def convert_and_fill(series, freq, func=None, position='END', *args, **kwargs):
"""
Converts a series from one frequency to another, by manipulating both the
`data` and `dates` attributes.
This function replicates the standard :func:`scikits.timeseries.convert`
function.
When converting to a higher frequency, the result is automatically forward-
filled (if ``position=='START'``) or backward-filled (if ``position='END'``)
The function accepts the same input parameters and has the same signature as
the standard :func:`scikits.timeseries.convert` function.
Please refer to the docstring of this latter for more information.
See Also
--------
convert
Original function
"""
if not isinstance(series, TimeSeries):
raise TypeError("The input should be a valid TimeSeries object!")
result = ts.convert(series,
freq,
func=func,
position=position,
*args,
**kwargs)
if freq > series._dates.freq:
_series = result._series
if position.upper()[0] == 'S':
_series[:] = ts.lib.forward_fill(_series)
else:
_series[:] = ts.lib.backward_fill(_series)
return result
def convert_and_fill(series, freq, func=None, position='END', *args, **kwargs):
"""
Converts a series from one frequency to another, by manipulating both the
`data` and `dates` attributes.
This function replicates the standard :func:`scikits.timeseries.convert`
function.
When converting to a higher frequency, the result is automatically forward-
filled (if ``position=='START'``) or backward-filled (if ``position='END'``)
The function accepts the same input parameters and has the same signature as
the standard :func:`scikits.timeseries.convert` function.
Please refer to the docstring of this latter for more information.
See Also
--------
convert
Original function
"""
if not isinstance(series, TimeSeries):
raise TypeError("The input should be a valid TimeSeries object!")
result = ts.convert(series, freq, func=func, position=position,
*args, **kwargs)
if freq > series._dates.freq:
_series = result._series
if position.upper()[0] == 'S':
_series[:] = ts.lib.forward_fill(_series)
else:
_series[:] = ts.lib.backward_fill(_series)
return result
def weighted(self,*args):
R = None
(weights,[data,series]) = args
logger.debug("Params are (w=%s,d=%s,s=%s)" % (weights,data,series))
if check_type(weights,str,unicode) and check_type(data,str,unicode) and check_type(series,str,unicode):
logger.debug("Params are (w=%s,d=%s,s=%s)" % (weights,data,series))
Ws = weights
if ',' in series:
Ss = series.split(',')
else:
raise ValueError,"La stringa di definizione delle serie è errata"
if ',' in weights:
Ws = weights.split(',')
else:
if '$$' not in weights:
raise ValueError,"La stringa di definizione dei pesi non permette la generazione di un elenco"
Ws = [ weights.replace('$$',S) for S in Ss ]
if ',' in data:
Ds = data.split(',')
else:
if '$$' not in data:
raise ValueError,"La stringa di definizione dei dati non permette la generazione di un elenco"
Ds = [ data.replace('$$',S) for S in Ss ]
if len(Ws)==len(Ds):
W = 0.0
S = 0.0
for d,w in zip(Ds,Ws):
wb=IS.has_key(w)
db=IS.has_key(d)
if wb and db:
Wn = IS[w].data[0]
Dn = IS[d].data
try:
# if isinstance(Dn,ts.TimeSeries):
# Dn = np.nan_to_num(Dn)
# if isinstance(W,ts.TimeSeries):
# W = np.nan_to_num(W)
# if isinstance(W,ts.TimeSeries):
# ts.align_series(W,Dn)
if isinstance(Dn,ts.TimeSeries):
Dn = ts.time_series(Dn,copy=True)
if isinstance(W,ts.TimeSeries):
Dn = ts.convert(Dn,W.freq)
ts.fill_missing_dates(Dn,dates=W.dates,fill_value=np.nan)
Dn = ts.adjust_endpoints(Dn,
start_date=W.start_date,
end_date=W.end_date,
copy=True)
# _ts = np.ma.masked_invalid(Dn)
# Dn = _ts.filled(0.0)
# if isinstance(W,ts.TimeSeries):
# print "GOò"
# _report(W,Dn*Wn)
W += Dn*Wn
# if isinstance(W,ts.TimeSeries):
# print "="
# _report(W)
except Exception, exc:
# if isinstance(W,ts.TimeSeries):
# _report(W)
# print "DN"
# _report(Dn)
# print w,d,"W",type(W),W,W.shape,"D",type(Dn),Dn,Dn.shape,"Wn",type(Wn),Wn
raise
logger.error('Non posso comporre %s * %s | %s', d,w)
ValueError, "%s * %s" % (d,w)
S += Wn
else:
if not wb: logger.error(u'la serie %s non è presente nell\'IS. ATTENZIONE i risultati dell\'aggregazione possono non essere sono attendibili',w)
if not db: logger.error(u'la serie %s non è presente nell\'IS. ATTENZIONE i risultati dell\'aggregazione possono non essere sono attendibili',d)
try:
R = ets.Timeseries(data=W / S,name="WEIGHTED(\"%s\",\"%s\")" % (','.join(Ws),','.join(Ds)))
except ZeroDivisionError, exc:
logger.warn('ZeroDivisionError')
R = None
if namo == 'LNS12032195': stitle = sera['title'].split(',')[0]
else: stitle = sera['title'].replace(r'&','and')
newstr = str(i1+7)+r" & "+stitle+r" & "+namo+r" & "+r"St. Louis Fed"+r" & "+ sera['units']+r" & "+stringo+r" & "+str(stlouislist[i1])+r"\\"+"\n"
tmpstr = tmpstr + newstr
freqf = resuf['seriess']['series']['frequency_short']
print "Done..."
valo = [x['value'] for x in resu['observations']['observation']]
valo2 = numpy.zeros(len(valo))
for i1,elem in enumerate(valo):
if elem == '.': valo2[i1] = float(valo2[i1-1])
else: valo2[i1] = float(elem)
valo = numpy.array(valo2)
dato = [x['date'] for x in resu['observations']['observation']]
if freqf == 'D':
tso = ts.time_series(valo,dates=dato,freq='D')
tso2 = ts.convert(tso,'M',func=numpy.ma.mean)
elif freqf == 'W':
tso = ts.time_series(valo,dates=dato,freq='D')
tso2 = ts.convert(tso,'M',func=numpy.ma.mean)
elif freqf == 'M':
tso = ts.time_series(valo,dates=dato,freq='M')
tso2 = copy.deepcopy(tso)
elif freqf == 'Q':
tso = ts.time_series(valo,dates=dato,freq='Q')
tso2 = ts.convert(tso,'M')
tso2 = interp_masked1d(tso2)
valor = tso2['1985-01-01':'2011-09-01']
dator = valor.dates
valor = valor.data
valor = numpy.reshape(valor,(len(valor),1))
matd = numpy.hstack((matd,valor))
) + r" & " + stitle + r" & " + namo + r" & " + r"St. Louis Fed" + r" & " + sera[
'units'] + r" & " + stringo + r" & " + str(
stlouislist[i1]) + r"\\" + "\n"
tmpstr = tmpstr + newstr
freqf = resuf['seriess']['series']['frequency_short']
print "Done..."
valo = [x['value'] for x in resu['observations']['observation']]
valo2 = numpy.zeros(len(valo))
for i1, elem in enumerate(valo):
if elem == '.': valo2[i1] = float(valo2[i1 - 1])
else: valo2[i1] = float(elem)
valo = numpy.array(valo2)
dato = [x['date'] for x in resu['observations']['observation']]
if freqf == 'D':
tso = ts.time_series(valo, dates=dato, freq='D')
tso2 = ts.convert(tso, 'M', func=numpy.ma.mean)
elif freqf == 'W':
tso = ts.time_series(valo, dates=dato, freq='D')
tso2 = ts.convert(tso, 'M', func=numpy.ma.mean)
elif freqf == 'M':
tso = ts.time_series(valo, dates=dato, freq='M')
tso2 = copy.deepcopy(tso)
elif freqf == 'Q':
tso = ts.time_series(valo, dates=dato, freq='Q')
tso2 = ts.convert(tso, 'M')
tso2 = interp_masked1d(tso2)
valor = tso2['1985-01-01':'2011-09-01']
dator = valor.dates
valor = valor.data
valor = numpy.reshape(valor, (len(valor), 1))
matd = numpy.hstack((matd, valor))
