30), %(f)s(GOOG, window=60)' % {
'f': self.function
})
result = dynts.evaluate(e)
symbols = result.expression.symbols()
self.assertEqual(len(symbols), 1)
self.assertEqual(len(result.data), 1)
ts = result.ts()
self.assertTrue(dynts.istimeseries(ts))
self.assertEqual(ts.count(), 2)
back = ts[-1]
self.assertEqual(len(back), 2)
self.assertEqual(back[0], back[0])
self.assertEqual(back[-1], back[-1])
def _unwind(self, values, backend, **kwargs):
sdata = values[self.value]
if istimeseries(sdata):
return sdata
else:
ts = timeseries(name = str(self),
date = sdata['date'],
data = sdata['value'],
backend = backend)
# Uses this hack to make sure timeseries are ordered
# Lots of room for performance improvement
hash = ts.ashash()
hash.modified = True
ts = hash.getts()
values[ts.name] = ts
return ts
