def _collect_and_plot(files):
TS = []
location = []
for f in files:
temperatures = [ section[1] for section in parse.parse_file(f)[1:-1] if section[1]['Plant'] == ['tmp'] ]
for t in temperatures:
if t['Step'][0] != '0000-00-00.01:00:00':
print 'Not hourly readings of temperature. Abort.'
break
dates = ts.date_array(start_date=ts.Date('H', t['Start'][0]), length=len(t['Value']))
data = [ float(value.rsplit('/')[0]) for value in t['Value'] ]
TS.append(ts.TimeSeries(data=data, dates=dates))
if location and t['Installation'][0] != location:
print 'Location changed during reading of gs2 files. Probably some bad grouping of gs2 files.'
location = t['Installation'][0]
if TS:
path = '/Users/tidemann/Documents/NTNU/devel/data/eklima/Telemark/'
for file in os.listdir(path):
try:
series = xml.parse(path + file)
sg.utils.plot_time_series([ts.concatenate((TS)), series], ['b-','r-'], [location, file])
except:
print file, 'had no data.'
else:
print 'No temperature data.'
def __init__(self, **kwargs):
"""
kwargs ={'baisnName':'Mackenzie',
'start_date':'2000-06-01',
'end_date':'2010-06-31',
'info_fl':r'C:\00_Work\02_Sim\00_Mackenzie\01_Data\01_Selected_RiverDischarge\RiverGages_description.csv',
'pthIn':r'C:\00_Work\02_Sim\00_Mackenzie\01_Data\01_Selected_RiverDischarge'}
"""
for key in kwargs:
setattr(self, key, kwargs[key])
self._load_rivergages()
self._idate = ts.Date('D', self.start_date)
self._idateHStr = self._idate.strfmt('%Y/%m/%d') + ' 00:00'
self._fdate = ts.Date('D', self.end_date)
self._dates = ts.date_array(start_date=self._idate,
end_date=self._fdate,
freq='d')
self.nr_days = self._fdate - self._idate + 1
self._get_matrix()
def test_tsfromtxt(self):
"Tests reading from a text file."
fcontent = """#
'Dates', 'One (S)','Two (I)','Three (F)','Four (M)','Five (-)','Six (C)'
'2007-01', 'strings',1,1.0,'mixed column',,1
'2007-02', 'with embedded "double quotes"',2,2.0,1.0,,1
'2007-03', 'strings',3,3.0E5,3,,1
'2007-05','strings',4,-1e-10,,,1
"""
import os
from datetime import datetime
import tempfile
(tmp_fd, tmp_fl) = tempfile.mkstemp()
os.write(tmp_fd, fcontent)
os.close(tmp_fd)
mrectxt = tsfromtxt(tmp_fl, delimiter=',', names=tuple('ABCDEFG'),
datecols=0, skip_header=2, asrecarray=True)
os.remove(tmp_fl)
#
dlist = ['2007-%02i' % i for i in (1, 2, 3, 5)]
self.failUnless(isinstance(mrectxt, TimeSeriesRecords))
assert_equal(mrectxt._dates, date_array(dlist, 'M'))
assert_equal(mrectxt.dtype.names, ['A', 'B', 'C', 'D', 'E', 'F'])
assert_equal(mrectxt.F, [1, 1, 1, 1])
assert_equal(mrectxt.E._mask, [1, 1, 1, 1])
assert_equal(mrectxt.C, [1, 2, 300000, -1e-10])
def test_sorted(self):
dates = [ts.Date('D', string='2007-01-%02i' % i) for i in (3, 2, 1)]
(a, b) = zip(*[(3., 30), (2., 20), (1., 10), ])
ndtype = [('a', np.float), ('b', np.int)]
controldates = date_array(dates, freq='D')
controldates.sort_chronologically()
series = time_series(zip(*(a, b)), dates, freq='D', dtype=ndtype)
assert_equal(series._data.tolist(), [(1., 10), (2., 20), (3., 30)])
assert_equal(series._dates, controldates)
#
trec = time_records(zip(*(a, b)), dates, freq='D', dtype=ndtype)
assert_equal(trec._data.tolist(), [(1., 10), (2., 20), (3., 30)])
assert_equal(trec._dates, controldates)
assert_equal(trec['a'], [1., 2., 3.])
assert_equal(trec.a, [1., 2., 3.])
#
trec = fromrecords(zip(a, b), dates, names=('a', 'b'))
assert_equal(trec._data.tolist(), [(1., 10), (2., 20), (3., 30)])
assert_equal(trec._dates, controldates)
assert_equal(trec['a'], [1., 2., 3.])
assert_equal(trec.a, [1., 2., 3.])
#
trec = fromarrays([a, b], dates, names=('a', 'b'))
assert_equal(trec._data.tolist(), [(1., 10), (2., 20), (3., 30)])
assert_equal(trec._dates, controldates)
assert_equal(trec['a'], [1., 2., 3.])
assert_equal(trec.a, [1., 2., 3.])
def _get_year(year,d,n=1):
"""
Return a time-series with the same frequency of the input time-series
with n complete years from input year and values taken from input series
:param year: base year
:type year: integer
:param d: time-series object
:type d: time-series
:param n: number of periods to take
:type n: integer
:return: output time-series
:rtype: time-series
"""
f = d.freqstr # frequenza d'ingresso
nels = _ts_nels(f) # numero di elementi da considerare in un anno (M=12, Q=4, A=1)
N=n*nels # Numero di elementi totali da considerare
startd = d.start_date
endd = d.end_date
if f[0]=='M':
starty = ts.Date(f,year=year,month=1)
endy = ts.Date(f,year=year,month=N)
elif f[0]=='Q':
starty = ts.Date(f,year=year,quarter=1)
endy = ts.Date(f,year=year,quarter=N)
elif f[0]=='A':
starty = ts.Date(f,year=year)
endy = ts.Date(f,year=year+N-1)
else:
raise UnknownFrequencyError, f
# Create a timeseries with N elements np.nan
# from starty with frequency f
s = ts.time_series([ np.nan for i in range(0,N)],
start_date=starty,
freq=f)
# create date range
da = ts.date_array(start_date=starty,
end_date=endy,
freq=f)
d.fill_missing_dates()
d.adjust_endpoints()
# copy values from d to s
d.mask=False
for _d in da:
s[_d]=np.nan
if _d <= d.end_date:
s[_d]=d[_d]
else:
s[_d]=np.nan
return s
def test_convert_to_annual(self):
"Test convert_to_annual"
base = dict(D=1, H=24, T=24 * 60, S=24 * 3600)
#for fq in ('D', 'H', 'T', 'S'):
# Don't test for minuTe and Second frequency, too time consuming.
for fq in ('D', 'H'):
dates = date_array(start_date=Date(fq, '2001-01-01 00:00:00'),
end_date=Date(fq, '2004-12-31 23:59:59'))
bq = base[fq]
series = time_series(range(365 * bq) * 3 + range(366 * bq),
dates=dates)
control = ma.masked_all((4, 366 * bq), dtype=series.dtype)
control[0, :58 * bq] = range(58 * bq)
control[0, 59 * bq:] = range(58 * bq, 365 * bq)
control[[1, 2]] = control[0]
control[3] = range(366 * bq)
test = convert_to_annual(series)
assert_equal(test, control)
#
series = time_series(range(59, 365) + range(366) + range(365),
start_date=Date('D', '2003-03-01'))
test = convert_to_annual(series)
assert_equal(test[:, 59:62],
ma.masked_values([[-1, 59, 60], [59, 60, 61], [-1, 59, 60]],
- 1))
def test_sorted(self):
dates = [ts.Date('D', string='2007-01-%02i' % i) for i in (3, 2, 1)]
(a, b) = zip(*[
(3., 30),
(2., 20),
(1., 10),
])
ndtype = [('a', np.float), ('b', np.int)]
controldates = date_array(dates, freq='D')
controldates.sort_chronologically()
series = time_series(zip(*(a, b)), dates, freq='D', dtype=ndtype)
assert_equal(series._data.tolist(), [(1., 10), (2., 20), (3., 30)])
assert_equal(series._dates, controldates)
#
trec = time_records(zip(*(a, b)), dates, freq='D', dtype=ndtype)
assert_equal(trec._data.tolist(), [(1., 10), (2., 20), (3., 30)])
assert_equal(trec._dates, controldates)
assert_equal(trec['a'], [1., 2., 3.])
assert_equal(trec.a, [1., 2., 3.])
#
trec = fromrecords(zip(a, b), dates, names=('a', 'b'))
assert_equal(trec._data.tolist(), [(1., 10), (2., 20), (3., 30)])
assert_equal(trec._dates, controldates)
assert_equal(trec['a'], [1., 2., 3.])
assert_equal(trec.a, [1., 2., 3.])
#
trec = fromarrays([a, b], dates, names=('a', 'b'))
assert_equal(trec._data.tolist(), [(1., 10), (2., 20), (3., 30)])
assert_equal(trec._dates, controldates)
assert_equal(trec['a'], [1., 2., 3.])
assert_equal(trec.a, [1., 2., 3.])
def test_tsfromtxt(self):
"Tests reading from a text file."
fcontent = """#
'Dates', 'One (S)','Two (I)','Three (F)','Four (M)','Five (-)','Six (C)'
'2007-01', 'strings',1,1.0,'mixed column',,1
'2007-02', 'with embedded "double quotes"',2,2.0,1.0,,1
'2007-03', 'strings',3,3.0E5,3,,1
'2007-05','strings',4,-1e-10,,,1
"""
import os
from datetime import datetime
import tempfile
(tmp_fd, tmp_fl) = tempfile.mkstemp()
os.write(tmp_fd, fcontent)
os.close(tmp_fd)
mrectxt = tsfromtxt(tmp_fl,
delimiter=',',
names=tuple('ABCDEFG'),
datecols=0,
skip_header=2,
asrecarray=True)
os.remove(tmp_fl)
#
dlist = ['2007-%02i' % i for i in (1, 2, 3, 5)]
self.failUnless(isinstance(mrectxt, TimeSeriesRecords))
assert_equal(mrectxt._dates, date_array(dlist, 'M'))
assert_equal(mrectxt.dtype.names, ['A', 'B', 'C', 'D', 'E', 'F'])
assert_equal(mrectxt.F, [1, 1, 1, 1])
assert_equal(mrectxt.E._mask, [1, 1, 1, 1])
assert_equal(mrectxt.C, [1, 2, 300000, -1e-10])
def add_diurnal(tseries, sine_period, peak_mag):
"""
Scales a time series to a sine wave of peak_mag with sine_period.
Input: tseries, sine_period (float, hrs), peak_mag (float)
Output: scaled_data (array-like)
"""
# Convert sine_period to same frequency as tseries
# Create a time delta of magnitude sine_period
# Convert that time delta into frequency units same as tseries
zero_date = ts.now('H')
second_date = zero_date + sine_period
time_delta = ts.date_array([zero_date, second_date])
time_delta = time_delta.asfreq(tseries.freq)
sine_period = float(time_delta[1] - time_delta[0])
angular_freq = (2. * np.pi) / sine_period
for i in range(len(tseries)-1):
passed_time = float(tseries.dates[i]- tseries.start_date)
sine_factor = peak_mag * np.sin(angular_freq * passed_time)
tseries[i] = tseries[i] + tseries[i] * sine_factor
return tseries
# Generate power density function (pdf) to create synthetic TPM from
# mean, stdev, autocorr, npointsx
# def gen_pdf(desired_mean, desired_stdev, bin_width):
## TODO
return 0
def test_convert_to_annual(self):
"Test convert_to_annual"
base = dict(D=1, H=24, T=24 * 60, S=24 * 3600)
#for fq in ('D', 'H', 'T', 'S'):
# Don't test for minuTe and Second frequency, too time consuming.
for fq in ('D', 'H'):
dates = date_array(start_date=Date(fq, '2001-01-01 00:00:00'),
end_date=Date(fq, '2004-12-31 23:59:59'))
bq = base[fq]
series = time_series(range(365 * bq) * 3 + range(366 * bq),
dates=dates)
control = ma.masked_all((4, 366 * bq), dtype=series.dtype)
control[0, :58 * bq] = range(58 * bq)
control[0, 59 * bq:] = range(58 * bq, 365 * bq)
control[[1, 2]] = control[0]
control[3] = range(366 * bq)
test = convert_to_annual(series)
assert_equal(test, control)
#
series = time_series(range(59, 365) + range(366) + range(365),
start_date=Date('D', '2003-03-01'))
test = convert_to_annual(series)
assert_equal(
test[:, 59:62],
ma.masked_values([[-1, 59, 60], [59, 60, 61], [-1, 59, 60]], -1))
def _get_tseriesD(freq,date_values,kw):
v = [ xlrd.xldate_as_tuple(int(d),0)
for i,d in enumerate(date_values.flatten())
if not np.isnan(d) ]
D = [ ts.Date(freq=str(freq),year=_v[0],month=_v[1],day=_v[2]) for _v in v]
date_array = ts.date_array(D)
return date_array
def request(self,reqs,**kw):
"""Random Request
>>> ds = Random()
>>> xs = ds.request([{'Instrument':'NAME=PIPPO~:2012-12-31~2007-01-01~M', 'Source': 'FRED'},])
>>> ts = xs['PIPPO']._data
>>> print ts.freqstr
M
"""
logger.debug('request')
rx={}
kw=udict(kw)
if 'SEED' in kw:
seed = int(kw['SEED'])
rand.seed(seed)
for rq in reqs:
logger.debug("Request: %s",rq)
s = parse_instrument(rq['Instrument'])
logger.debug(s)
params = ldict(mk_params(s['TICKER']))
if 'name' in params:
name = params['name'].upper()
del params['name']
else:
name = rq['Source'].upper()
if 'NAME' in kw and kw['NAME']:
name = kw['NAME'].upper()
kw['NAME']=name
if s['FREQ']!='0':
dr = ts.date_array(freq=s['FREQ'],start_date=s['START'],end_date=s['END'])
L = len(dr)
else:
L = s['END']-s['START']
params['size']=L
dd = np.zeros(L)
logger.debug(params)
try:
if re.match('^constant$',rq['Source'],re.I):
name = 'CONSTANT'
v = float(name)
params['LOW']=v
params['HIGH']=v
dd = rand.uniform(**params)
elif re.match('^walk$',rq['Source'],re.I):
dd = RandomWalk(**params)
elif re.match('^uniform|beta|binomial|chisquare|exponential|gamma|geometric|gumbel|hypergeometric|laplace|logistic|lognormal|logseries|multinomial|multivariate_normal|negative_binomial|noncentral_chisquare|noncentral_f|normal|pareto|poisson|power|rayleigh|standard_cauchy|standard_exponential|standard_gamma|standard_normal|standard_t|triangular|uniform|vonmises|wald|weibull|zipf$',rq['Source'],re.I):
generator = rq['Source'].lower()
if hasattr(rand,generator):
f = getattr(rand,generator)
dd = f(**params)
## Add other distributions here
else:
raise TypeError, 'Unknown generator'
except TypeError, exc:
logging.error('%s generator - %s',rq['Source'],exc.args[0])
except:
def _make_predict_dates(self):
data = self._data
dtstart = data.predict_start
dtend = data.predict_end
freq = data.freq
#pandas_freq = _freq_to_pandas[freq]
dates = date_array(start_date=dtstart, end_date=dtend,
freq=freq).toordinal().astype(int)
self._data.predict_dates = asarray(
[datetime.datetime.fromordinal(i) for i in dates])
def _get_tseriesQ(freq,date_values,kw):
# print date_values
by=0
if kw.has_key('YEAR'):
by = eval(kw['YEAR'])-1
v = [ (int(d)-1)%4+1+(int(_i/4)*4)+by*4
for _i,d in enumerate(date_values.flatten())
if not np.isnan(d) ]
D = [ ts.Date(freq=str(freq),value=_v) for _v in v]
date_array = ts.date_array(D)
return date_array
def setup(self):
"Generic setup"
d = np.arange(5)
m = ma.make_mask([1, 0, 0, 1, 1])
base_d = np.r_[d, d[::-1]].reshape(2, -1).T
base_m = np.r_[[m, m[::-1]]].T
base = ma.array(base_d, mask=base_m)
mrec = mr.fromarrays(base.T,)
dlist = ['2007-%02i' % (i + 1) for i in d]
dates = date_array(dlist)
mts = time_series(mrec, dates)
rts = time_records(mrec, dates)
self.data = [d, m, mrec, dlist, dates, mts, rts]
def setup(self):
"Generic setup"
d = np.arange(5)
m = ma.make_mask([1, 0, 0, 1, 1])
base_d = np.r_[d, d[::-1]].reshape(2, -1).T
base_m = np.r_[[m, m[::-1]]].T
base = ma.array(base_d, mask=base_m)
mrec = mr.fromarrays(base.T, )
dlist = ['2007-%02i' % (i + 1) for i in d]
dates = date_array(dlist)
mts = time_series(mrec, dates)
rts = time_records(mrec, dates)
self.data = [d, m, mrec, dlist, dates, mts, rts]
def test_dates_on_several_columns(self):
"Test tsfromtxt when the date spans several columns."
datatxt = """
2001, 01, 0.0, 10.
2001, 02, 1.1, 11.
2001, 02, 2.2, 12.
"""
data = StringIO.StringIO(datatxt)
dateconverter = lambda y, m: Date('M', year=int(y), month=int(m))
test = tsfromtxt(data, delimiter=',', dtype=float, datecols=(0, 1),
dateconverter=dateconverter)
assert_equal(test, [[0., 10.], [1.1, 11.], [2.2, 12.]])
assert_equal(test.dates,
date_array(['2001-01', '2001-02', '2001-02'], freq='M'))
def _make_predict_dates(self):
try:
from scikits.timeseries import date_array
except ImportError:
self._data.predict_dates = None
data = self._data
dtstart = data.predict_start
dtend = data.predict_end
freq = data.freq
#pandas_freq = _freq_to_pandas[freq]
dates = date_array(start_date=dtstart, end_date=dtend,
freq=freq).toordinal().astype(int)
self._data.predict_dates = asarray(
[datetime.datetime.fromordinal(i) for i in dates])
def _make_predict_dates(self):
try:
from scikits.timeseries import date_array
except ImportError:
self._data.predict_dates = None
data = self._data
dtstart = data.predict_start
dtend = data.predict_end
freq = data.freq
#pandas_freq = _freq_to_pandas[freq]
dates = date_array(start_date=dtstart, end_date=dtend,
freq=freq).toordinal().astype(int)
self._data.predict_dates = asarray(
[datetime.datetime.fromordinal(i) for i in dates])
def test_with_datecols(self):
"Test two datecols"
fcontent = StringIO.StringIO("""
year, month, A, B
2009, 01, 1, 1.
2009, 03, 3, 3.
""")
dateconv = lambda y, m: Date("M", year=int(y), month=int(m))
test = tsfromtxt(fcontent, delimiter=",", skip_header=1, names=True,
converters={'dates': dateconv}, datecols=(0, 1))
dates = date_array(['2009-01', '2009-03'], freq='M')
assert_equal(test.dates.tovalue(), dates)
assert_equal(test['A'], [1, 3])
assert_equal(test['B'], [1., 3.])
assert_equal(test.dtype, np.dtype([('A', int), ('B', float)]))
def _get_tseriesM(freq,date_values,kw):
dformat='INT'
if kw.has_key('DFORMAT'):
dformat = kw['DFORMAT']
if dformat=='INT':
v = [ (int(d)-1)*12+i%12+1 for i,d in enumerate(date_values.flatten()) if not np.isnan(d) ]
D = [ ts.Date(freq=str(freq),value=_v) for _v in v]
elif dformat=='XL_DATE':
v = [ xlrd.xldate_as_tuple(d,0) for i,d in enumerate(date_values.flatten()) if not np.isnan(d) ]
D = [ ts.Date(freq=str(freq),year=_v[0],month=_v[1]) for _v in v]
else:
logger.error('DATE FORMAT NOT SUPPORTED ON EXCEL READING')
raise ValueError, dformat
# print "FREQ=|%s|"%freq,D
date_array = ts.date_array(D)
return date_array
def test_dates_on_several_columns(self):
"Test tsfromtxt when the date spans several columns."
datatxt = """
2001, 01, 0.0, 10.
2001, 02, 1.1, 11.
2001, 02, 2.2, 12.
"""
data = StringIO.StringIO(datatxt)
dateconverter = lambda y, m: Date('M', year=int(y), month=int(m))
test = tsfromtxt(data,
delimiter=',',
dtype=float,
datecols=(0, 1),
dateconverter=dateconverter)
assert_equal(test, [[0., 10.], [1.1, 11.], [2.2, 12.]])
assert_equal(test.dates,
date_array(['2001-01', '2001-02', '2001-02'], freq='M'))
def setUp(self):
"Initializes"
ndtype = [('lin',float),('rand',float)]
dlin = np.linspace(0,10,120)
drnd = np.random.rand(120)
data = np.array(zip(dlin, drnd), dtype=ndtype)
dates = ts.date_array(start_date=ts.now('M')-120, length=120, freq='M')
enso = ENSOIndicator(np.random.rand(120) + np.linspace(-1,1,120),
dates=dates,
thresholds=(-0.5,0.5),
full_year='False', refseason='NDH', minsize=5)
cdat = data.view(ClimateRecords)
cdat._dates = dates
cdat.ensoindicator = enso
self.dlin = dlin
self.cdat=cdat
self.enso=enso
def test_with_datecols(self):
"Test two datecols"
fcontent = StringIO.StringIO("""
year, month, A, B
2009, 01, 1, 1.
2009, 03, 3, 3.
""")
dateconv = lambda y, m: Date("M", year=int(y), month=int(m))
test = tsfromtxt(fcontent,
delimiter=",",
skip_header=1,
names=True,
converters={'dates': dateconv},
datecols=(0, 1))
dates = date_array(['2009-01', '2009-03'], freq='M')
assert_equal(test.dates.tovalue(), dates)
assert_equal(test['A'], [1, 3])
assert_equal(test['B'], [1., 3.])
assert_equal(test.dtype, np.dtype([('A', int), ('B', float)]))
def test_with_names(self):
"Tests w/ names"
fcontent = StringIO.StringIO("""#
'Dates', 'One (S)','Two (I)','Three (F)','Four (M)','Five (-)','Six (C)'
'2007-01', 'strings',1,1.0,'mixed column',,1
'2007-02', 'with embedded "double quotes"',2,2.0,1.0,,1
'2007-03', 'strings',3,3.0E5,3,,1
'2007-05','strings',4,-1e-10,,,1
""")
test = tsfromtxt(fcontent, delimiter=",", datecols=0, skip_header=2,
names="A,B,C,D,E,F", freq='M')
assert(isinstance(test, TimeSeries))
dlist = ['2007-%02i' % i for i in (1, 2, 3, 5)]
assert_equal(test.dates.tovalue(),
date_array(dlist, freq='M').tovalue())
assert_equal(test.dtype.names, ['A', 'B', 'C', 'D', 'E', 'F'])
assert_equal(test['F'], [1, 1, 1, 1])
assert_equal(test['E'].mask, [1, 1, 1, 1])
assert_equal(test['C'], [1, 2, 300000, -1e-10])
def setUp(self):
"Initializes"
ndtype = [('lin', float), ('rand', float)]
dlin = np.linspace(0, 10, 120)
drnd = np.random.rand(120)
data = np.array(zip(dlin, drnd), dtype=ndtype)
dates = ts.date_array(start_date=ts.now('M') - 120,
length=120,
freq='M')
enso = ENSOIndicator(np.random.rand(120) + np.linspace(-1, 1, 120),
dates=dates,
thresholds=(-0.5, 0.5),
full_year='False',
refseason='NDH',
minsize=5)
cdat = data.view(ClimateRecords)
cdat._dates = dates
cdat.ensoindicator = enso
self.dlin = dlin
self.cdat = cdat
self.enso = enso
def test_without_names(self):
"Test w/o names"
fcontent = StringIO.StringIO("""#
'Dates', 'One (S)','Two (I)','Three (F)','Four (M)','Five (-)','Six (C)'
'2007-01', 'strings',1,1.0,'mixed column',,1
'2007-02', 'with embedded "double quotes"',2,2.0,1.0,,1
'2007-03', 'strings',3,3.0E5,3,,1
'2007-05','strings',4,-1e-10,,,1
""")
test = tsfromtxt(fcontent,
delimiter=",",
skip_header=1,
names=True,
freq='M')
assert (isinstance(test, TimeSeries))
dlist = ['2007-%02i' % i for i in (1, 2, 3, 5)]
assert_equal(test.dates.tovalue(),
date_array(dlist, freq='M').tovalue())
assert_equal(test.dtype.names,
['One_S', 'Two_I', 'Three_F', 'Four_M', 'Five_', 'Six_C'])
assert_equal(test['Six_C'], [1, 1, 1, 1])
assert_equal(test['Five_'].mask, [1, 1, 1, 1])
assert_equal(test['Three_F'], [1, 2, 300000, -1e-10])
import statsmodels.api as sm import numpy as np import pandas # Getting started # --------------- data = sm.datasets.sunspots.load() # Right now an annual date series must be datetimes at the end of the year. # We can use scikits.timeseries and datetime to create this array. import datetime import scikits.timeseries as ts dates = ts.date_array(start_date=1700, length=len(data.endog), freq="A") # To make an array of datetime types, we need an integer array of ordinals # .. from datetime import datetime # .. dt_dates = dates.toordinal().astype(int) # .. dt_dates = np.asarray([datetime.fromordinal(i) for i in dt_dates]) dt_dates = dates.tolist() # Using Pandas # ------------ # Make a pandas TimeSeries or DataFrame endog = pandas.Series(data.endog, index=dt_dates) # and instantiate the model
* both la and pandas handle datetime objects as object arrays
* tabular requires conversion to structured dtype, but easy helper
functions or methods are available in scikits.timeseries and tabular
* not too bad for a first try
Created on Sat Jan 30 08:33:11 2010
Author: josef-pktd
"""
import numpy as np
import scikits.timeseries as ts
s = ts.time_series([1, 2, 3, 4, 5],
dates=ts.date_array(
["2001-01", "2001-01", "2001-02", "2001-03", "2001-03"],
freq="M"))
print '\nUsing la'
import la
dta = la.larry(s.data, label=[range(len(s.data))])
dat = la.larry(s.dates.tolist(), label=[range(len(s.data))])
s2 = ts.time_series(dta.group_mean(dat).x,
dates=ts.date_array(dat.x, freq="M"))
s2u = ts.remove_duplicated_dates(s2)
print repr(s)
print dat
print repr(s2)
print repr(s2u)
print '\nUsing pandas'
"""
Look at some macro plots, then do some VARs and IRFs.
"""
import numpy as np
import statsmodels.api as sm
import scikits.timeseries as ts
import scikits.timeseries.lib.plotlib as tplt
data = sm.datasets.macrodata.load(as_pandas=False)
data = data.data
### Create Timeseries Representations of a few vars
dates = ts.date_array(start_date=ts.Date('Q', year=1959, quarter=1),
end_date=ts.Date('Q', year=2009, quarter=3))
ts_data = data[['realgdp', 'realcons', 'cpi']].view(float).reshape(-1, 3)
ts_data = np.column_stack((ts_data, (1 - data['unemp'] / 100) * data['pop']))
ts_series = ts.time_series(ts_data, dates)
fig = tplt.tsfigure()
fsp = fig.add_tsplot(221)
fsp.tsplot(ts_series[:, 0], '-')
fsp.set_title("Real GDP")
fsp = fig.add_tsplot(222)
fsp.tsplot(ts_series[:, 1], 'r-')
fsp.set_title("Real Consumption")
fsp = fig.add_tsplot(223)
fsp.tsplot(ts_series[:, 2], 'g-')
fsp.set_title("CPI")
def getHappinessStats(fromFunc=False, groupId=None, userId=None, endDateIn=None):
condFilter = [1==1]
companyId = request.form.get("companyId") and request.form.get("companyId") or session.get("companyId")
if companyId :
condFilter.append(Happiness.companyId==companyId)
else :
return None
if groupId:
condFilter.append(Happiness.groupId==groupId)
elif userId:
condFilter.append(Happiness.userId==userId)
if request.form.get('endDate'):
endDateIn = request.form['endDate']
elif fromFunc==True and not endDateIn:
endDateIn = datetime.now().strftime("%Y-%m-%d")
if not endDateIn:
return None
if request.form.get("startDate"):
condFilter.append(Happiness.rdate > request.form['startDate'])
# cols = func.date(Happiness.rdate).label("rdate"), func.avg(Happiness.happyVal)
# if request.form.get('type')=='all' and request.form.get('groupId') and request.form.get('userId'):
# cols = cols + ( func.avg(func.IF(Happiness.groupId==groupId, Happiness.happyVal, None)) )
# cols = cols + ( func.avg(func.IF(Happiness.userId==userId, Happiness.happyVal, None)) )
cols = func.date(Happiness.rdate).label("rdate"), func.avg(Happiness.happyVal), func.avg(func.IF(Happiness.groupId==request.form.get('groupId'), Happiness.happyVal, None)), func.avg(func.IF(Happiness.userId==request.form.get('userId'), Happiness.happyVal, None))
db_result = db_session.query(*cols)\
.group_by( func.date(Happiness.rdate) )\
.filter(func.date(Happiness.rdate) <= endDateIn)\
.filter(*condFilter).all()
db_dates = list()
db_vals1 = list()
db_vals2 = list()
db_vals3 = list()
for row in db_result:
db_dates.append(row[0])
db_vals1.append(row[1])
if len(row)>2 :
db_vals2.append(row[2])
db_vals3.append(row[3])
stats = list()
dateAry = ts.date_array(db_dates, freq='D')
timeSrz1 = ts.time_series(db_vals1, dateAry)
fillVals1 = timeSrz1.fill_missing_dates(fill_value=0)
fillVals1 = fillVals1.filled(0)
timeSrz2 = ts.time_series(db_vals2, dateAry)
fillVals2 = timeSrz2.fill_missing_dates(fill_value=0)
fillVals2 = fillVals2.filled(0)
timeSrz3 = ts.time_series(db_vals3, dateAry)
fillVals3 = timeSrz3.fill_missing_dates(fill_value=0)
fillVals3 = fillVals3.filled(0)
if request.form.get('startDate'):
startDate = datetime.strptime(request.form['startDate'], '%Y-%m-%d')
else :
startDate = db_dates[0]
endDate = datetime.strptime(endDateIn, '%Y-%m-%d')
fillDateAry = ts.date_array(start_date=startDate, end_date=endDate, freq='D')
firstDayIdx = 0
for day in fillDateAry :
if len(dateAry)==0 or dateAry[0] != fillDateAry[firstDayIdx] :
stats.append({ "date" : fillDateAry[firstDayIdx].strftime("%m/%d"), "val" : 0, "gr":0, "me":0 })
firstDayIdx += 1
continue
else :
break
for idx, val in enumerate(fillVals1):
gr = fillVals2[idx]
me = fillVals3[idx]
rowDict = { "date" : fillDateAry[firstDayIdx + idx].strftime("%m/%d"), "val" : "{0:.2f}".format(float(val or 0)) }
rowDict["gr"] = "{0:.2f}".format(float(gr or 0))
rowDict["me"] = "{0:.2f}".format(float(me or 0))
stats.append(rowDict)
statsLen = len(stats)
for i in range(len(fillDateAry) - len(stats)) :
stats.append({ "date" : fillDateAry[statsLen + i].strftime("%m/%d"), "val" : 0, "gr":0, "me":0 })
if fromFunc==True :
return stats
else :
return jsonify({"stats" : stats})
def load_oni(mode='standard', **options):
"""
Loads the ONI 3-m averaged monthly SST anomalies over the Niño-3.4 region
and returns a :class:`~scikits.hydroclimpy.enso.ENSOIndicator` object.
Two modes are accepted as arguments:
- in the ``standard`` mode, the SSTs are retrieved from the original CPC
website_.
Data are available from Jan. 1950 to present.
- in the ``backup`` mode, the SSTs are retrieved from the CPC `ftp site <ftpsite>`_.
Data are available from Jan. 1900 to present.
.. _website : http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml
.. _ftpsite : ftp://eclipse.ncdc.noaa.gov/pub/ersst/pdo/el_nino_v3.dat.
Parameters
----------
mode : {'standard','backup'}, optional
Mode describing the data to download.
options : dictionary
Optional parameters to parse to the ENSOIndicator for the definition of
ENSO indices.
thresholds : tuple of floats, optional
Low and high temperature thresholds for the definition of El Niño and
La Niña conditions.
By default, the CPC uses -0.5oC and +0.5oC.
minimum_size : int, optional
Minimum number of consecutive months in El Niño / La Niña conditions
required for the definition of an episode.
By default, the CPC use 5 consecutive months.
reference_season : string or tuple, optional
Months that must be in an episode for it to be valid.
By default, the CPC uses None (no restriction on the months).
full_year : boolean, optional
The CPC uses ``full_year=False``.
References
----------
Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr
SST normals during 1871-2000. *J. Climate*, 16, 1601-1612.
"""
# Initialization .......................
ensoarchive = dict(config.items('ENSO'))['ensoarchive']
if ensoarchive[-4:].lower() != '.zip':
ensoarchive += '.zip'
#
mode = mode.lower()
cfg = dict(config.items('ENSO.ONI'))
cfg.update(options)
try:
from BeautifulSoup import BeautifulSoup, SoupStrainer
except ImportError:
warnings.warn("The module 'BeautifulSoup' is unavailable.\n"\
"Reverting to backup mode")
mode = 'backup'
#
datadir = cfg['datadir']
if mode == 'standard':
netfile = cfg['netfile']
archive = cfg['archive']
else:
netfile = cfg['netfile_backup']
archive = cfg['archive_backup']
# Try to open an existing ENSOIndicator
ensoarchive = dict(config.items('ENSO'))['ensoarchive']
if ensoarchive[-4:].lower() != '.zip':
ensoarchive += '.zip'
#
try:
zipf = zipfile.ZipFile(ensoarchive, 'r')
ensoi = cPickle.loads(zipf.read(archive))
ensologger.info("... Loading from existing archived file")
except IOError:
zipf = zipfile.ZipFile(ensoarchive, 'w')
ensologger.info("... Creating archive")
except KeyError:
zipf = zipfile.ZipFile(ensoarchive, 'a')
ensologger.info("... Appending to archive")
else:
if isinstance(ensoi, enso.ENSOIndicator):
return ensoi
#
sourcedir = np.lib._datasource.DataSource(datadir)
dfile = sourcedir.open(netfile)
#
#
if mode == 'standard':
# Load the file as a tree, but only take the SST table (border=1)
table = BeautifulSoup(dfile.read(),
parseOnlyThese=SoupStrainer("table", border=1))
# Separate it by rows, but skip the first one (the header)
years = []
data = []
indices = []
color = {'red': +1, 'white': 0, 'blue': -1}
deft = [(None, 'color:white')]
for row in table.findAll("tr")[1:]:
cols = row.findAll('td')
years.append(int(cols.pop(0).strong.string))
data.append([
float(_.fetchText()[-1].string.replace(' ', '99.9'))
for _ in cols
])
indices.append([
color[getattr(_.span, 'attrs', deft)[0][-1].split(':')[-1]]
for _ in cols
])
#
start_date = Date('M', year=years[0], month=1)
ensoi = enso.ENSOIndicator(
ma.masked_values(data, 99.9).ravel(),
start_date=start_date,
)
# oni.set_indices(full_year=False, minsize=5, refseason=None)
indices = time_series(np.array(indices).ravel(), start_date=start_date)
else:
rawdata = np.loadtxt(dfile)
dates = date_array(
[Date('M', year=yy, month=mm) for (yy, mm) in rawdata[:, :2]],
freq='M')
ensoi = enso.ENSOIndicator(
cmov_mean(rawdata[:, -1], 3).round(2),
dates,
)
#
_set_ensoindicator_options(ensoi, **cfg)
ensoi.set_indices()
#
# Store in the archive
zipf.writestr(archive, cPickle.dumps(ensoi))
zipf.close()
return ensoi
# t = timer()
# mod_tb = tb.lpc(y, 2)
# t_end = timer()
# print str(t_end - t) + " seconds for talkbox.lpc"
# print """For higher lag lengths ours quickly fills up memory and starts
#thrashing the swap. Should we include talkbox C code or Cythonize the
#Levinson recursion algorithm?"""
## Try with a pandas series
import pandas
import scikits.timeseries as ts
d1 = ts.Date(year=1700, freq='A')
#NOTE: have to have yearBegin offset for annual data until parser rewrite
#should this be up to the user, or should it be done in TSM init?
#NOTE: not anymore, it's end of year now
ts_dr = ts.date_array(start_date=d1, length=len(sunspots.endog))
pandas_dr = pandas.DateRange(start=d1.datetime,
periods=len(sunspots.endog), timeRule='A@DEC')
#pandas_dr = pandas_dr.shift(-1, pandas.datetools.yearBegin)
dates = np.arange(1700, 1700 + len(sunspots.endog))
dates = ts.date_array(dates, freq='A')
#sunspots = pandas.Series(sunspots.endog, index=dates)
#NOTE: pandas only does business days for dates it looks like
import datetime
dt_dates = np.asarray(lmap(datetime.datetime.fromordinal,
ts_dr.toordinal().astype(int)))
sunspots = pandas.Series(sunspots.endog, index=dt_dates)
#NOTE: pandas can't handle pre-1900 dates
rearranged
1.00796791 0.24449867(-0.00521004) 0.50554663
garch11:
[ 1.01258264 0.24149155 0.50479994]
-2056.3877404
R include_constant=False
Final Estimate:
LLH: 2056.397 norm LLH: 2.056397
omega alpha1 beta1
1.0123560 0.2409589 0.5049154
'''
erro,ho, etaxo = generate_gjrgarch(20, ar, ma, mu=0.04, scale=0.01,
varinnovation = np.ones(20))
if 'sp500' in examples:
import tabular as tb
import scikits.timeseries as ts
a = tb.loadSV(r'C:\Josef\work-oth\gspc_table.csv')
s = ts.time_series(a[0]['Close'][::-1],
dates=ts.date_array(a[0]['Date'][::-1],freq="D"))
sp500 = a[0]['Close'][::-1]
sp500r = np.diff(np.log(sp500))
plt.show()
dd = f(**params)
## Add other distributions here
else:
raise TypeError, 'Unknown generator'
except TypeError, exc:
logging.error('%s generator - %s',rq['Source'],exc.args[0])
except:
raise
if s['FREQ']!='0':
data = ts.time_series(dd,
freq=s['FREQ'],
dates=dr)
if s['NAN']:
t=ts.now(data.freqstr)
if t<data.end_date:
da = ts.date_array(start_date=t,end_date=data.end_date)
data[da]=np.nan
rx[kw['NAME']]=Timeseries(data=data,name=name)
else: # for a vector
print type(dd),dd.shape
xx = dd.reshape((1,-1))
print type(xx),xx.shape
rx[kw['NAME']]=np.array(dd)
return rx
# random://normal/loc=10,scale=.2~:2012-12-31~2007-01-01~M?name=UNI
if __name__=="__main__":
parse_instrument('NAME=PIPPO')
parse_instrument('NAME=PIPPO~M')
parse_instrument('NAME=PIPPO~Q')
def _daily_finder(vmin, vmax, freq):
periodsperday = -1
if freq >= _c.FR_HR:
if freq == _c.FR_SEC:
periodsperday = 24 * 60 * 60
elif freq == _c.FR_MIN:
periodsperday = 24 * 60
elif freq == _c.FR_HR:
periodsperday = 24
else:
raise ValueError("unexpected frequency: %s" % check_freq_str(freq))
periodsperyear = 365 * periodsperday
periodspermonth = 28 * periodsperday
elif freq == _c.FR_BUS:
periodsperyear = 261
periodspermonth = 19
elif freq == _c.FR_DAY:
periodsperyear = 365
periodspermonth = 28
elif get_freq_group(freq) == _c.FR_WK:
periodsperyear = 52
periodspermonth = 3
elif freq == _c.FR_UND:
periodsperyear = 100
periodspermonth = 10
else:
raise ValueError("unexpected frequency")
# save this for later usage
vmin_orig = vmin
(vmin, vmax) = (int(vmin), int(vmax))
span = vmax - vmin + 1
dates_ = date_array(start_date=Date(freq, vmin), end_date=Date(freq, vmax))
# Initialize the output
info = np.zeros(span,
dtype=[('val', int), ('maj', bool), ('min', bool),
('fmt', '|S20')])
info['val'][:] = np.arange(vmin, vmax + 1)
info['fmt'][:] = ''
info['maj'][[0, -1]] = True
# .. and set some shortcuts
info_maj = info['maj']
info_min = info['min']
info_fmt = info['fmt']
def first_label(label_flags):
if (label_flags[0] == 0) and (label_flags.size > 1) and \
((vmin_orig % 1) > 0.0):
return label_flags[1]
else:
return label_flags[0]
# Case 1. Less than a month
if span <= periodspermonth:
day_start = period_break(dates_, 'day')
month_start = period_break(dates_, 'month')
def _hour_finder(label_interval, force_year_start):
_hour = dates_.hour
_prev_hour = (dates_ - 1).hour
hour_start = (_hour - _prev_hour) != 0
info_maj[day_start] = True
info_min[hour_start & (_hour % label_interval == 0)] = True
year_start = period_break(dates_, 'year')
info_fmt[hour_start & (_hour % label_interval == 0)] = '%H:%M'
info_fmt[day_start] = '%H:%M\n%d-%b'
info_fmt[year_start] = '%H:%M\n%d-%b\n%Y'
if force_year_start and not has_level_label(year_start, vmin_orig):
info_fmt[first_label(day_start)] = '%H:%M\n%d-%b\n%Y'
def _minute_finder(label_interval):
hour_start = period_break(dates_, 'hour')
_minute = dates_.minute
_prev_minute = (dates_ - 1).minute
minute_start = (_minute - _prev_minute) != 0
info_maj[hour_start] = True
info_min[minute_start & (_minute % label_interval == 0)] = True
year_start = period_break(dates_, 'year')
info_fmt = info['fmt']
info_fmt[minute_start & (_minute % label_interval == 0)] = '%H:%M'
info_fmt[day_start] = '%H:%M\n%d-%b'
info_fmt[year_start] = '%H:%M\n%d-%b\n%Y'
def _second_finder(label_interval):
minute_start = period_break(dates_, 'minute')
_second = dates_.second
_prev_second = (dates_ - 1).second
second_start = (_second - _prev_second) != 0
info['maj'][minute_start] = True
info['min'][second_start & (_second % label_interval == 0)] = True
year_start = period_break(dates_, 'year')
info_fmt = info['fmt']
info_fmt[second_start
& (_second % label_interval == 0)] = '%H:%M:%S'
info_fmt[day_start] = '%H:%M:%S\n%d-%b'
info_fmt[year_start] = '%H:%M:%S\n%d-%b\n%Y'
if span < periodsperday / 12000.0: _second_finder(1)
elif span < periodsperday / 6000.0: _second_finder(2)
elif span < periodsperday / 2400.0: _second_finder(5)
elif span < periodsperday / 1200.0: _second_finder(10)
elif span < periodsperday / 800.0: _second_finder(15)
elif span < periodsperday / 400.0: _second_finder(30)
elif span < periodsperday / 150.0: _minute_finder(1)
elif span < periodsperday / 70.0: _minute_finder(2)
elif span < periodsperday / 24.0: _minute_finder(5)
elif span < periodsperday / 12.0: _minute_finder(15)
elif span < periodsperday / 6.0: _minute_finder(30)
elif span < periodsperday / 2.5: _hour_finder(1, False)
elif span < periodsperday / 1.5: _hour_finder(2, False)
elif span < periodsperday * 1.25: _hour_finder(3, False)
elif span < periodsperday * 2.5: _hour_finder(6, True)
elif span < periodsperday * 4: _hour_finder(12, True)
else:
info_maj[month_start] = True
info_min[day_start] = True
year_start = period_break(dates_, 'year')
info_fmt = info['fmt']
info_fmt[day_start] = '%d'
info_fmt[month_start] = '%d\n%b'
info_fmt[year_start] = '%d\n%b\n%Y'
if not has_level_label(year_start, vmin_orig):
if not has_level_label(month_start, vmin_orig):
info_fmt[first_label(day_start)] = '%d\n%b\n%Y'
else:
info_fmt[first_label(month_start)] = '%d\n%b\n%Y'
# Case 2. Less than three months
elif span <= periodsperyear // 4:
month_start = period_break(dates_, 'month')
info_maj[month_start] = True
if freq < _c.FR_HR:
info['min'] = True
else:
day_start = period_break(dates_, 'day')
info['min'][day_start] = True
week_start = period_break(dates_, 'week')
year_start = period_break(dates_, 'year')
info_fmt[week_start] = '%d'
info_fmt[month_start] = '\n\n%b'
info_fmt[year_start] = '\n\n%b\n%Y'
if not has_level_label(year_start, vmin_orig):
if not has_level_label(month_start, vmin_orig):
info_fmt[first_label(week_start)] = '\n\n%b\n%Y'
else:
info_fmt[first_label(month_start)] = '\n\n%b\n%Y'
# Case 3. Less than 14 months ...............
elif span <= 1.15 * periodsperyear:
year_start = period_break(dates_, 'year')
month_start = period_break(dates_, 'month')
week_start = period_break(dates_, 'week')
info_maj[month_start] = True
info_min[week_start] = True
info_min[year_start] = False
info_min[month_start] = False
info_fmt[month_start] = '%b'
info_fmt[year_start] = '%b\n%Y'
if not has_level_label(year_start, vmin_orig):
info_fmt[first_label(month_start)] = '%b\n%Y'
# Case 4. Less than 2.5 years ...............
elif span <= 2.5 * periodsperyear:
year_start = period_break(dates_, 'year')
quarter_start = period_break(dates_, 'quarter')
month_start = period_break(dates_, 'month')
info_maj[quarter_start] = True
info_min[month_start] = True
info_fmt[quarter_start] = '%b'
info_fmt[year_start] = '%b\n%Y'
# Case 4. Less than 4 years .................
elif span <= 4 * periodsperyear:
year_start = period_break(dates_, 'year')
month_start = period_break(dates_, 'month')
info_maj[year_start] = True
info_min[month_start] = True
info_min[year_start] = False
month_break = dates_[month_start].month
jan_or_jul = month_start[(month_break == 1) | (month_break == 7)]
info_fmt[jan_or_jul] = '%b'
info_fmt[year_start] = '%b\n%Y'
# Case 5. Less than 11 years ................
elif span <= 11 * periodsperyear:
year_start = period_break(dates_, 'year')
quarter_start = period_break(dates_, 'quarter')
info_maj[year_start] = True
info_min[quarter_start] = True
info_min[year_start] = False
info_fmt[year_start] = '%Y'
# Case 6. More than 12 years ................
else:
year_start = period_break(dates_, 'year')
year_break = dates_[year_start].years
nyears = span / periodsperyear
(min_anndef, maj_anndef) = _get_default_annual_spacing(nyears)
major_idx = year_start[(year_break % maj_anndef == 0)]
info_maj[major_idx] = True
minor_idx = year_start[(year_break % min_anndef == 0)]
info_min[minor_idx] = True
info_fmt[major_idx] = '%Y'
#............................................
return info
import scikits.statsmodels.api as sm import numpy as np import pandas # Getting started # --------------- data = sm.datasets.sunspots.load() # Right now an annual date series must be datetimes at the end of the year. # We can use scikits.timeseries and datetime to create this array. import datetime import scikits.timeseries as ts dates = ts.date_array(start_date=1700, length=len(data.endog), freq='A') # To make an array of datetime types, we need an integer array of ordinals #.. from datetime import datetime #.. dt_dates = dates.toordinal().astype(int) #.. dt_dates = np.asarray([datetime.fromordinal(i) for i in dt_dates]) dt_dates = dates.tolist() # Using Pandas # ------------ # Make a pandas TimeSeries or DataFrame endog = pandas.Series(data.endog, index=dt_dates) # and instantiate the model ar_model = sm.tsa.AR(endog, freq='A')
import datetime
from matplotlib.finance import quotes_historical_yahoo
import scikits.timeseries as ts
import scikits.timeseries.lib.tstables as tstab
startdate = datetime.date(2002, 1, 5)
enddate = datetime.date(2003, 12, 1)
# retrieve data from yahoo.
# Data format is [(d, open, close, high, low, volume), ...] where d is
# a floating point representation of the number of days since 01-01-01 UTC
quotes = quotes_historical_yahoo('INTC', startdate, enddate)
# Create a DateArray of daily dates and convert it to business day frequency
dates = ts.date_array([q[0] for q in quotes], freq='DAILY').asfreq('BUSINESS')
opens = [q[1] for q in quotes]
# opens: the data portion of the timeserie
# dates: the date portion of the timeserie
raw_series = ts.time_series(opens, dates)
test_series = raw_series
#test_series = ts.fill_missing_dates(raw_series, fill_value=-1)
# Write to a PyTables file
output_dir = '../timeseries'
try:
os.mkdir(output_dir)
except OSError:
pass
* pandas is missing GroupBy in the docs, but the docstring is helpful
* both la and pandas handle datetime objects as object arrays
* tabular requires conversion to structured dtype, but easy helper
functions or methods are available in scikits.timeseries and tabular
* not too bad for a first try
Created on Sat Jan 30 08:33:11 2010
Author: josef-pktd
"""
from statsmodels.compat.python import lrange, zip
import numpy as np
import scikits.timeseries as ts
s = ts.time_series([1,2,3,4,5],
dates=ts.date_array(["2001-01","2001-01",
"2001-02","2001-03","2001-03"],freq="M"))
print('\nUsing la')
import la
dta = la.larry(s.data, label=[lrange(len(s.data))])
dat = la.larry(s.dates.tolist(), label=[lrange(len(s.data))])
s2 = ts.time_series(dta.group_mean(dat).x,dates=ts.date_array(dat.x,freq="M"))
s2u = ts.remove_duplicated_dates(s2)
print(repr(s))
print(dat)
print(repr(s2))
print(repr(s2u))
print('\nUsing pandas')
import pandas
"""
Look at some macro plots, then do some VARs and IRFs.
"""
import numpy as np
import statsmodels.api as sm
import scikits.timeseries as ts
import scikits.timeseries.lib.plotlib as tplt
data = sm.datasets.macrodata.load(as_pandas=False)
data = data.data
### Create Timeseries Representations of a few vars
dates = ts.date_array(start_date=ts.Date('Q', year=1959, quarter=1),
end_date=ts.Date('Q', year=2009, quarter=3))
ts_data = data[['realgdp','realcons','cpi']].view(float).reshape(-1,3)
ts_data = np.column_stack((ts_data, (1 - data['unemp']/100) * data['pop']))
ts_series = ts.time_series(ts_data, dates)
fig = tplt.tsfigure()
fsp = fig.add_tsplot(221)
fsp.tsplot(ts_series[:,0],'-')
fsp.set_title("Real GDP")
fsp = fig.add_tsplot(222)
fsp.tsplot(ts_series[:,1],'r-')
fsp.set_title("Real Consumption")
fsp = fig.add_tsplot(223)
fsp.tsplot(ts_series[:,2],'g-')
def _daily_finder(vmin, vmax, freq):
periodsperday = -1
if freq >= _c.FR_HR:
if freq == _c.FR_SEC:
periodsperday = 24 * 60 * 60
elif freq == _c.FR_MIN:
periodsperday = 24 * 60
elif freq == _c.FR_HR:
periodsperday = 24
else:
raise ValueError("unexpected frequency: %s" % check_freq_str(freq))
periodsperyear = 365 * periodsperday
periodspermonth = 28 * periodsperday
elif freq == _c.FR_BUS:
periodsperyear = 261
periodspermonth = 19
elif freq == _c.FR_DAY:
periodsperyear = 365
periodspermonth = 28
elif get_freq_group(freq) == _c.FR_WK:
periodsperyear = 52
periodspermonth = 3
elif freq == _c.FR_UND:
periodsperyear = 100
periodspermonth = 10
else:
raise ValueError("unexpected frequency")
# save this for later usage
vmin_orig = vmin
(vmin, vmax) = (int(vmin), int(vmax))
span = vmax - vmin + 1
dates_ = date_array(start_date=Date(freq, vmin), end_date=Date(freq, vmax))
# Initialize the output
info = np.zeros(span, dtype=[("val", int), ("maj", bool), ("min", bool), ("fmt", "|S20")])
info["val"][:] = np.arange(vmin, vmax + 1)
info["fmt"][:] = ""
info["maj"][[0, -1]] = True
# .. and set some shortcuts
info_maj = info["maj"]
info_min = info["min"]
info_fmt = info["fmt"]
def first_label(label_flags):
if (label_flags[0] == 0) and (label_flags.size > 1) and ((vmin_orig % 1) > 0.0):
return label_flags[1]
else:
return label_flags[0]
# Case 1. Less than a month
if span <= periodspermonth:
day_start = period_break(dates_, "day")
month_start = period_break(dates_, "month")
def _hour_finder(label_interval, force_year_start):
_hour = dates_.hour
_prev_hour = (dates_ - 1).hour
hour_start = (_hour - _prev_hour) != 0
info_maj[day_start] = True
info_min[hour_start & (_hour % label_interval == 0)] = True
year_start = period_break(dates_, "year")
info_fmt[hour_start & (_hour % label_interval == 0)] = "%H:%M"
info_fmt[day_start] = "%H:%M\n%d-%b"
info_fmt[year_start] = "%H:%M\n%d-%b\n%Y"
if force_year_start and not has_level_label(year_start, vmin_orig):
info_fmt[first_label(day_start)] = "%H:%M\n%d-%b\n%Y"
def _minute_finder(label_interval):
hour_start = period_break(dates_, "hour")
_minute = dates_.minute
_prev_minute = (dates_ - 1).minute
minute_start = (_minute - _prev_minute) != 0
info_maj[hour_start] = True
info_min[minute_start & (_minute % label_interval == 0)] = True
year_start = period_break(dates_, "year")
info_fmt = info["fmt"]
info_fmt[minute_start & (_minute % label_interval == 0)] = "%H:%M"
info_fmt[day_start] = "%H:%M\n%d-%b"
info_fmt[year_start] = "%H:%M\n%d-%b\n%Y"
def _second_finder(label_interval):
minute_start = period_break(dates_, "minute")
_second = dates_.second
_prev_second = (dates_ - 1).second
second_start = (_second - _prev_second) != 0
info["maj"][minute_start] = True
info["min"][second_start & (_second % label_interval == 0)] = True
year_start = period_break(dates_, "year")
info_fmt = info["fmt"]
info_fmt[second_start & (_second % label_interval == 0)] = "%H:%M:%S"
info_fmt[day_start] = "%H:%M:%S\n%d-%b"
info_fmt[year_start] = "%H:%M:%S\n%d-%b\n%Y"
if span < periodsperday / 12000.0:
_second_finder(1)
elif span < periodsperday / 6000.0:
_second_finder(2)
elif span < periodsperday / 2400.0:
_second_finder(5)
elif span < periodsperday / 1200.0:
_second_finder(10)
elif span < periodsperday / 800.0:
_second_finder(15)
elif span < periodsperday / 400.0:
_second_finder(30)
elif span < periodsperday / 150.0:
_minute_finder(1)
elif span < periodsperday / 70.0:
_minute_finder(2)
elif span < periodsperday / 24.0:
_minute_finder(5)
elif span < periodsperday / 12.0:
_minute_finder(15)
elif span < periodsperday / 6.0:
_minute_finder(30)
elif span < periodsperday / 2.5:
_hour_finder(1, False)
elif span < periodsperday / 1.5:
_hour_finder(2, False)
elif span < periodsperday * 1.25:
_hour_finder(3, False)
elif span < periodsperday * 2.5:
_hour_finder(6, True)
elif span < periodsperday * 4:
_hour_finder(12, True)
else:
info_maj[month_start] = True
info_min[day_start] = True
year_start = period_break(dates_, "year")
info_fmt = info["fmt"]
info_fmt[day_start] = "%d"
info_fmt[month_start] = "%d\n%b"
info_fmt[year_start] = "%d\n%b\n%Y"
if not has_level_label(year_start, vmin_orig):
if not has_level_label(month_start, vmin_orig):
info_fmt[first_label(day_start)] = "%d\n%b\n%Y"
else:
info_fmt[first_label(month_start)] = "%d\n%b\n%Y"
# Case 2. Less than three months
elif span <= periodsperyear // 4:
month_start = period_break(dates_, "month")
info_maj[month_start] = True
if freq < _c.FR_HR:
info["min"] = True
else:
day_start = period_break(dates_, "day")
info["min"][day_start] = True
week_start = period_break(dates_, "week")
year_start = period_break(dates_, "year")
info_fmt[week_start] = "%d"
info_fmt[month_start] = "\n\n%b"
info_fmt[year_start] = "\n\n%b\n%Y"
if not has_level_label(year_start, vmin_orig):
if not has_level_label(month_start, vmin_orig):
info_fmt[first_label(week_start)] = "\n\n%b\n%Y"
else:
info_fmt[first_label(month_start)] = "\n\n%b\n%Y"
# Case 3. Less than 14 months ...............
elif span <= 1.15 * periodsperyear:
year_start = period_break(dates_, "year")
month_start = period_break(dates_, "month")
week_start = period_break(dates_, "week")
info_maj[month_start] = True
info_min[week_start] = True
info_min[year_start] = False
info_min[month_start] = False
info_fmt[month_start] = "%b"
info_fmt[year_start] = "%b\n%Y"
if not has_level_label(year_start, vmin_orig):
info_fmt[first_label(month_start)] = "%b\n%Y"
# Case 4. Less than 2.5 years ...............
elif span <= 2.5 * periodsperyear:
year_start = period_break(dates_, "year")
quarter_start = period_break(dates_, "quarter")
month_start = period_break(dates_, "month")
info_maj[quarter_start] = True
info_min[month_start] = True
info_fmt[quarter_start] = "%b"
info_fmt[year_start] = "%b\n%Y"
# Case 4. Less than 4 years .................
elif span <= 4 * periodsperyear:
year_start = period_break(dates_, "year")
month_start = period_break(dates_, "month")
info_maj[year_start] = True
info_min[month_start] = True
info_min[year_start] = False
month_break = dates_[month_start].month
jan_or_jul = month_start[(month_break == 1) | (month_break == 7)]
info_fmt[jan_or_jul] = "%b"
info_fmt[year_start] = "%b\n%Y"
# Case 5. Less than 11 years ................
elif span <= 11 * periodsperyear:
year_start = period_break(dates_, "year")
quarter_start = period_break(dates_, "quarter")
info_maj[year_start] = True
info_min[quarter_start] = True
info_min[year_start] = False
info_fmt[year_start] = "%Y"
# Case 6. More than 12 years ................
else:
year_start = period_break(dates_, "year")
year_break = dates_[year_start].years
nyears = span / periodsperyear
(min_anndef, maj_anndef) = _get_default_annual_spacing(nyears)
major_idx = year_start[(year_break % maj_anndef == 0)]
info_maj[major_idx] = True
minor_idx = year_start[(year_break % min_anndef == 0)]
info_min[minor_idx] = True
info_fmt[major_idx] = "%Y"
# ............................................
return info
# t = timer()
# mod_tb = tb.lpc(y, 2)
# t_end = timer()
# print str(t_end - t) + " seconds for talkbox.lpc"
# print """For higher lag lengths ours quickly fills up memory and starts
#thrashing the swap. Should we include talkbox C code or Cythonize the
#Levinson recursion algorithm?"""
## Try with a pandas series
import pandas
import scikits.timeseries as ts
d1 = ts.Date(year=1700, freq='A')
#NOTE: have to have yearBegin offset for annual data until parser rewrite
#should this be up to the user, or should it be done in TSM init?
#NOTE: not anymore, it's end of year now
ts_dr = ts.date_array(start_date=d1, length=len(sunspots.endog))
pandas_dr = pandas.DateRange(start=d1.datetime,
periods=len(sunspots.endog), timeRule='A@DEC')
#pandas_dr = pandas_dr.shift(-1, pandas.datetools.yearBegin)
dates = np.arange(1700, 1700 + len(sunspots.endog))
dates = ts.date_array(dates, freq='A')
#sunspots = pandas.Series(sunspots.endog, index=dates)
#NOTE: pandas only does business days for dates it looks like
import datetime
dt_dates = np.asarray(lmap(datetime.datetime.fromordinal,
ts_dr.toordinal().astype(int)))
sunspots = pandas.Series(sunspots.endog, index=dt_dates)
#NOTE: pandas can't handle pre-1900 dates
def getMsgStats(fromFunc=False):
companyId = session.get("companyId")
condFilter = [ 1==1 ]
if companyId:
condFilter.append(Message.companyId==companyId)
if request.form.get('startDate'):
condFilter.append(Message.rdate > request.form['startDate'])
db_result = db_session.query(func.date(Message.rdate).label("rdate"), func.count(func.IF(Message.msgType==1,1,None)), func.count(func.IF(Message.msgType==2,1,None)))\
.group_by( func.date(Message.rdate) )\
.filter(func.date(Message.rdate) <= request.form['endDate'])\
.filter(*condFilter).all()
db_dates = list()
db_cnt1 = list()
db_cnt2 = list()
for row in db_result:
db_dates.append(row[0])
db_cnt1.append(row[1])
db_cnt2.append(row[2])
stats = list()
dateAry = ts.date_array(db_dates, freq='D')
timeSrz1 = ts.time_series(db_cnt1, dateAry)
timeSrz2 = ts.time_series(db_cnt2, dateAry)
fillVals1 = timeSrz1.fill_missing_dates(fill_value=0)
fillVals2 = timeSrz2.fill_missing_dates(fill_value=0)
fillVals1 = fillVals1.filled(0)
fillVals2 = fillVals2.filled(0)
if request.form.get('startDate'):
startDate = datetime.strptime(request.form['startDate'], '%Y-%m-%d')
else :
startDate = '2015-10-27'
endDate = datetime.strptime(request.form['endDate'], '%Y-%m-%d')
fillDateAry = ts.date_array(start_date=startDate, end_date=endDate, freq='D')
firstDayIdx = 0
for day in fillDateAry :
if len(dateAry)==0 or dateAry[0] != fillDateAry[firstDayIdx] :
stats.append({ "date" : fillDateAry[firstDayIdx].strftime("%m/%d"), "msg1" : 0, "msg2" : 0 })
firstDayIdx += 1
continue
else :
break
for idx, msg1cnt in enumerate(fillVals1):
msg2cnt = fillVals2[idx]
stats.append({ "date" : fillDateAry[firstDayIdx + idx].strftime("%m/%d"), "msg1" : msg1cnt, "msg2" : msg2cnt })
statsLen = len(stats)
for i in range(len(fillDateAry) - len(stats)) :
stats.append({ "date" : fillDateAry[statsLen + i].strftime("%m/%d"), "msg1" : 0, "msg2" : 0 })
if fromFunc==True :
return stats
else :
return jsonify({"stats" : stats})
_attrs)
_methods = {'predict': 'dates'}
_wrap_methods = wrap.union_dicts(
base.LikelihoodResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(TimeSeriesResultsWrapper, TimeSeriesModelResults)
if __name__ == "__main__":
import scikits.statsmodels.api as sm
import datetime
import pandas
data = sm.datasets.macrodata.load()
#make a DataFrame
#TODO: attach a DataFrame to some of the datasets, for quicker use
dates = [str(int(x[0])) +':'+ str(int(x[1])) \
for x in data.data[['year','quarter']]]
try:
import scikits.timeseries as ts
ts_dates = date_array(start_date=Date(year=1959, quarter=1, freq='Q'),
length=len(data.data))
except:
pass
df = pandas.DataFrame(data.data[['realgdp', 'realinv', 'realcons']],
index=dates)
ex_mod = TimeSeriesModel(df)
#ts_series = pandas.TimeSeries()
def getGoodworkStats(fromFunc=False):
db_dates1 = list()
db_dates2 = list()
db_dates3 = list()
db_cnt1 = list()
db_cnt2 = list()
db_cnt3 = list()
companyId = session.get("companyId")
condFilter = [ 1==1 ]
if companyId:
condFilter.append(GoodPost.companyId==companyId)
if request.form.get('startDate'):
condFilter.append(GoodPost.rdate > request.form['startDate'])
db_result1 = db_session.query(func.date(GoodPost.rdate).label("rdate"), func.count())\
.group_by( func.date(GoodPost.rdate) )\
.filter(func.date(GoodPost.rdate) <= request.form['endDate'])\
.filter(*condFilter).all()
for row in db_result1:
db_dates1.append(row[0])
db_cnt1.append(row[1])
condFilter2 = [ 1==1 ]
if request.form.get('startDate'):
condFilter2.append(GoodLike.rdate > request.form['startDate'])
if companyId:
db_result2 = db_session.query(func.date(GoodLike.rdate).label("rdate"), func.count())\
.join(User, User.userId==GoodLike.userId)\
.group_by( func.date(GoodLike.rdate) )\
.filter(func.date(GoodLike.rdate) <= request.form['endDate'])\
.filter(User.companyId==companyId)\
.filter(*condFilter2).all()
else :
db_result2 = db_session.query(func.date(GoodLike.rdate).label("rdate"), func.count())\
.group_by( func.date(GoodLike.rdate) )\
.filter(func.date(GoodLike.rdate) <= request.form['endDate'])\
.filter(*condFilter2).all()
for row in db_result2:
db_dates2.append(row[0])
db_cnt2.append(row[1])
condFilter3 = [ 1==1 ]
if request.form.get('startDate'):
condFilter3.append(GoodReply.rdate > request.form['startDate'])
if companyId:
db_result3 = db_session.query(func.date(GoodReply.rdate).label("rdate"), func.count())\
.join(User, User.userId==GoodReply.userId)\
.group_by( func.date(GoodReply.rdate) )\
.filter(func.date(GoodReply.rdate) <= request.form['endDate'])\
.filter(User.companyId==companyId)\
.filter(*condFilter3).all()
else :
db_result3 = db_session.query(func.date(GoodReply.rdate).label("rdate"), func.count())\
.group_by( func.date(GoodReply.rdate) )\
.filter(func.date(GoodReply.rdate) <= request.form['endDate'])\
.filter(*condFilter3).all()
for row in db_result3:
db_dates3.append(row[0])
db_cnt3.append(row[1])
dateAry1 = ts.date_array(db_dates1, freq='D')
dateAry2 = ts.date_array(db_dates2, freq='D')
dateAry3 = ts.date_array(db_dates3, freq='D')
timeSrz1 = ts.time_series(db_cnt1, dateAry1)
timeSrz2 = ts.time_series(db_cnt2, dateAry2)
timeSrz3 = ts.time_series(db_cnt3, dateAry3)
fillVals1 = timeSrz1.fill_missing_dates(fill_value=0)
fillVals2 = timeSrz2.fill_missing_dates(fill_value=0)
fillVals3 = timeSrz3.fill_missing_dates(fill_value=0)
fillVals1 = fillVals1.filled(0)
fillVals2 = fillVals2.filled(0)
fillVals3 = fillVals3.filled(0)
if request.form.get('startDate'):
startDate = datetime.strptime(request.form['startDate'], '%Y-%m-%d')
else :
startDate = '2015-10-27'
endDate = datetime.strptime(request.form['endDate'], '%Y-%m-%d')
fillDateAry = ts.date_array(start_date=startDate, end_date=endDate, freq='D')
if len(dateAry1)>0 and len(dateAry2)>0 and dateAry1[0] > dateAry2[0] :
if len(dateAry3)>0 and dateAry2[0] > dateAry3[0] :
minDate = dateAry3[0]
else :
minDate = dateAry2[0]
else :
if (len(dateAry1)>0 and len(dateAry3)>0 and dateAry1[0] > dateAry3[0]) or len(dateAry1)==0 :
minDate = len(dateAry3)>0 and dateAry3[0] or None
else:
minDate = len(dateAry1)>0 and dateAry1[0] or None
postStats = list()
likeStats = list()
replyStats = list()
firstDayIdx1 = 0
for day in fillDateAry :
if minDate != fillDateAry[firstDayIdx1] :
postStats.append({ "date" : fillDateAry[firstDayIdx1].strftime("%m/%d"), "post" : 0 })
likeStats.append({ "date" : fillDateAry[firstDayIdx1].strftime("%m/%d"), "like" : 0 })
replyStats.append({ "date" : fillDateAry[firstDayIdx1].strftime("%m/%d"), "reply" : 0 })
firstDayIdx1 += 1
continue
else :
break
firstDayIdx2 = 0
for day in fillDateAry :
if len(dateAry2)==0 or dateAry2[0] != fillDateAry[firstDayIdx2] :
if len(dateAry1)>0 and minDate!=dateAry1[0]:
postStats.append({ "date" : fillDateAry[firstDayIdx2].strftime("%m/%d"), "post" : 0 })
if len(dateAry2)>0 and minDate!=dateAry2[0]:
likeStats.append({ "date" : fillDateAry[firstDayIdx2].strftime("%m/%d"), "like" : 0 })
if len(dateAry3)>0 and minDate!=dateAry3[0]:
replyStats.append({ "date" : fillDateAry[firstDayIdx2].strftime("%m/%d"), "reply" : 0 })
firstDayIdx2 += 1
continue
else :
break
firstDayIdx3 = 0
for day in fillDateAry :
if len(dateAry3)==0 or dateAry3[0] != fillDateAry[firstDayIdx3] :
if len(dateAry1)>0 and minDate!=dateAry1[0]:
postStats.append({ "date" : fillDateAry[firstDayIdx3].strftime("%m/%d"), "post" : 0 })
if len(dateAry2)>0 and minDate!=dateAry2[0]:
likeStats.append({ "date" : fillDateAry[firstDayIdx3].strftime("%m/%d"), "like" : 0 })
if len(dateAry3)>0 and minDate!=dateAry3[0]:
replyStats.append({ "date" : fillDateAry[firstDayIdx3].strftime("%m/%d"), "reply" : 0 })
firstDayIdx3 += 1
continue
else :
break
for idx, post in enumerate(fillVals1):
postStats.append({ "date" : fillDateAry[firstDayIdx1 + idx].strftime("%m/%d"), "post" : post })
for idx, like in enumerate(fillVals2):
likeStats.append({ "date" : fillDateAry[firstDayIdx2 + idx].strftime("%m/%d"), "like" : like })
for idx, reply in enumerate(fillVals3):
replyStats.append({ "date" : fillDateAry[firstDayIdx3 + idx].strftime("%m/%d"), "reply" : reply })
postLen = len(postStats)
likeLen = len(likeStats)
replyLen = len(replyStats)
for i in range(len(fillDateAry) - postLen) :
postStats.append({ "date" : fillDateAry[postLen + i].strftime("%m/%d"), "post" : 0 })
for i in range(len(fillDateAry) - likeLen) :
likeStats.append({ "date" : fillDateAry[likeLen + i].strftime("%m/%d"), "like" : 0 })
for i in range(len(fillDateAry) - replyLen) :
replyStats.append({ "date" : fillDateAry[replyLen + i].strftime("%m/%d"), "reply" : 0 })
#merge
stats = list()
for idx, postStat in enumerate(postStats):
mergedStats = postStat.copy()
mergedStats.update(likeStats[idx])
mergedStats.update(replyStats[idx])
stats.append(mergedStats)
if fromFunc==True :
return stats
else :
return jsonify({"stats" : stats})
import datetime
from matplotlib.finance import quotes_historical_yahoo
import scikits.timeseries as ts
import scikits.timeseries.lib.tstables as tstab
startdate = datetime.date(2002, 1, 5)
enddate = datetime.date(2003, 12, 1)
# retrieve data from yahoo.
# Data format is [(d, open, close, high, low, volume), ...] where d is
# a floating point representation of the number of days since 01-01-01 UTC
quotes = quotes_historical_yahoo('INTC', startdate, enddate)
# Create a DateArray of daily dates and convert it to business day frequency
dates = ts.date_array([q[0] for q in quotes], freq='DAILY').asfreq('BUSINESS')
opens = [q[1] for q in quotes]
# opens: the data portion of the timeserie
# dates: the date portion of the timeserie
raw_series = ts.time_series(opens, dates)
test_series = raw_series
#test_series = ts.fill_missing_dates(raw_series, fill_value=-1)
# Write to a PyTables file
output_dir = '../timeseries'
try:
os.mkdir(output_dir)
except OSError:
pass
def __call__(self, *tseries, **kwargs):
"""
generate a report. Parameter values are not saved to the Report instance.
Parameters
----------
Accepts same parameters as __init__ method of Report class
"""
option_dict = copy.copy(self.options)
option_dict.update(self.__make_dict(**kwargs))
if len(tseries) == 0:
tseries = self.tseries
def option(kw):
return option_dict.get(kw, _default_options[kw])
dates = option('dates')
header_row = option('header_row')
header_char = option('header_char')
header_justify = option('header_justify')
row_char = option('row_char')
footer_label = option('footer_label')
footer_char = option('footer_char')
footer_func = option('footer_func')
delim = option('delim')
justify = option('justify')
prefix = option('prefix')
postfix = option('postfix')
mask_rep = option('mask_rep')
datefmt = option('datefmt')
fmt_func = option('fmt_func')
wrap_func = option('wrap_func')
col_width = option('col_width')
nls=option('nls')
output=option('output')
fixed_width=option('fixed_width')
if header_row is not None:
has_header=True
if len(header_row) == len(tseries)+1:
# label for date column included
rows = [header_row]
elif len(header_row) == len(tseries):
# label for date column not included
rows = [['']+header_row]
else:
raise ValueError("mismatch with number of headers and series")
else:
has_header=False
rows=[]
if fixed_width:
def _standardize_justify(userspec):
if isinstance(userspec, str):
# justify all columns the the same way
return [userspec for x in range(len(tseries)+1)]
elif isinstance(userspec, list):
if len(userspec) == len(tseries):
# justification for date column not included, so set that
# to left by default
return ['left'] + userspec
else:
raise ValueError("invalid `justify` specification")
if justify is not None:
justify = _standardize_justify(justify)
else:
# default column justification
justify = ['left']
for ser in tseries:
if ser.dtype.char in 'SUO': justify.append('left')
else: justify.append('right')
if header_justify is not None:
header_justify = _standardize_justify(header_justify)
else:
# default column justification
header_justify = ['left' for x in range(len(tseries)+1)]
else:
justify = ['none' for x in range(len(tseries)+1)]
header_justify = justify
if datefmt is None:
def datefmt_func(date): return str(date)
else:
def datefmt_func(date): return date.strftime(datefmt)
if dates is None:
tseries = ts.align_series(*tseries)
dates = ts.date_array(start_date=tseries[0].start_date,
end_date=tseries[0].end_date)
else:
tseries = ts.align_series(start_date=dates[0], end_date=dates[-1], *tseries)
if isinstance(fmt_func, list):
fmt_func = [fmt_func_wrapper(f, mask_rep) for f in fmt_func]
else:
fmt_func = [fmt_func_wrapper(fmt_func, mask_rep)]*len(tseries)
def wrap_func_default(func):
if func is None: return lambda x:x
else: return func
if isinstance(wrap_func, list):
if len(wrap_func) == len(tseries):
wrap_func = [lambda x: x] + wrap_func
wrap_func = [wrap_func_default(func) for func in wrap_func]
else:
wrap_func = [wrap_func_default(wrap_func) for x in range(len(tseries)+1)]
if isinstance(col_width, list):
if len(col_width) == len(tseries):
col_width = [None] + col_width
else:
col_width = [col_width for x in range(len(tseries)+1)]
_sd = dates[0]
for d in dates:
rows.append(
[datefmt_func(d)] + \
[fmt_func[i](ser.series[d - _sd]) \
for i, ser in enumerate(tseries)]
)
if footer_func is not None:
has_footer=True
if not isinstance(footer_func, list):
footer_func = [footer_func]*len(tseries)
if footer_label is None: footer_label = ['']
else: footer_label = [footer_label]
footer_data = []
has_missing = dates.has_missing_dates()
for i, ser in enumerate(tseries):
if footer_func[i] is None:
footer_data.append('')
else:
if has_missing: _input = ser[dates]
else: _input = ser.series
footer_data.append(fmt_func[i](footer_func[i](_input)))
rows.append(footer_label + footer_data)
else:
has_footer=False
def rowWrapper(row):
newRows = [wrap_func[i](item).split('\n') for i, item in enumerate(row)]
return [[(substr or '') for substr in item] for item in map(None, *newRows)]
# break each logical row into one or more physical ones
logicalRows = [rowWrapper(row) for row in rows]
numLogicalRows = len(logicalRows)
# columns of physical rows
columns = map(None,*reduce(operator.add,logicalRows))
numCols = len(columns)
colNums = list(range(numCols))
# get the maximum of each column by the string length of its items
maxWidths = [max(col_width[i], *[len(str(item)) for item in column])
for i, column in enumerate(columns)]
def getSeparator(char, separate):
if char is not None and separate:
return char * (len(prefix) + len(postfix) + sum(maxWidths) + \
len(delim)*(len(maxWidths)-1))
else:
return None
header_separator = getSeparator(header_char, has_header)
footer_separator = getSeparator(footer_char, has_footer)
row_separator = getSeparator(row_char, True)
# select the appropriate justify method
justify_funcs = {'center':str.center, 'right':str.rjust, 'left':str.ljust,
'none':(lambda text, width: text)}
if has_header and has_footer:
data_start = 1
data_end = numLogicalRows-3
elif has_header:
data_start = 1
data_end = numLogicalRows-2
elif has_footer:
data_start = 0
data_end = numLogicalRows-3
else:
data_start = 0
data_end = numLogicalRows-2
for rowNum, physicalRows in enumerate(logicalRows):
if rowNum == 0 and header_separator:
_justify = header_justify
else:
_justify = justify
def apply_justify(colNum, item, width):
jfunc_key = str(_justify[colNum]).lower()
jfunc = justify_funcs[jfunc_key]
return jfunc(str(item), width)
for row in physicalRows:
output.write(
prefix + \
delim.join([
apply_justify(cn, item, width) \
for (cn, item, width) in zip(colNums, row, maxWidths)
]) + \
postfix + nls)
if row_separator and (data_start <= rowNum <= data_end):
output.write(row_separator + nls)
elif header_separator and rowNum < data_start:
output.write(header_separator + nls)
elif footer_separator and rowNum == data_end + 1:
output.write(footer_separator + nls)
def getAccessStats(fromFunc=False):
companyId = session.get("companyId")
condFilter = [1==1]
if request.form.get('startDate'):
condFilter.append(LogAccess.rdate > request.form['startDate'])
if companyId:
condFilter.append(User.companyId==companyId)
db_result = db_session.query(func.date(LogAccess.rdate).label("rdate"), func.count(), func.count(distinct(LogAccess.userId)))\
.group_by( func.date(LogAccess.rdate) )\
.filter(*condFilter)\
.filter(func.date(LogAccess.rdate) <= request.form['endDate'])\
.all()
db_dates = list()
db_pv = list()
db_uv = list()
for row in db_result:
db_dates.append(row[0])
db_pv.append(row[1])
db_uv.append(row[2])
stats = list()
dateAry = ts.date_array(db_dates, freq='D')
timeSrz1 = ts.time_series(db_pv, dateAry)
timeSrz2 = ts.time_series(db_uv, dateAry)
fillVals1 = timeSrz1.fill_missing_dates(fill_value=0)
fillVals2 = timeSrz2.fill_missing_dates(fill_value=0)
fillVals1 = fillVals1.filled(0)
fillVals2 = fillVals2.filled(0)
if request.form.get('startDate'):
startDate = datetime.strptime(request.form['startDate'], '%Y-%m-%d')
else :
startDate = '2015-10-27'
endDate = datetime.strptime(request.form['endDate'], '%Y-%m-%d')
fillDateAry = ts.date_array(start_date=startDate, end_date=endDate, freq='D')
firstDayIdx = 0
for day in fillDateAry :
if len(dateAry)==0 or dateAry[0] != fillDateAry[firstDayIdx] :
stats.append({ "date" : fillDateAry[firstDayIdx].strftime("%m/%d"), "pv" : 0, "uv" : 0 })
firstDayIdx += 1
continue
else :
break
for idx, pv in enumerate(fillVals1):
uv = fillVals2[idx]
stats.append({ "date" : fillDateAry[firstDayIdx + idx].strftime("%m/%d"), "pv" : pv, "uv" : uv })
statsLen = len(stats)
for i in range(len(fillDateAry) - len(stats)) :
stats.append({ "date" : fillDateAry[statsLen + i].strftime("%m/%d"), "pv" : 0, "uv" : 0 })
if fromFunc==True :
return stats
else :
return jsonify({"stats" : stats})
def _get_tseriesA(freq,date_values,kw):
v = [ int(d) for d in date_values.flatten() if not np.isnan(d) ]
D = [ ts.Date(freq=str(freq),value=_v) for _v in v]
date_array = ts.date_array(D)
return date_array
garch11:
[ 1.01258264 0.24149155 0.50479994]
-2056.3877404
R include_constant=False
Final Estimate:
LLH: 2056.397 norm LLH: 2.056397
omega alpha1 beta1
1.0123560 0.2409589 0.5049154
'''
erro, ho, etaxo = generate_gjrgarch(20,
ar,
ma,
mu=0.04,
scale=0.01,
varinnovation=np.ones(20))
if 'sp500' in examples:
import tabular as tb
import scikits.timeseries as ts
a = tb.loadSV(r'C:\Josef\work-oth\gspc_table.csv')
s = ts.time_series(a[0]['Close'][::-1],
dates=ts.date_array(a[0]['Date'][::-1], freq="D"))
sp500 = a[0]['Close'][::-1]
sp500r = np.diff(np.log(sp500))
#plt.show()
* not too bad for a first try
Created on Sat Jan 30 08:33:11 2010
Author: josef-pktd
"""
from statsmodels.compat.python import lrange
import numpy as np
import scikits.timeseries as ts
import la
import pandas
import tabular as tb
from finance import msft, ibm # hack to make it run as standalone
s = ts.time_series([1,2,3,4,5],
dates=ts.date_array(["2001-01","2001-01",
"2001-02","2001-03","2001-03"],freq="M"))
print('\nUsing la')
dta = la.larry(s.data, label=[lrange(len(s.data))])
dat = la.larry(s.dates.tolist(), label=[lrange(len(s.data))])
s2 = ts.time_series(dta.group_mean(dat).x,dates=ts.date_array(dat.x,freq="M"))
s2u = ts.remove_duplicated_dates(s2)
print(repr(s))
print(dat)
print(repr(s2))
print(repr(s2u))
print('\nUsing pandas')
pdta = pandas.DataFrame(s.data, np.arange(len(s.data)), [1])
pa = pdta.groupby(dict(zip(np.arange(len(s.data)),
s.dates.tolist()))).aggregate(np.mean)