def test__can_insert_row():
""" I can insert a new row into a bitemp ts and it comes back when selecting the latest data
"""
df = get_bitemporal_test_data()
df = insert_at(df, dt('2014-01-03'), [[9, 90]])
assert len(df) == 9
df = groupby_asof(df)
assert len(df) == 4
assert df.loc[dt('2014-01-03')]['OPEN'] == 9
assert df.loc[dt('2014-01-03')]['CLOSE'] == 90
def test__can_insert_row():
""" I can insert a new row into a bitemp ts and it comes back when selecting the latest data
"""
df = get_bitemporal_test_data()
df = insert_at(df, dt('2014-01-03'), [[9, 90]])
assert len(df) == 9
df = groupby_asof(df)
assert len(df) == 4
assert df.loc[dt('2014-01-03')]['OPEN'] == 9
assert df.loc[dt('2014-01-03')]['CLOSE'] == 90
def test__get_ts__asof_datetime():
""" I can get a timeseries as-of a particular point in time
"""
df = groupby_asof(get_bitemporal_test_data(), as_of=dt('2015-01-05'))
assert len(df) == 3
assert all(df['OPEN'] == [1.1, 2.1, 3.0])
assert all(df['CLOSE'] == [10.1, 20.1, 30.0])
def test__get_ts__asof_datetime():
""" I can get a timeseries as-of a particular point in time
"""
df = groupby_asof(get_bitemporal_test_data(), as_of=dt('2015-01-05'))
assert len(df) == 3
assert all(df['OPEN'] == [1.1, 2.1, 3.0])
assert all(df['CLOSE'] == [10.1, 20.1, 30.0])
def insert_at(df, sample_date, values):
""" Insert some values into a bi-temporal dataframe.
This is like what would happen when we get a price correction.
"""
observed_dt = dt(datetime.now())
return multi_index_insert_row(df, [sample_date, observed_dt], values)
def get_datetime_index_test_data():
sample_dates = pd.DatetimeIndex(4 * [dt('1/1/2014 21:30')] +
4 * [dt('2/1/2014 21:30')] +
4 * [dt('3/1/2014 21:30')])
observed_dates = [
dt('1/1/2014 22:00'),
dt('1/1/2014 22:30'),
dt('2/1/2014 00:00'),
dt('1/1/2015 21:30'),
dt('2/1/2014 23:00'),
dt('2/1/2014 23:30'),
dt('3/1/2014 00:00'),
dt('2/1/2015 21:30'),
dt('3/1/2014 21:30'),
dt('3/1/2014 22:30'),
dt('4/1/2014 00:00'),
dt('3/1/2015 21:30'),
]
index = pd.MultiIndex.from_arrays([sample_dates, observed_dates],
names=['sample_dt', 'observed_dt'])
prices = np.arange(24).reshape(12, 2) * 10
df = pd.DataFrame(prices, index=index, columns=['OPEN', 'CLOSE'])
# OPEN CLOSE
# sample_dt observed_dt
# 2014-01-01 21:30:00 2014-01-01 22:00:00 0 10
# 2014-01-01 22:30:00 20 30
# 2014-02-01 00:00:00 40 50
# 2015-01-01 21:30:00 60 70
# 2014-02-01 21:30:00 2014-02-01 23:00:00 80 90
# 2014-02-01 23:30:00 100 110
# 2014-03-01 00:00:00 120 130
# 2015-02-01 21:30:00 140 150
# 2014-03-01 21:30:00 2014-03-01 21:30:00 160 170
# 2014-03-01 22:30:00 180 190
# 2014-04-01 00:00:00 200 210
# 2015-03-01 21:30:00 220 230
return df
def get_datetime_index_test_data():
sample_dates = pd.DatetimeIndex(4 * [dt('1/1/2014 21:30')] +
4 * [dt('2/1/2014 21:30')] +
4 * [dt('3/1/2014 21:30')])
observed_dates = [dt('1/1/2014 22:00'), dt('1/1/2014 22:30'), dt('2/1/2014 00:00'), dt('1/1/2015 21:30'),
dt('2/1/2014 23:00'), dt('2/1/2014 23:30'), dt('3/1/2014 00:00'), dt('2/1/2015 21:30'),
dt('3/1/2014 21:30'), dt('3/1/2014 22:30'), dt('4/1/2014 00:00'), dt('3/1/2015 21:30'),
]
index = pd.MultiIndex.from_arrays([sample_dates, observed_dates], names=['sample_dt', 'observed_dt'])
prices = np.arange(24).reshape(12, 2) * 10
df = pd.DataFrame(prices, index=index, columns=['OPEN', 'CLOSE'])
# OPEN CLOSE
# sample_dt observed_dt
# 2014-01-01 21:30:00 2014-01-01 22:00:00 0 10
# 2014-01-01 22:30:00 20 30
# 2014-02-01 00:00:00 40 50
# 2015-01-01 21:30:00 60 70
# 2014-02-01 21:30:00 2014-02-01 23:00:00 80 90
# 2014-02-01 23:30:00 100 110
# 2014-03-01 00:00:00 120 130
# 2015-02-01 21:30:00 140 150
# 2014-03-01 21:30:00 2014-03-01 21:30:00 160 170
# 2014-03-01 22:30:00 180 190
# 2014-04-01 00:00:00 200 210
# 2015-03-01 21:30:00 220 230
return df