# We just need a list of masked_arrays. It's easier to create it like that:
_mask = _variables.T == missingchar
_datalist = [masked_array(a, mask=m, dtype=t) for (a, m, t) in zip(_variables.T, _mask, vartypes)]
#
newdates = __getdates(dates=dates, newdates=newdates, length=nvars, freq=None, start_date=None)
return MultiTimeSeries(_datalist, dates=newdates, dtype=mdescr)
################################################################################
if __name__ == "__main__":
import numpy as N
from maskedarray.testutils import assert_equal
if 1:
d = N.arange(5)
m = MA.make_mask([1, 0, 0, 1, 1])
base_d = N.r_[d, d[::-1]].reshape(2, -1).T
base_m = N.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)
ts = time_series(mrec, dates)
mts = MultiTimeSeries(mrec, dates)
self_data = [d, m, mrec, dlist, dates, ts, mts]
assert isinstance(mts.f0, TimeSeries)
if 0:
mts[:2] = 5
assert_equal(mts.f0._data, [5, 5, 2, 3, 4])
