def get_time_range_from_files(files):
"""Get time range from input files."""
times = [re.findall('\d\d\d\d\d\d+', fname) for fname in files]
t_1 = [t1 for t1, t2 in times]
t_2 = [t2 for t1, t2 in times]
t_min, t_max = min(t_1), max(t_2)
#times = np.array(times, 'i4').reshape(len(times), 2)
#t_min, t_max = str(times.min()), str(times.max())
y1, m1 = int(t_min[:4]), int(t_min[4:6])
y2, m2 = int(t_max[:4]), int(t_max[4:6])
# seasonal
if len(t_min) > 6 and len(t_max) > 6:
y1, m1 = get_season(y1, m1, get_month=1)
y2, m2 = get_season(y2, m2, get_month=1)
start = '%d/%d/%d' % (y1, m1, 15)
end = '%d/%d/%d' % (y2, m2, 15)
return pn.DateRange(start,
end,
offset=pn.datetools.DateOffset(months=3))
# monthly
else:
print t_min
start = '%d/%d/%d' % (y1, m1, 15)
end = '%d/%d/%d' % (y2, m2, 15)
return pn.DateRange(start,
end,
offset=pn.datetools.DateOffset(months=1))
def analysis(self, year, month, force=False):
if year and month:
self.range = pandas.DateRange(datetime.datetime(year,month,1,12)
, periods = 1
, offset = pandas.DateOffset(months=1)
)
else:
self.range = pandas.DateRange(self.start_date
, end = self.end_date
, offset = pandas.DateOffset(months=1)
)
mr_dict = self.map_reduce(force=force)
return self.cohort_analysis(mr_dict)
def signups(db, end=None, period='hours', start=None):
if not end: end = now()
end = datetime.datetime.fromtimestamp(end)
end = end.replace(minute=0, second=0, microsecond=0)
if not start:
start = end - pandas.DateOffset(hours=48)
else:
start = datetime.datetime.fromtimestamp(start)
start = start.replace(minute=0, second=0, microsecond=0)
if not period is 'hours': start = start.replace(hour=8)
hourly = pandas.DateRange(start=start, end=end, offset=pandas.DateOffset(**{period: 1}))
start_epoch = datetime_to_int(hourly[0])
end_epoch = datetime_to_int(hourly[-1] + hourly.offset)
spec = {'created': {'$gt': start_epoch, '$lt': end_epoch}}
def group_data(cursor):
contact_times = sorted([datetime.datetime.utcfromtimestamp(c['created']) for c in cursor])
data = pandas.Series(1, contact_times)
data = pandas.Series(data.groupby(hourly.asof).sum())
return data.reindex(index=hourly, fill_value=0)
cursor = db.contact_log.find(spec, {'created': True})
contacts = group_data(cursor)
spec.update({'user_created': {'$exists': True}})
cursor = db.contact_log.find(spec, {'created': True})
created_contacts = group_data(cursor)
return { 'index': [time.mktime(x.timetuple()) for x in hourly.tolist()]
, 'total': contacts.values.tolist()
, 'active': created_contacts.values.tolist()
, 'ratio': (created_contacts / contacts).values.tolist()
}
def load_time_series(symbol,
start_date=None,
end_date=None,
downsample_days=1):
log.info("loading %s for %s to %s" % (symbol, start_date, end_date))
filename = "%s.csv" % symbol
if not os.path.exists(filename):
fetch.fetch_data(symbol)
data = pandas.read_csv(filename, parse_dates=True, index_col=0)
data = data.drop(["Open", "High", "Low", "Close", "Volume"], axis=1)
data = data.rename(columns={"Adj Close": symbol})
data = data.sort()
if data.index[0] > start_date:
log.warning("no data for %s before %s" % (symbol, data.index[0]))
return None
data = data.truncate(before=start_date, after=end_date)
log.info("%d rows after truncating" % len(data))
# downsample if necessary
if downsample_days > 1:
drange = pandas.DateRange(start_date,
end_date,
offset=downsample_days * datetools.day)
grouped = data.groupby(drange.asof)
means = grouped.mean()
log.info("%d rows after downsampling" % len(means))
return means
else:
return data
def fetch_price_frame(self,startdate,enddate):
dts=ps.DateRange(start=startdate,end=enddate,offset=bday)
i=0
dividends=[]
prices=[]
for dt in dts:
irs=InterestRate.objects.filter(date__lte=dt,investment=self).order_by('-date')
if len(irs) == 0:
return None
else:
ir=irs[0]
prices.append(Decimal(1.0))
dividends.append(ir.annualrate/365)
data={
'price':prices,
'dividend':dividends,
}
df=ps.DataFrame(data,index=dts)
return df
def table_33():
path = os.path.join(data_path, 'Table3.3.data.txt')
sep = '\s+'
lines = [re.split(sep, l.strip()) for l in open(path)]
y_data = []
f_data = []
saw_f = False
for line in lines:
if line[0] == 'Y':
continue
elif line[0] == 'F':
saw_f = True
continue
# drop year
if saw_f:
f_data.extend(line[1:])
else:
y_data.extend(line[1:])
y_data = np.array(y_data, dtype=float)
f_data = np.array(f_data, dtype=float)
dates = pn.DateRange(datetime(1975, 1, 1),
periods=len(y_data),
timeRule='Q@MAR')
Y = pn.Series(y_data, index=dates)
F = pn.Series(f_data, index=dates)
return Y, F
def download_hist(sleept=10):
"""
download historical data from 1999/01/31 to 2014/05/31
"""
for i in pd.DateRange(dt.datetime(1999,1,1), dt.datetime(2014,6,1), offset=pd.datetools.MonthEnd()):
download(i)
time.sleep(sleept)
def sse_hist(start=dt.datetime(2006, 8, 7), end=dt.datetime.today()):
fail_list = []
for date in pd.DateRange(start, end):
print date
fail_list.append(sse_lhb(date))
time.sleep(10)
print[i for i in fail_list if i]
def _trade_dates(dt_start, dt_end, s_period):
'''
@summary: Generate dates on which we need to trade
@param c_strat: Strategy config class
@param dt_start: Start date
@param dt_end: End date
'''
ldt_timestamps = getNYSEdays(dt_start,
dt_end, dt.timedelta(hours=16) )
# Use pandas reindex method instead
# Note, dates are index as well as values, we select based on index
# but return values since it is a numpy array of datetimes instead of
# pandas specific.
ts_dates = pd.TimeSeries(index=ldt_timestamps, data=ldt_timestamps)
# These are the dates we want
if s_period[:2] == 'BW':
# special case for biweekly
dr_range = pd.DateRange(dt_start, dt_end,
timeRule=s_period[1:])
dr_range = np.asarray(dr_range)
li_even = np.array(range(len(dr_range)))
dr_range = dr_range[li_even[li_even % 2 == 0]]
else:
dr_range = pd.DateRange(dt_start, dt_end,
timeRule=s_period)
dr_range = np.asarray(dr_range)
# Warning, we MUST copy the date range, if we modify it it will be returned
# in it's modified form the next time we use it.
dr_range = np.copy(dr_range)
dr_range += pd.DateOffset(hours=16)
ts_dates = ts_dates.reindex( dr_range, method='bfill' )
ldt_dates = ts_dates[ts_dates.notnull()].values
#Make unique
sdt_unique = set()
ldt_dates = [x for x in ldt_dates
if x not in sdt_unique and not sdt_unique.add(x)]
return ldt_dates
def download_hist(sleept=10):
"""
download historical data from 2014-06-01
"""
for i in pd.DateRange(dt.datetime(2014, 6, 1),
dt.datetime.today(),
offset=pd.datetools.MonthEnd()):
downloader_new(i)
time.sleep(sleept)
def discrete_boxcar_average(self, seconds=1):
"""Computes a discrete boxcar average for the DataFrame"""
date_range = pandas.DateRange(self.data.index[0],
self.data.index[-1],
offset=pandas.datetools.Second(seconds))
grouped = self.data.groupby(date_range.asof)
subsampled = grouped.mean()
return LightCurve(subsampled, self.header.copy())
def collect_year(self, year=None, station_name=None, exact_station = False,
location_WMO=None, location_WBAN=None, country=None,
state=None, internet_connected = True):
""" Process a request for data for a given year at a given location
optionaly.
Inputs:
- year, int. If no year is passed, choose the current one.
- station_name, str. (Part of) Name of the station to collect data at.
The station names are search for in the ish-history txt file stored in
self.location_db.
- exact_station, bool. If false, all station names are search and the
ones containing the string station_name are selected.
- location WMO code and/or WBAN code, int, int. If no location is selected,
collect the yearly data for all locations.
Output:
- pandas data structure: 2D (DataFrame) if only one location is
requested, 3D (panel) if multiple locations are requested
"""
if year is None:
year = datetime.datetime.today().year
warnings.warn("No year was provided: using the current one (%s)"
% year)
no_location = (location_WMO is None and location_WBAN is None
and station_name is None and country is None and
state is None)
if no_location:
# Requested all data for the year that is at all locations. Returns
# a panel if it can fit in memory, and None if not. In the latter
# case, the data files are still stored locally.
return collect_year(year)
else:
filtered = search_station(self.location_db, self.location_dict,
station_name, exact_station, location_WMO, location_WBAN,
country, state)
if len(filtered) == 1:
result = collect_year_at_loc(year, location_WMO = filtered['USAF'][0],
location_WBAN = filtered['WBAN'][0],
internet_connected = internet_connected)
else:
data = {}
for layer in filtered:
df = collect_year_at_loc(year, layer['USAF'], layer['WBAN'],
internet_connected = internet_connected)
# reindex over the entire year in case there are missing values
if df is None:
continue
df = df.reindex(pandas.DateRange(start = '1/1/%s' % year,
end = '31/12/%s' % year,
offset = pandas.datetools.day))
key = "%s-%s" % (layer['USAF'], layer['WBAN'])
data[key] = df
result = pandas.Panel(data)
return result
def dataframe(self):
bs = self.budgets
ts = self.timeseries
if len(ts) == 0:
return None
for c in self.child.all():
cts = c.timeseries
if cts:
ts = ts.combine(cts, np.sum, 0)
if bs:
bts = bs[0].timeseries
if len(bs) > 1:
for b in range(1, len(bs)):
bts = bts.combine(b.timeseries, np.sum, 0)
else:
bts = ps.TimeSeries([0], index=[ts.index[0]])
startdate = min(ts.index[0], bts.index[0])
enddate = max(ts.index[-1], bts.index[-1])
dates = ps.DateRange(startdate, enddate, offset=ps.DateOffset(days=1))
bts = bts.reindex(dates)
ts = ts.reindex(dates)
df = ps.DataFrame({'actual': ts, 'budget': bts})
df = df.fillna(0)
df['actual'] = df['actual'].apply(Decimal)
df['budget'] = df['budget'].apply(Decimal)
df['vsbudget'] = (df['actual'] - df['budget']).apply(Decimal)
types = []
accounts = []
depth = []
for dt in df.index:
types.append(self.account_type)
accounts.append(self.guid)
depth.append(self.depth)
edf = ps.DataFrame({
'type': types,
'account': accounts,
'depth': depth
},
index=df.index)
df['type'] = edf['type']
df['account'] = edf['account']
df['depth'] = edf['depth']
return df
def _generateCashflow(self):
monthly_offset = 12 / self.frequency
#offset = int(365.2425/self.frequency)
dr = pd.DateRange(
self.startdate,
self.maturitydate,
offset=pdt.DateOffset(months=monthly_offset)) #bday * offset)
cf = Cashflow(cf_times=dr,
cf_amounts=self.couponrate * 100 / self.frequency)
cf.cf[0] = -self.price
cf.cf[cf.cf.count() - 1] += 100.0
return cf
def contacts_per_day(db, end=now()):
end = datetime.datetime.fromtimestamp(end)
end = end.replace(hour=12, minute=0, second=0, microsecond=0)
hourly = pandas.DateRange(end=end, offset=pandas.DateOffset(hours=24), periods=120)
contacts = db.contact_log.find({'created':{'$gt': time.mktime(hourly[0].timetuple())}}, {'created': True})
contact_times = sorted([datetime.datetime.utcfromtimestamp(c['created']) for c in contacts])
data = pandas.Series(1, contact_times)
data = pandas.Series(data.groupby(hourly.asof).sum())
return { 'times': [time.mktime(x.timetuple()) for x in data.index.tolist()]
, 'values': data.values.tolist()
}
def parse_lutkepohl_data(path): # pragma: no cover
"""
Parse data files from Lutkepohl (2005) book
Source for data files: www.jmulti.de
"""
from collections import deque
from datetime import datetime
import pandas
import pandas.core.datetools as dt
import re
regex = re.compile('<(.*) (\w)([\d]+)>.*')
lines = deque(open(path))
to_skip = 0
while '*/' not in lines.popleft():
to_skip += 1
while True:
to_skip += 1
line = lines.popleft()
m = regex.match(line)
if m:
year, freq, start_point = m.groups()
break
data = np.genfromtxt(path, names=True, skip_header=to_skip+1)
n = len(data)
# generate the corresponding date range (using pandas for now)
start_point = int(start_point)
year = int(year)
offsets = {
'Q' : dt.BQuarterEnd(),
'M' : dt.BMonthEnd(),
'A' : dt.BYearEnd()
}
# create an instance
offset = offsets[freq]
inc = offset * (start_point - 1)
start_date = offset.rollforward(datetime(year, 1, 1)) + inc
offset = offsets[freq]
date_range = pandas.DateRange(start_date, offset=offset, periods=n)
return data, date_range
def _execute(self, period):
def actions_per_user_per_day():
map1 = """
function() {
date = new Date((this.created - 12*3600) * 1000);
day = Date.UTC(date.getFullYear(), date.getMonth(), date.getDate(), 12);
emit({name: this.user_name, date: day/1000}, 1);
}"""
reduce = """
function(key, values) {
var total=0;
for (var i=0; i < values.length; i++) {
total += values[i];
}
return total;
}"""
mr1_name = 'mr.actions_per_user_per_day'
mr1 = self.db.mdb[mr1_name]
latest = mr1.find_one(sort=[('_id.date', -1)])['_id']['date']
# The following line performs incremental map reduce, but depends on mongodb version >= 1.8
return self.db.ActionLog._col.map_reduce(
map1,
reduce,
mr1_name,
merge_output=True,
query={'created': {
'$gt': latest - 24 * 3600
}})
mr_col = actions_per_user_per_day()
mr_col.ensure_index('_id.date')
offset = pandas.DateOffset(days=period)
start = newhive.utils.time_u(
mr_col.find_one(sort=[('_id.date', 1)])['_id']['date'])
index = pandas.DateRange(start=start + offset,
end=datetime.datetime.now(),
offset=pandas.DateOffset(days=1))
def users_active_on(date):
cursor = mr_col.find(
{'_id.date': dates_to_spec(date - offset, date)})
return len(cursor.distinct('_id.name'))
data = pandas.DataFrame(
index=index,
data={'Active{}'.format(period): index.map(users_active_on)})
return data
def downloadBTCDateRange(start, end):
for d in pd.DateRange(start, end):
addDateToPayload(d)
req2 = s.post(BTCurl, headers=headers, data=payload)
res = req2.headers.get('content-disposition')
if pd.isnull(res):
print "no data on {}".format(d)
elif 'filename' in res:
filename = res.split('=')[1]
print filename
filepath = '{}/BTC-OpenPositions/{}'.format(outdir, filename)
with open(filepath, "wb") as f:
f.write(req2.content)
# pause 5 secs
pause()
def foo():
path = os.path.join(data_path, 'Table11.1.data.txt')
sep = '\s+'
lines = [re.split(sep, l.strip()) for l in open(path)]
datad = {}
for start in [0]:
name = lines[start][0]
time_rule = lines[start + 1][0]
start_date = lines[start + 2][0]
data = np.concatenate(lines[start + 3:start + 9]).astype(float)
dates = pn.DateRange(start_date, periods=len(data), timeRule=time_rule)
datad[name] = pn.Series(data, index=dates)
return pn.DataFrame(datad)
def parse_table_22():
path = os.path.join(data_path, 'Table2.2.data.txt')
sep = '\s+'
lines = [re.split(sep, l.strip()) for l in open(path)]
data = []
for line in lines:
# drop year
data.extend(line[1:])
data = np.array(data, dtype=float) / 100
dates = pn.DateRange(datetime(1975, 1, 1),
periods=len(data),
timeRule='EOM')
return pn.Series(data, index=dates)
def budgetpanel(self, startdate, enddate):
analysis_dates = ps.DateRange(startdate,
enddate,
offset=ps.DateOffset(days=1))
data = {}
for a in self:
if type(a) != str:
df = a.dataframe
if df is not None:
df = df.reindex(analysis_dates)
data[a] = df
p = ps.Panel(data, major_axis=analysis_dates)
return p
def loadfrets(model, start_date, stop_date):
'''
load axioma factor returns data over a given window
Parameters:
model : str
name of axioma risk model
start_date : datetime
start date of factor returns
stop_date : datetime
stop date of factor returns
returns DataFrame
'''
tmp = pandas.DataFrame()
for dt in pandas.DateRange(start_date, stop_date):
_tmp = loadfret(model, dt)
tmp = tmp.append(_tmp, ignore_index=True)
return tmp
def backfill_b_alphas2(startdate=datetime.datetime(2005, 1, 1),
enddate=datetime.datetime.today() -
pandas.datetools.day,
ncpus=8):
"""
this function is to backfill the bucket signals defined in the production env for 1 day.
"""
print "going to generate production def. bucket alphas"
job_server = pp.Server(ncpus)
jobs = []
for date in pandas.DateRange(startdate,
enddate,
offset=pandas.datetools.day):
jobs.append(
job_server.submit(backfill_b_1d, (date, ), (),
('pandas', 'datetime')))
for job in jobs:
job()
job_server.print_stats()
job_server.destroy()
def create_full_record(p_series_list):
'''tiles records together
creates a daily record over the minimum to maximum dates in the
series list
'''
#--find the min and max dates
min_date, max_date = datetime(year=3012, month=1,
day=1), datetime(year=1512, month=1, day=1)
for p in p_series_list:
if p.index.min() < min_date:
min_date = p.index[0]
if p.index.max() > max_date:
max_date = p.index[-1]
#print min_date,max_date
#--create new pandas date range inclusive of the whole record
d_range = pandas.DateRange(start=min_date,
end=max_date,
offset=pandas.core.datetools.day)
full_series = pandas.TimeSeries(np.ones(len(d_range)) * np.nan, d_range)
#print d_range
for dt, val in full_series.iteritems():
#--try to find an entry in one of the series for this day
v = np.nan
for p in p_series_list:
#print p.head()
try:
if p[dt] != np.nan:
v = p[dt]
except:
pass
if v != np.nan:
full_series[dt] = v
#break
return full_series
def mdf_evalto(self, parameter_s=""):
"""
Advances the current context to the end date and return a pandas
dataframe of nodes evaluated on each timestep.
%mdf_evalto <end_date> [nodes...]
eg: %mdf_evalto 2020-01-01 <my node 1> <my node 2>
"""
args = tokenize(parameter_s)
cur_ctx = _get_current_context()
root_ctx = cur_ctx.get_parent() or cur_ctx
end_date, nodes = args[0], args[1:]
end_date = _parse_datetime(end_date, self.shell.user_global_ns,
self.shell.user_ns)
nodes = map(
lambda x: eval(x, self.shell.user_global_ns, self.shell.user_ns),
nodes)
df_ctx = root_ctx
if len(nodes) > 0 and isinstance(nodes[-1], (dict, list, tuple)):
shift_sets = _get_shift_sets(args[-1], nodes.pop())
assert len(
shift_sets) <= 1, "Only one shift set allowed for %mdf_evalto"
if shift_sets:
unused, shift_set = shift_sets[0]
df_ctx = df_ctx.shift(shift_set=shift_set)
df_builder = DataFrameBuilder(nodes, filter=True)
date_range = pd.DateRange(cur_ctx.get_date(),
end_date,
offset=self.__timestep)
for dt in date_range:
root_ctx.set_date(dt)
df_builder(dt, df_ctx)
return df_builder.get_dataframe(df_ctx)
def downloadBTCDateRange(start, end):
fns = []
for d in pd.DateRange(start, end):
addDateToPayload(d)
req2 = s.post(BTCurl, headers=headers, data=payload)
res = req2.headers.get('content-disposition')
if pd.isnull(res):
print "no data on {}".format(d)
elif 'filename' in res:
filename = res.split('=')[1]
print filename
filepath = '{}/BTC-OpenPositions/{}'.format(outdir, filename)
foldern = outdir + '/BTC-OpenPositions'
if not os.path.exists(foldern):
os.makedirs(foldern)
print filepath
with open(filepath, 'wb') as f:
f.write(req2.content)
# pause 5 secs
fns.append(filepath)
time.sleep(5)
return fns
def main(startdate=None, enddate=None, sleeptime=0):
"""
"""
if startdate is None:
startdate = dt.datetime.today()
if enddate is None:
enddate = dt.datetime.today()
for date in pd.DateRange(startdate, enddate, offset=pd.datetools.day):
print date.strftime('%Y-%m-%d')
fn = download_report(date)
df = parser(fn)
sql_del = """delete from %(table)s where datadate='%(date)s'""" % {
'table': TABLE,
'date': date.strftime('%Y-%m-%d')
}
dbo.cursor.execute(sql_del)
dbo.commit()
print "len(df) = %s" % len(df)
print "start to upload to table %s" % TABLE
sql_io.write_frame(df, TABLE, if_exists='append', bulk='off')
time.sleep(sleeptime)
def run(start_date, end_date=None, forceall=False):
if end_date is None:
end_date = start_date
bd_list = pd.DateRange(start_date, end_date, offset=pd.datetools.bday)
d_str_l = [re.search('\d+', i).group() for i in os.listdir(output_dir)]
for bd in bd_list:
print bd
d_str = bd.strftime('%Y%m%d')
if (not forceall) and (d_str in d_str_l):
continue
c1 = requests.get(url_base % d_str).content
if '\xe4\xbd\x8d\xe7\xbd\xae\xe5\xb7\xb2\xe6\x9b\xb4\xe6\x94\xb9' in c1:
continue
with open(output_dir + '/DTOP_O_%s.zip' % d_str, 'w') as f:
f.write(c1)
time.sleep(60)
def _magic_dataframe(self, parameter_s, widepanel=False, single_df=True):
"""Implementation for magic_dataframe and magic_widepanel"""
# the first two arguments are dates, and after that it's a list of nodes
# with some optional keyword args, ie %mdf_df <start> <end> node, node, node, shifts=[{x:1}, {x:2}]
args = arg_names = tokenize(parameter_s)
args = [
_try_eval(x, self.shell.user_global_ns, self.shell.user_ns)
for x in args
]
args = list(zip(arg_names, args))
start = None
if len(args) > 0:
arg_name, arg = args.pop(0)
start = _parse_datetime(arg_name, self.shell.user_global_ns,
self.shell.user_ns)
end = None
if len(args) > 0:
arg_name, arg = args.pop(0)
end = _parse_datetime(arg_name, self.shell.user_global_ns,
self.shell.user_ns)
# the final argument can be the number of processes to use
num_processes = 0
if len(args) > 0:
arg_name, arg = args[-1]
if isinstance(arg, basestring) and arg.startswith("||"):
arg_name, arg = args.pop()
num_processes = int(arg[2:])
# the next to last parameter may be a shift set or list of
# shift sets.
has_shifts = False
shift_sets = [{}] # always have at least one empty shift set
shift_names = ["_0"]
arg_name, arg = args[-1] if len(args) > 0 else (None, None)
if not isinstance(arg, MDFNode):
arg_name, arg = args.pop()
named_shift_sets = _get_shift_sets(arg_name, arg)
if named_shift_sets:
shift_names, shift_sets = zip(*named_shift_sets)
has_shifts = True
# any remaining arguments are the nodes
nodes = []
node_var_names = []
for arg_name, node in args:
assert isinstance(node, MDFNode), "%s is not a node" % arg_name
nodes.append(node)
node_var_names.append(arg_name)
curr_ctx = _get_current_context()
ctxs = [None] * len(nodes)
if not nodes:
# get the selected nodes from the viewer
if _viewer_imported:
selected = viewer.get_selected()
ctxs, nodes = zip(*selected)
for i, (ctx, node) in enumerate(selected):
assert ctx.is_shift_of(curr_ctx), \
"selected node '%s' is not in the current context" % node.name
# replace any contexts that are simply the current context with None
# so that shifting works correctly
if ctx is curr_ctx:
ctxs[i] = None
# if there are shifts then all the contexts have to be None otherwise the
# shifts won't work correctly. This could be relaxed later if it causes problems,
# but for now this makes the code simpler.
if has_shifts:
assert np.array([x is None for x in ctxs]).all(), \
"Can't apply shifts when contexts are explicitly specified"
# list df_builders, one per node or group of nodes
callbacks = []
df_builders = []
if widepanel or not single_df:
# build multiple dataframes
for node, ctx in zip(nodes, ctxs):
if ctx is None:
df_builder = DataFrameBuilder([node], filter=True)
else:
df_builder = DataFrameBuilder([node],
contexts=[ctx],
filter=True)
df_builders.append(df_builder)
else:
# build a single dataframe
if np.array([x is None for x in ctxs]).all():
df_builder = DataFrameBuilder(nodes, filter=True)
else:
df_builder = DataFrameBuilder(nodes,
contexts=ctxs,
filter=True)
df_builders.append(df_builder)
# add all the dataframe builders to the callbacks
callbacks.extend(df_builders)
root_ctx = curr_ctx.get_parent() or curr_ctx
date_range = pd.DateRange(start, end, offset=self.__timestep)
# Add a progress bar to the callbacks
callbacks.append(ProgressBar(date_range[0], date_range[-1]))
shifted_ctxs = run(date_range,
callbacks,
ctx=root_ctx,
shifts=shift_sets,
num_processes=num_processes)
if not has_shifts:
shifted_ctxs = [root_ctx]
# when returning a list of results because multiple shifts have been specified
# use a named tuple with the items being the names of the shifts
tuple_ctr = tuple
if has_shifts:
# Currying hell yeah
tuple_ctr = partial(ShiftedResultsTuple, shift_names)
if widepanel:
wps = []
for shift_name, shift_set, shifted_ctx in zip(
shift_names, shift_sets, shifted_ctxs):
wp_dict = {}
for node_var_name, df_builder in zip(node_var_names,
df_builders):
wp_dict[node_var_name] = df_builder.get_dataframe(
shifted_ctx)
wp = pd.WidePanel.from_dict(wp_dict)
if has_shifts:
wp = WidePanelWithShiftSet(wp, shift_name, shift_set)
wps.append(wp)
if len(wps) == 1:
return wps[0]
return tuple_ctr(*wps)
# list a list of lists of dataframes
# [[dfs for one shift set], [dfs for next shift set], ...]
df_lists = []
for shift_name, shift_set, shifted_ctx in zip(shift_names, shift_sets,
shifted_ctxs):
dfs = []
for df_builder in df_builders:
df = df_builder.get_dataframe(shifted_ctx)
if has_shifts:
df = DataFrameWithShiftSet(df, shift_name, shift_set)
dfs.append(df)
df_lists.append(dfs)
if single_df:
# flatten into a single list (there should be one dataframe per shift)
dfs = reduce(operator.add, df_lists, [])
if len(dfs) == 1:
return dfs[0]
return tuple_ctr(*dfs)
if len(df_lists) == 1:
return df_lists[0]
return tuple_ctr(*df_lists)
# 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
mod = AR(sunspots, freq='A')
res = mod.fit(method='mle', maxlag=9)
