import scikits.timeseries.lib.tstables as tstab
import scikits.timeseries.lib.reportlib as rl
## a test series
data = np.arange(1, 366)
startdate = ts.Date(freq='D', year=2009, month=1, day=1)
test_series = ts.time_series(data, start_date=startdate, freq='D')
output_dir = '../timeseries'
try:
os.mkdir(output_dir)
except OSError:
pass
## write to csv file
csv_file = os.path.abspath(os.path.join(output_dir, 'test_series.csv'))
mycsv = open(csv_file, 'w')
csvReport = rl.Report(test_series, delim=';', fixed_width=True)
csvReport(output=mycsv)
mycsv.close()
# Write to a PyTables file
hdf5_name = 'scikits_test1.hdf5'
filepath_hdf5 = os.path.join(output_dir, hdf5_name)
h5file = tstab.open_file(filepath_hdf5,
mode="w",
title='Example table with csikits time series')
group_doc = h5file.create_group("/", 'examples', 'Test Data')
table = h5file.createTimeSeriesTable(group_doc, 'Example_1', test_series)
h5file.close()
# 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
hdf5_name = 'scikits_test2.hdf5'
filepath_hdf5 = os.path.join(output_dir, hdf5_name)
h5file = tstab.open_file(filepath_hdf5, mode="w",
title='Example table with csikits time series')
group_doc = h5file.create_group("/", 'examples', 'Test Data')
table = h5file.createTimeSeriesTable(group_doc, 'Example_2', test_series)
h5file.close()
