def testVar(self): '''Calculate the biased variance of a series''' ts = dynts.timeseries(date=datepopulate(10), data=range(1, 11), backend=self.backend) self.assertAlmostEqual(ts.var()[0], 8.25, places) self.assertAlmostEqual(ts.var(ddof=1)[0], 9.166667, places)
def testHashEmpty(self): ts = timeseries(dtype=object) h = ts.ashash() self.assertFalse(h) dt1 = date.today() dt0 = date.today() - timedelta(days=1) h[dt1] = [56, 48] h[dt0] = 'this is a string' self.assertEqual(len(h), 2) ts = h.getts() self.assertEqual(len(ts), 2) self.assertEqual(ts[0][0], 'this is a string') self.assertEqual(ts[1][0], [56, 48])
def testHashEmpty(self): ts = timeseries(dtype = object) h = ts.ashash() self.assertFalse(h) dt1 = date.today() dt0 = date.today()-timedelta(days=1) h[dt1] = [56,48] h[dt0] = 'this is a string' self.assertEqual(len(h),2) ts = h.getts() self.assertEqual(len(ts),2) self.assertEqual(ts[0][0], 'this is a string') self.assertEqual(ts[1][0], [56,48])
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 _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
def load_data(self, result): loads = self.pickler.loads vloads = self.value_pickler.loads dt, va = result if result[0] and va: dates = ny.array([loads(t) for t in dt]) fields = [] vals = [] if not isinstance(va, Mapping): va = dict(va) for f in sorted(va): fields.append(f) data = va[f] vals.append((vloads(v) for v in data)) values = ny.array(list(zip(*vals))) name = tsname(*fields) else: name = None dates = None values = None return timeseries(name=name, date=dates, data=values)
def testEmptyHashWrapper(self): ts = timeseries() hash = ts.ashash() self.assertFalse(hash)
def timeseries(self, name='', date=None, data=None): return timeseries(name=name, date=date, data=data, backend=self.backend)
def randomts(size = 100, cols = 1, start = None, delta = 1, generator = None, backend=None, name='randomts'): from dynts import timeseries dates = datepopulate(size,start=start,delta=delta) data = populate(size,cols=cols,generator=generator) return timeseries(name=name,backend=backend,date=dates,data=data)
def testVar(self): '''Calculate the biased variance of a series''' ts = timeseries(date = datepopulate(10), data = range(1,11), backend = self.backend) self.assertAlmostEqual(ts.var()[0],8.25) self.assertAlmostEqual(ts.var(ddof=1)[0],9.166667)
def get(self): ts = timeseries(dtype=object) self.assertTrue(ts.is_object) self.assertEqual(ts.dtype, np.dtype(object))
def get(self): ts = timeseries(dtype = object) self.assertTrue(ts.is_object) self.assertEqual(ts.dtype, np.dtype(object))