def setUp(self):
dindex = pd.date_range("2000-01-01T00:00:00", periods=24, freq="H")
self.ats = np.arange(0, 360, 15)
self.ats = np.sin(2 * np.pi * self.ats / 360)
self.ats = pd.DataFrame(self.ats, index=dindex)
self.ats = tsutils.memory_optimize(self.ats)
self.compare = self.ats.copy()
self.compare = self.compare.join(
pd.Series(
np.zeros(len(self.ats)).astype("f"),
index=self.ats.index,
name="0::peak",
))
self.compare = self.compare.join(
pd.Series(
np.zeros(len(self.ats)).astype("f"),
index=self.ats.index,
name="0::valley",
))
self.compare.index.name = "Datetime"
self.compare["0::peak"] = np.nan
self.compare.loc[self.compare[0] == 1, "0::peak"] = 1
self.compare["0::valley"] = np.nan
self.compare.loc[self.compare[0] == -1, "0::valley"] = -1
self.compare = tsutils.memory_optimize(self.compare)
def setUp(self):
self.data_0_to_1 = tstoolbox.read(
'tests/data_sunspot_normalized_0_to_1.csv')
self.data_10_to_20 = tstoolbox.read(
'tests/data_sunspot_normalized_10_to_20.csv')
self.data_zscore = tstoolbox.read(
'tests/data_sunspot_normalized_zscore.csv')
self.data_zscore = tsutils.memory_optimize(self.data_zscore)
self.data_pct_rank = tstoolbox.read(
'tests/data_sunspot_normalized_pct_rank.csv')
self.data_pct_rank = tsutils.memory_optimize(self.data_pct_rank)
def setUp(self):
dr = pandas.date_range("2000-01-01", periods=2, freq="D")
ts = pandas.Series([4.5, 4.6], index=dr)
self.round_index_direct = pandas.DataFrame(ts, columns=["Value"])
self.round_index_direct.index.name = "Datetime"
self.round_index_direct = tsutils.memory_optimize(
self.round_index_direct)
self.round_index_multiple_direct = pandas.DataFrame(ts,
columns=["Value"])
self.round_index_multiple_direct = pandas.concat(
[
self.round_index_multiple_direct,
pandas.Series(ts, name="Value")
],
axis="columns",
)
self.round_index_multiple_direct.index.name = "Datetime"
self.round_index_multiple_direct = tsutils.memory_optimize(
self.round_index_multiple_direct)
self.round_index_cli = b"""Datetime,Value
2000-01-01,4.5
2000-01-02,4.6
"""
self.round_index_multiple_cli = b"""Datetime,Value,Value
2000-01-01,4.5,4.5
2000-01-02,4.6,4.6
"""
self.round_index_tsstep_2_daily_cli = b"""Datetime,Value,Value1
2000-01-01,4.5,45.6
2000-01-03,4.7,34.2
2000-01-05,4.5,7.2
"""
self.round_index_tsstep_2_daily = pandas.DataFrame(
[[4.5, 45.6], [4.7, 34.2], [4.5, 7.2]],
columns=["Value", "Value1"],
index=pandas.DatetimeIndex(
["2000-01-01", "2000-01-03", "2000-01-05"]),
)
self.round_index_tsstep_2_daily = tsutils.memory_optimize(
self.round_index_tsstep_2_daily)
self.round_index_tsstep_2_daily.index.name = "Datetime"
self.round_index_blanks = b"""Datetime,Value_mean,Unnamed: 2_mean,Unnamed: 3_mean,Unnamed: 4_mean,Unnamed: 5_mean,Unnamed: 6_mean,Unnamed: 7_mean,Unnamed: 8_mean,Unnamed: 9_mean
def setUp(self):
self.data_0_to_1 = tstoolbox.read(
"tests/data_sunspot_normalized_0_to_1.csv")
self.data_0_to_1.columns = ["Area::minmax"]
self.data_10_to_20 = tstoolbox.read(
"tests/data_sunspot_normalized_10_to_20.csv")
self.data_10_to_20.columns = ["Area::minmax"]
self.data_zscore = tstoolbox.read(
"tests/data_sunspot_normalized_zscore.csv")
self.data_zscore.columns = ["Area::zscore"]
self.data_zscore = tsutils.memory_optimize(self.data_zscore)
self.data_pct_rank = tstoolbox.read(
"tests/data_sunspot_normalized_pct_rank.csv")
self.data_pct_rank.columns = ["Area::pct_rank"]
self.data_pct_rank = tsutils.memory_optimize(self.data_pct_rank)
def setUp(self):
dr = pd.date_range('2011-01-01T12:00:00', periods=3, freq='H')
self.date_slice = pd.DataFrame([2, 2, 2], index=dr, columns=['Value'])
self.date_slice = tsutils.memory_optimize(self.date_slice)
self.date_slice_cli = capture.capture(tsutils._printiso,
self.date_slice)
def setUp(self):
try:
self.read_direct = pd.read_csv('tests/data_sunspot_EST.csv',
index_col=0,
parse_dates=[0]).tz_localize('UTC').tz_convert('EST')
except TypeError:
self.read_direct = pd.read_csv('tests/data_sunspot_EST.csv',
index_col=0,
parse_dates=[0]).tz_convert('EST')
self.read_direct = tsutils.memory_optimize(self.read_direct)
def setUp(self):
dr = pandas.date_range('2000-01-01', periods=6, freq='D')
ts1 = pandas.Series([4.5, 4.6, 4.7, 4.6, 4.5, 4.4], index=dr)
ts2 = pandas.Series([45.6, 90.5, 34.2, 23.1, 7.2, 4.3], index=dr)
self.pick_multiple_direct = pandas.DataFrame(ts2, columns=['Value1'])
self.pick_multiple_direct = self.pick_multiple_direct.join(pandas.DataFrame(ts1, columns=['Value']))
self.pick_multiple_direct = tsutils.memory_optimize(self.pick_multiple_direct)
self.pick_cli = capture.capture(tsutils._printiso, self.pick_multiple_direct)
def setUp(self):
self.add_trend_cli = b"""Datetime,Value::trend
2011-01-01 00:00:00,1
2011-01-01 01:00:00,1.04255
2011-01-01 02:00:00,1.08511
2011-01-01 03:00:00,1.12766
2011-01-01 04:00:00,1.17021
2011-01-01 05:00:00,1.21277
2011-01-01 06:00:00,1.25532
2011-01-01 07:00:00,1.29787
2011-01-01 08:00:00,1.34043
2011-01-01 09:00:00,1.38298
2011-01-01 10:00:00,1.42553
2011-01-01 11:00:00,1.46809
2011-01-01 12:00:00,1.51064
2011-01-01 13:00:00,1.55319
2011-01-01 14:00:00,1.59574
2011-01-01 15:00:00,1.6383
2011-01-01 16:00:00,1.68085
2011-01-01 17:00:00,1.7234
2011-01-01 18:00:00,1.76596
2011-01-01 19:00:00,1.80851
2011-01-01 20:00:00,1.85106
2011-01-01 21:00:00,1.89362
2011-01-01 22:00:00,1.93617
2011-01-01 23:00:00,1.97872
2011-01-02 00:00:00,2.02128
2011-01-02 01:00:00,2.06383
2011-01-02 02:00:00,2.10638
2011-01-02 03:00:00,2.14894
2011-01-02 04:00:00,2.19149
2011-01-02 05:00:00,2.23404
2011-01-02 06:00:00,2.2766
2011-01-02 07:00:00,2.31915
2011-01-02 08:00:00,2.3617
2011-01-02 09:00:00,2.40426
2011-01-02 10:00:00,2.44681
2011-01-02 11:00:00,2.48936
2011-01-02 12:00:00,2.53191
2011-01-02 13:00:00,2.57447
2011-01-02 14:00:00,2.61702
2011-01-02 15:00:00,2.65957
2011-01-02 16:00:00,2.70213
2011-01-02 17:00:00,2.74468
2011-01-02 18:00:00,2.78723
2011-01-02 19:00:00,2.82979
2011-01-02 20:00:00,2.87234
2011-01-02 21:00:00,2.91489
2011-01-02 22:00:00,2.95745
2011-01-02 23:00:00,3
"""
self.add_trend_direct = tstoolbox.date_slice(
input_ts=self.add_trend_cli)
self.add_trend_direct.index.name = 'Datetime'
self.add_trend_direct = tsutils.memory_optimize(self.add_trend_direct)
def setUp(self):
self.add_trend_cli = b"""Datetime,Value_trend
2011-01-01 00:00:00,1
2011-01-01 01:00:00,1.04255
2011-01-01 02:00:00,1.08511
2011-01-01 03:00:00,1.12766
2011-01-01 04:00:00,1.17021
2011-01-01 05:00:00,1.21277
2011-01-01 06:00:00,1.25532
2011-01-01 07:00:00,1.29787
2011-01-01 08:00:00,1.34043
2011-01-01 09:00:00,1.38298
2011-01-01 10:00:00,1.42553
2011-01-01 11:00:00,1.46809
2011-01-01 12:00:00,1.51064
2011-01-01 13:00:00,1.55319
2011-01-01 14:00:00,1.59574
2011-01-01 15:00:00,1.6383
2011-01-01 16:00:00,1.68085
2011-01-01 17:00:00,1.7234
2011-01-01 18:00:00,1.76596
2011-01-01 19:00:00,1.80851
2011-01-01 20:00:00,1.85106
2011-01-01 21:00:00,1.89362
2011-01-01 22:00:00,1.93617
2011-01-01 23:00:00,1.97872
2011-01-02 00:00:00,2.02128
2011-01-02 01:00:00,2.06383
2011-01-02 02:00:00,2.10638
2011-01-02 03:00:00,2.14894
2011-01-02 04:00:00,2.19149
2011-01-02 05:00:00,2.23404
2011-01-02 06:00:00,2.2766
2011-01-02 07:00:00,2.31915
2011-01-02 08:00:00,2.3617
2011-01-02 09:00:00,2.40426
2011-01-02 10:00:00,2.44681
2011-01-02 11:00:00,2.48936
2011-01-02 12:00:00,2.53191
2011-01-02 13:00:00,2.57447
2011-01-02 14:00:00,2.61702
2011-01-02 15:00:00,2.65957
2011-01-02 16:00:00,2.70213
2011-01-02 17:00:00,2.74468
2011-01-02 18:00:00,2.78723
2011-01-02 19:00:00,2.82979
2011-01-02 20:00:00,2.87234
2011-01-02 21:00:00,2.91489
2011-01-02 22:00:00,2.95745
2011-01-02 23:00:00,3
"""
self.add_trend_direct = tstoolbox.date_slice(
input_ts=self.add_trend_cli)
self.add_trend_direct.index.name = 'Datetime'
self.add_trend_direct = tsutils.memory_optimize(self.add_trend_direct)
def setUp(self):
"""Prepare in-memory files data_stacked.csv and data_unstacked.csv."""
self.stacked = pd.read_csv(
"tests/data_stacked.csv", index_col=0, parse_dates=True
)
self.stacked.index.name = "Datetime"
self.stacked = tsutils.memory_optimize(self.stacked)
self.stacked_1 = pd.read_csv(
"tests/data_stacked_1.csv", index_col=0, parse_dates=True
)
self.stacked_1.index.name = "Datetime"
self.stacked_1 = tsutils.memory_optimize(self.stacked_1)
self.unstacked = pd.read_csv(
"tests/data_unstacked.csv", index_col=0, parse_dates=True
)
self.unstacked.rename(columns=lambda x: x.strip("'\" "))
self.unstacked.index.name = "Datetime"
self.unstacked = tsutils.memory_optimize(self.unstacked)
def setUp(self):
"""Prepare in-memory files data_stacked.csv and data_unstacked.csv."""
self.stacked = pd.read_csv('tests/data_stacked.csv',
index_col=0,
parse_dates=True)
self.stacked.index.name = 'Datetime'
self.stacked = tsutils.memory_optimize(self.stacked)
self.stacked_1 = pd.read_csv('tests/data_stacked_1.csv',
index_col=0,
parse_dates=True)
self.stacked_1.index.name = 'Datetime'
self.stacked_1 = tsutils.memory_optimize(self.stacked_1)
self.unstacked = pd.read_csv('tests/data_unstacked.csv',
index_col=0,
parse_dates=True)
self.unstacked.rename(columns=lambda x: x.strip('\'\" '))
self.unstacked.index.name = 'Datetime'
self.unstacked = tsutils.memory_optimize(self.unstacked)
def setUp(self):
dr = pandas.date_range('2000-01-01', periods=2, freq='D')
ts = pandas.Series([4.5, 4.6], index=dr)
self.round_index_direct = pandas.DataFrame(ts, columns=['Value'])
self.round_index_direct.index.name = 'Datetime'
self.round_index_direct = tsutils.memory_optimize(self.round_index_direct)
self.round_index_multiple_direct = pandas.DataFrame(ts,
columns=['Value'])
self.round_index_multiple_direct = pandas.concat([self.round_index_multiple_direct,
pandas.Series(ts, name='Value')], axis='columns')
self.round_index_multiple_direct.index.name = 'Datetime'
self.round_index_multiple_direct = tsutils.memory_optimize(self.round_index_multiple_direct)
self.round_index_cli = b"""Datetime,Value
2000-01-01,4.5
2000-01-02,4.6
"""
self.round_index_multiple_cli = b"""Datetime,Value,Value
2000-01-01,4.5,4.5
2000-01-02,4.6,4.6
"""
self.round_index_tsstep_2_daily_cli = b"""Datetime,Value,Value1
2000-01-01,4.5,45.6
2000-01-03,4.7,34.2
2000-01-05,4.5,7.2
"""
self.round_index_tsstep_2_daily = pandas.DataFrame(
[[4.5, 45.6], [4.7, 34.2], [4.5, 7.2]],
columns=['Value', 'Value1'],
index=pandas.DatetimeIndex(
['2000-01-01', '2000-01-03', '2000-01-05']))
self.round_index_tsstep_2_daily = tsutils.memory_optimize(self.round_index_tsstep_2_daily)
self.round_index_tsstep_2_daily.index.name = 'Datetime'
self.round_index_blanks = b"""Datetime,Value_mean,Unnamed: 2_mean,Unnamed: 3_mean,Unnamed: 4_mean,Unnamed: 5_mean,Unnamed: 6_mean,Unnamed: 7_mean,Unnamed: 8_mean,Unnamed: 9_mean
def setUp(self):
dr = pandas.date_range('2000-01-01', periods=2, freq='D')
ts = pandas.Series([4.5, 4.6], index=dr)
self.read_direct = pandas.DataFrame(ts, columns=['Value'])
self.read_direct.index.name = 'Datetime'
self.read_direct = tsutils.memory_optimize(self.read_direct)
self.read_multiple_direct = pandas.DataFrame(ts,
columns=['Value'])
self.read_multiple_direct = pandas.concat([self.read_multiple_direct,
pandas.Series(ts, name='Value')], axis='columns')
self.read_multiple_direct.index.name = 'Datetime'
self.read_multiple_direct = tsutils.memory_optimize(self.read_multiple_direct)
self.read_cli = b"""Datetime,Value
2000-01-01,4.5
2000-01-02,4.6
"""
self.read_multiple_cli = b"""Datetime,Value,Value
2000-01-01,4.5,4.5
2000-01-02,4.6,4.6
"""
self.read_tsstep_2_daily_cli = b"""Datetime,Value,Value1
2000-01-01,4.5,45.6
2000-01-03,4.7,34.2
2000-01-05,4.5,7.2
"""
self.read_tsstep_2_daily = pandas.DataFrame(
[[4.5, 45.6], [4.7, 34.2], [4.5, 7.2]],
columns=['Value', 'Value1'],
index=pandas.DatetimeIndex(
['2000-01-01', '2000-01-03', '2000-01-05']))
self.read_tsstep_2_daily = tsutils.memory_optimize(self.read_tsstep_2_daily)
self.read_tsstep_2_daily.index.name = 'Datetime'
self.read_blanks = b"""Datetime,Value_mean,Unnamed: 2_mean,Unnamed: 3_mean,Unnamed: 4_mean,Unnamed: 5_mean,Unnamed: 6_mean,Unnamed: 7_mean,Unnamed: 8_mean,Unnamed: 9_mean
def setUp(self):
dr = pandas.date_range('2011-01-01', periods=2, freq='D')
ts = pandas.Series([2, 2], index=dr)
self.aggregate_direct_mean = pandas.DataFrame(ts,
columns=['Value::mean'])
self.aggregate_direct_mean.index.name = 'Datetime'
self.aggregate_direct_mean = tsutils.memory_optimize(self.aggregate_direct_mean)
ts = pandas.Series([48, 48], index=dr)
self.aggregate_direct_sum = pandas.DataFrame(ts, columns=['Value::sum'])
self.aggregate_direct_sum.index.name = 'Datetime'
self.aggregate_direct_sum = tsutils.memory_optimize(self.aggregate_direct_sum)
self.aggregate_cli_mean = b"""Datetime,Value::mean
2011-01-01,2
2011-01-02,2
"""
self.aggregate_cli_sum = b"""Datetime,Value::sum
def setUp(self):
try:
self.read_direct = (
pd.read_csv("tests/data_sunspot_EST.csv", index_col=0, parse_dates=[0])
.tz_localize("UTC")
.tz_convert("EST")
)
except TypeError:
self.read_direct = pd.read_csv(
"tests/data_sunspot_EST.csv", index_col=0, parse_dates=[0]
).tz_convert("EST")
self.read_direct = tsutils.memory_optimize(self.read_direct)
def setUp(self):
dr = pandas.date_range("2000-01-01", periods=6, freq="D")
ts1 = pandas.Series([4.5, 4.6, 4.7, 4.6, 4.5, 4.4], index=dr)
ts2 = pandas.Series([45.6, 90.5, 34.2, 23.1, 7.2, 4.3], index=dr)
self.pick_multiple_direct = pandas.DataFrame(ts2, columns=["Value1"])
self.pick_multiple_direct = self.pick_multiple_direct.join(
pandas.DataFrame(ts1, columns=["Value"])
)
self.pick_multiple_direct = tsutils.memory_optimize(self.pick_multiple_direct)
self.pick_cli = capture.capture(tsutils._printiso, self.pick_multiple_direct)
def setUp(self):
''' Prepare in-memory versions of the files data_stacked.csv and
data_unstacked.csv.
'''
self.stacked = pd.read_csv('tests/data_stacked.csv',
index_col=0,
parse_dates=True)
self.stacked.index.name = 'Datetime'
self.stacked = tsutils.memory_optimize(self.stacked)
self.stacked_1 = pd.read_csv('tests/data_stacked_1.csv',
index_col=0,
parse_dates=True)
self.stacked_1.index.name = 'Datetime'
self.stacked_1 = tsutils.memory_optimize(self.stacked_1)
self.unstacked = pd.read_csv('tests/data_unstacked.csv',
index_col=0,
parse_dates=True)
self.unstacked.rename(columns=lambda x: x.strip('\'\" '))
self.unstacked.index.name = 'Datetime'
self.unstacked = tsutils.memory_optimize(self.unstacked)
def setUp(self):
dr = pandas.date_range("2000-01-01", periods=2, freq="D")
ts = pandas.Series([4.5, 4.6], index=dr)
self.compare_direct_01 = pandas.DataFrame(ts,
columns=["Value::convert"])
self.compare_direct_01.index.name = "Datetime"
self.compare_direct_01 = tsutils.memory_optimize(
self.compare_direct_01)
dr = pandas.date_range("2000-01-01", periods=2, freq="D")
ts = pandas.Series([11.0, 11.2], index=dr)
self.compare_direct_02 = pandas.DataFrame(ts,
columns=["Value::convert"])
self.compare_direct_02.index.name = "Datetime"
self.compare_direct_02 = tsutils.memory_optimize(
self.compare_direct_02)
self.compare_cli_01 = b"""Datetime,Value::convert
2000-01-01,4.5
2000-01-02,4.6
"""
self.compare_cli_02 = b"""Datetime,Value::convert
def setUp(self):
dindex = pd.date_range('2000-01-01T00:00:00', periods=24, freq='H')
self.ats = pd.np.arange(0, 360, 15)
self.ats = pd.np.sin(2 * pd.np.pi * self.ats / 360)
self.ats = pd.DataFrame(self.ats, index=dindex)
self.ats = tsutils.memory_optimize(self.ats)
self.compare = self.ats.copy()
self.compare = self.compare.join(
pd.Series(pd.np.zeros(len(self.ats)).astype('f'),
index=self.ats.index,
name='0_peak'))
self.compare = self.compare.join(
pd.Series(pd.np.zeros(len(self.ats)).astype('f'),
index=self.ats.index,
name='0_valley'))
self.compare.index.name = 'Datetime'
self.compare['0_peak'] = pd.np.nan
self.compare.loc[self.compare[0] == 1, '0_peak'] = 1
self.compare['0_valley'] = pd.np.nan
self.compare.loc[self.compare[0] == -1, '0_valley'] = -1
self.compare = tsutils.memory_optimize(self.compare)
def setUp(self):
dindex = pd.date_range('2000-01-01T00:00:00', periods=24, freq='H')
self.ats = pd.np.arange(0, 360, 15)
self.ats = pd.np.sin(2*pd.np.pi*self.ats/360)
self.ats = pd.DataFrame(self.ats, index=dindex)
self.ats = tsutils.memory_optimize(self.ats)
self.compare = self.ats.copy()
self.compare = self.compare.join(
pd.Series(pd.np.zeros(len(self.ats)).astype('f'),
index=self.ats.index,
name='0::peak'))
self.compare = self.compare.join(
pd.Series(pd.np.zeros(len(self.ats)).astype('f'),
index=self.ats.index,
name='0::valley'))
self.compare.index.name = 'Datetime'
self.compare['0::peak'] = pd.np.nan
self.compare.loc[self.compare[0] == 1, '0::peak'] = 1
self.compare['0::valley'] = pd.np.nan
self.compare.loc[self.compare[0] == -1, '0::valley'] = -1
self.compare = tsutils.memory_optimize(self.compare)
def setUp(self):
dr = pandas.date_range('2000-01-01', periods=2, freq='D')
ts = pandas.Series([4.5, 4.6], index=dr)
self.read_direct = pandas.DataFrame(ts, columns=['Value'])
self.read_direct.index.name = 'Datetime'
self.read_direct = tsutils.memory_optimize(self.read_direct)
self.read_cli = b"""Datetime,Value
2000-01-01,4.5
2000-01-02,4.6
"""
dr = pandas.date_range('2000-01-01', periods=5, freq='D')
ts = pandas.Series([4.5, 4.6, 4.7, 4.8, 4.9], index=dr)
self.read_direct_sparse = pandas.DataFrame(ts, columns=['Value'])
self.read_direct_sparse.index.name = 'Datetime'
self.read_direct_sparse = tsutils.memory_optimize(self.read_direct_sparse)
self.read_cli_sparse = b"""Datetime,Value
def setUp(self):
dr = pandas.date_range("2000-01-01", periods=2, freq="D")
ts = pandas.Series([4.5, 4.6], index=dr)
self.read_direct = pandas.DataFrame(ts, columns=["Value"])
self.read_direct.index.name = "Datetime"
self.read_direct = tsutils.memory_optimize(self.read_direct)
self.read_cli = b"""Datetime,Value
2000-01-01,4.5
2000-01-02,4.6
"""
dr = pandas.date_range("2000-01-01", periods=5, freq="D")
ts = pandas.Series([4.5, 4.6, 4.7, 4.8, 4.9], index=dr)
self.read_direct_sparse = pandas.DataFrame(ts, columns=["Value"])
self.read_direct_sparse.index.name = "Datetime"
self.read_direct_sparse = tsutils.memory_optimize(self.read_direct_sparse)
self.read_cli_sparse = b"""Datetime,Value
def setUp(self):
dr = pandas.date_range("2011-01-01", periods=2, freq="D")
ts = pandas.Series([2, 2], index=dr)
self.aggregate_direct_mean = pandas.DataFrame(ts,
columns=["Value::mean"])
self.aggregate_direct_mean.index.name = "Datetime"
self.aggregate_direct_mean = tsutils.memory_optimize(
self.aggregate_direct_mean)
ts = pandas.Series([48, 48], index=dr)
self.aggregate_direct_sum = pandas.DataFrame(ts,
columns=["Value::sum"])
self.aggregate_direct_sum.index.name = "Datetime"
self.aggregate_direct_sum = tsutils.memory_optimize(
self.aggregate_direct_sum)
self.aggregate_cli_mean = b"""Datetime,Value::mean
2011-01-01,2
2011-01-02,2
"""
self.aggregate_cli_sum = b"""Datetime,Value::sum
def setUp(self):
''' Setup
'''
dr = pandas.date_range('2011-01-01', periods=2, freq='D')
ts = pandas.Series([2, 2], index=dr)
self.aggregate_direct_mean = pandas.DataFrame(ts,
columns=['Value_mean'])
self.aggregate_direct_mean.index.name = 'Datetime'
self.aggregate_direct_mean = tsutils.memory_optimize(
self.aggregate_direct_mean)
ts = pandas.Series([48, 48], index=dr)
self.aggregate_direct_sum = pandas.DataFrame(ts, columns=['Value_sum'])
self.aggregate_direct_sum.index.name = 'Datetime'
self.aggregate_direct_sum = tsutils.memory_optimize(
self.aggregate_direct_sum)
self.aggregate_cli_mean = b"""Datetime,Value_mean
2011-01-01,2
2011-01-02,2
"""
self.aggregate_cli_sum = b"""Datetime,Value_sum
def setUp(self):
dindex = pd.date_range("2011-01-01T00:00:00", periods=26, freq="H")
self.ats = np.ones((26)) * 2
self.ats = pd.DataFrame(self.ats,
index=dindex,
columns=["Value_with_missing::fill"])
self.ats.index.name = "Datetime"
self.ats = tsutils.memory_optimize(self.ats)
self.ats_cli = capture.capture(tsutils._printiso, self.ats)
self.ffill_compare = self.ats.copy()
self.ffill_compare["Value_with_missing::fill"][
"2011-01-01T09:00:00":"2011-01-01T12:00:00"] = 3
self.ffill_compare["Value_with_missing::fill"][
"2011-01-01T13:00:00"] = 9
self.ffill_compare_cli = capture.capture(tsutils._printiso,
self.ffill_compare)
self.bfill_compare = self.ats.copy()
self.bfill_compare["Value_with_missing::fill"][
"2011-01-01T09:00:00"] = 3
self.bfill_compare["Value_with_missing::fill"][
"2011-01-01T10:00:00":"2011-01-01T13:00:00"] = 9
self.bfill_compare_cli = capture.capture(tsutils._printiso,
self.bfill_compare)
self.linear_compare = self.ats.copy()
self.linear_compare["Value_with_missing::fill"][
"2011-01-01T09:00:00"] = 3.0
self.linear_compare["Value_with_missing::fill"][
"2011-01-01T10:00:00"] = 4.5
self.linear_compare["Value_with_missing::fill"][
"2011-01-01T11:00:00"] = 6.0
self.linear_compare["Value_with_missing::fill"][
"2011-01-01T12:00:00"] = 7.5
self.linear_compare["Value_with_missing::fill"][
"2011-01-01T13:00:00"] = 9.0
self.linear_compare_cli = capture.capture(tsutils._printiso,
self.linear_compare)
self.nearest_compare = self.ats.copy()
self.nearest_compare["Value_with_missing::fill"][
"2011-01-01T09:00:00":"2011-01-01T11:00:00"] = 3.0
self.nearest_compare["Value_with_missing::fill"][
"2011-01-01T12:00:00":"2011-01-01T13:00:00"] = 9.0
self.nearest_compare = tsutils.memory_optimize(self.nearest_compare)
self.nearest_compare_cli = capture.capture(tsutils._printiso,
self.nearest_compare)
self.mean_compare = self.ats.copy()
self.mean_compare["Value_with_missing::fill"][
"2011-01-01T01:00:00"] = 2.4210526315789473
self.mean_compare["Value_with_missing::fill"][
"2011-01-01T09:00:00"] = 3.0
self.mean_compare["Value_with_missing::fill"][
"2011-01-01T10:00:00"] = 2.4210526315789473
self.mean_compare["Value_with_missing::fill"][
"2011-01-01T11:00:00"] = 2.4210526315789473
self.mean_compare["Value_with_missing::fill"][
"2011-01-01T12:00:00"] = 2.4210526315789473
self.mean_compare["Value_with_missing::fill"][
"2011-01-01T13:00:00"] = 9.0
self.mean_compare["Value_with_missing::fill"][
"2011-01-01T16:00:00"] = 2.4210526315789473
self.mean_compare["Value_with_missing::fill"][
"2011-01-01T22:00:00"] = 2.4210526315789473
self.mean_compare["Value_with_missing::fill"][
"2011-01-01T23:00:00"] = 2.4210526315789473
self.mean_compare_cli = capture.capture(tsutils._printiso,
self.mean_compare)
self.median_compare = self.ats.copy()
self.median_compare["Value_with_missing::fill"][
"2011-01-01T01:00:00"] = 2.0
self.median_compare["Value_with_missing::fill"][
"2011-01-01T09:00:00"] = 3.0
self.median_compare["Value_with_missing::fill"][
"2011-01-01T10:00:00"] = 2.0
self.median_compare["Value_with_missing::fill"][
"2011-01-01T11:00:00"] = 2.0
self.median_compare["Value_with_missing::fill"][
"2011-01-01T12:00:00"] = 2.0
self.median_compare["Value_with_missing::fill"][
"2011-01-01T13:00:00"] = 9.0
self.median_compare["Value_with_missing::fill"][
"2011-01-01T16:00:00"] = 2.0
self.median_compare["Value_with_missing::fill"][
"2011-01-01T22:00:00"] = 2.0
self.median_compare["Value_with_missing::fill"][
"2011-01-01T23:00:00"] = 2.0
self.median_compare = tsutils.memory_optimize(self.median_compare)
self.median_compare_cli = capture.capture(tsutils._printiso,
self.median_compare)
self.max_compare = self.ats.copy()
self.max_compare["Value_with_missing::fill"][
"2011-01-01T01:00:00"] = 9.0
self.max_compare["Value_with_missing::fill"][
"2011-01-01T09:00:00"] = 3.0
self.max_compare["Value_with_missing::fill"][
"2011-01-01T10:00:00"] = 9.0
self.max_compare["Value_with_missing::fill"][
"2011-01-01T11:00:00"] = 9.0
self.max_compare["Value_with_missing::fill"][
"2011-01-01T12:00:00"] = 9.0
self.max_compare["Value_with_missing::fill"][
"2011-01-01T13:00:00"] = 9.0
self.max_compare["Value_with_missing::fill"][
"2011-01-01T16:00:00"] = 9.0
self.max_compare["Value_with_missing::fill"][
"2011-01-01T22:00:00"] = 9.0
self.max_compare["Value_with_missing::fill"][
"2011-01-01T23:00:00"] = 9.0
self.max_compare = tsutils.memory_optimize(self.max_compare)
self.max_compare_cli = capture.capture(tsutils._printiso,
self.max_compare)
self.min_compare = self.ats.copy()
self.min_compare["Value_with_missing::fill"][
"2011-01-01T01:00:00"] = 2.0
self.min_compare["Value_with_missing::fill"][
"2011-01-01T09:00:00"] = 3.0
self.min_compare["Value_with_missing::fill"][
"2011-01-01T10:00:00"] = 2.0
self.min_compare["Value_with_missing::fill"][
"2011-01-01T11:00:00"] = 2.0
self.min_compare["Value_with_missing::fill"][
"2011-01-01T12:00:00"] = 2.0
self.min_compare["Value_with_missing::fill"][
"2011-01-01T13:00:00"] = 9.0
self.min_compare["Value_with_missing::fill"][
"2011-01-01T16:00:00"] = 2.0
self.min_compare["Value_with_missing::fill"][
"2011-01-01T22:00:00"] = 2.0
self.min_compare["Value_with_missing::fill"][
"2011-01-01T23:00:00"] = 2.0
self.min_compare = tsutils.memory_optimize(self.min_compare)
self.min_compare_cli = capture.capture(tsutils._printiso,
self.min_compare)
self.con_compare = self.ats.copy()
self.con_compare["Value_with_missing::fill"][
"2011-01-01T01:00:00"] = 2.42
self.con_compare["Value_with_missing::fill"][
"2011-01-01T09:00:00"] = 3.0
self.con_compare["Value_with_missing::fill"][
"2011-01-01T10:00:00"] = 2.42
self.con_compare["Value_with_missing::fill"][
"2011-01-01T11:00:00"] = 2.42
self.con_compare["Value_with_missing::fill"][
"2011-01-01T12:00:00"] = 2.42
self.con_compare["Value_with_missing::fill"][
"2011-01-01T13:00:00"] = 9.0
self.con_compare["Value_with_missing::fill"][
"2011-01-01T16:00:00"] = 2.42
self.con_compare["Value_with_missing::fill"][
"2011-01-01T22:00:00"] = 2.42
self.con_compare["Value_with_missing::fill"][
"2011-01-01T23:00:00"] = 2.42
self.con_compare_cli = capture.capture(tsutils._printiso,
self.con_compare)
def setUp(self):
dindex = pd.date_range("2000-01-01T00:00:00", periods=2, freq="D")
ts1 = pd.Series([4.5, 4.6], index=dindex)
ts2 = pd.Series([20.0, 20.4], index=dindex)
self.equation = pd.DataFrame(
{"Value": ts1, "Value::equation": ts2}, index=dindex, dtype="float64"
)
self.equation.index.name = "Datetime"
self.equation = tsutils.memory_optimize(self.equation)
self.equation_cli = capture.capture(tsutils._printiso, self.equation)
dindex = pd.date_range("2000-01-01T00:00:00", periods=6, freq="D")
ts1 = [4.50, 4.60, 4.70, 4.60, 4.50, 4.40]
ts2 = [45.60, 90.50, 34.20, 23.10, 7.20, 4.30]
ts3 = [
-4.00389911875,
-5.61841686236,
-5.87322876716,
-4.86614401542,
-2.6934178416,
-4.60800663972,
]
self.equation_multiple_cols_01 = pd.DataFrame(
{"Value": ts1, "Value1": ts2, "_::equation": ts3},
index=dindex,
dtype="float64",
)
self.equation_multiple_cols_01.index.name = "Datetime"
self.equation_multiple_cols_01 = tsutils.memory_optimize(
self.equation_multiple_cols_01
)
self.equation_result = pd.DataFrame(
{"_::equation": pd.np.array(ts2) * 10}, index=dindex, dtype="float64"
)
self.equation_result = tsutils.memory_optimize(self.equation_result)
self.equation_multiple_cols_01_cli = capture.capture(
tsutils._printiso, self.equation_multiple_cols_01
)
ts3 = [50.1, 95.1, 38.9, 27.7, 11.7, 8.7]
self.equation_multiple_cols_02 = pd.DataFrame(
{"Value": ts1, "Value1": ts2, "_::equation": ts3},
index=dindex,
dtype="float64",
)
self.equation_multiple_cols_02.index.name = "Datetime"
self.equation_multiple_cols_02 = tsutils.memory_optimize(
self.equation_multiple_cols_02
)
self.equation_multiple_cols_02_cli = capture.capture(
tsutils._printiso, self.equation_multiple_cols_02
)
ts3 = [0, 97.92, 41.66, 30.52, 14.46, 0]
ts3[0] = pd.np.nan
ts3[-1] = pd.np.nan
self.equation_multiple_cols_03 = pd.DataFrame(
{"Value": ts1, "Value1": ts2, "_::equation": ts3},
index=dindex,
dtype="float64",
)
self.equation_multiple_cols_03.index.name = "Datetime"
self.equation_multiple_cols_03 = tsutils.memory_optimize(
self.equation_multiple_cols_03
)
self.equation_multiple_cols_03_cli = capture.capture(
tsutils._printiso, self.equation_multiple_cols_03
)
dindex = pd.date_range("2011-01-01T00:00:00", periods=48, freq="H")
ts1 = [2] * 48
ts2 = [5.2] * 48
ts2[0] = pd.np.nan
ts2[-1] = pd.np.nan
self.equation_multiple_cols_04 = pd.DataFrame(
{"Value": ts1, "Value::equation": ts2}, index=dindex
)
self.equation_multiple_cols_04.index.name = "Datetime"
self.equation_multiple_cols_04 = tsutils.memory_optimize(
self.equation_multiple_cols_04
)
self.equation_multiple_cols_04_cli = capture.capture(
tsutils._printiso, self.equation_multiple_cols_04
)
def setUp(self):
self.read_direct = pd.read_csv('tests/data_sunspot_index.csv',
index_col=0)
self.read_direct = tsutils.memory_optimize(self.read_direct)
def setUp(self):
dr = pd.date_range('2011-01-01T12:00:00', periods=3, freq='H')
self.date_slice = pd.DataFrame([2, 2, 2], index=dr, columns=['Value'])
self.date_slice = tsutils.memory_optimize(self.date_slice)
self.date_slice_cli = capture.capture(tsutils._printiso, self.date_slice)
def setUp(self):
self.ats = pd.read_csv(StringIO(test_sinwave),
parse_dates=True,
index_col=[0])
self.ats = tsutils.memory_optimize(self.ats)
def setUp(self):
dindex = pd.date_range('2000-01-01T00:00:00', periods=2, freq='D')
ts1 = pd.Series([4.5, 4.6], index=dindex)
ts2 = pd.Series([20.0, 20.4], index=dindex)
self.equation = pd.DataFrame({'Value': ts1,
'Value::equation': ts2},
index=dindex,
dtype='float64')
self.equation.index.name = 'Datetime'
self.equation = tsutils.memory_optimize(self.equation)
self.equation_cli = capture.capture(tsutils._printiso, self.equation)
dindex = pd.date_range('2000-01-01T00:00:00', periods=6, freq='D')
ts1 = [4.50, 4.60, 4.70, 4.60, 4.50, 4.40]
ts2 = [45.60, 90.50, 34.20, 23.10, 7.20, 4.30]
ts3 = [-4.00389911875,
-5.61841686236,
-5.87322876716,
-4.86614401542,
-2.6934178416,
-4.60800663972]
self.equation_multiple_cols_01 = pd.DataFrame({'Value': ts1,
'Value1': ts2,
'_::equation': ts3},
index=dindex,
dtype='float64')
self.equation_multiple_cols_01.index.name = 'Datetime'
self.equation_multiple_cols_01 = tsutils.memory_optimize(self.equation_multiple_cols_01)
self.equation_result = pd.DataFrame({'_::equation': pd.np.array(ts2)*10},
index=dindex,
dtype='float64')
self.equation_result = tsutils.memory_optimize(self.equation_result)
self.equation_multiple_cols_01_cli = capture.capture(tsutils._printiso,
self.equation_multiple_cols_01)
ts3 = [50.1, 95.1, 38.9, 27.7, 11.7, 8.7]
self.equation_multiple_cols_02 = pd.DataFrame({'Value': ts1,
'Value1': ts2,
'_::equation': ts3},
index=dindex,
dtype='float64')
self.equation_multiple_cols_02.index.name = 'Datetime'
self.equation_multiple_cols_02 = tsutils.memory_optimize(self.equation_multiple_cols_02)
self.equation_multiple_cols_02_cli = capture.capture(tsutils._printiso,
self.equation_multiple_cols_02)
ts3 = [0, 97.92, 41.66, 30.52, 14.46, 0]
ts3[0] = pd.np.nan
ts3[-1] = pd.np.nan
self.equation_multiple_cols_03 = pd.DataFrame({'Value': ts1,
'Value1': ts2,
'_::equation': ts3},
index=dindex,
dtype='float64')
self.equation_multiple_cols_03.index.name = 'Datetime'
self.equation_multiple_cols_03 = tsutils.memory_optimize(self.equation_multiple_cols_03)
self.equation_multiple_cols_03_cli = capture.capture(tsutils._printiso,
self.equation_multiple_cols_03)
dindex = pd.date_range('2011-01-01T00:00:00', periods=48, freq='H')
ts1 = [2]*48
ts2 = [5.2]*48
ts2[0] = pd.np.nan
ts2[-1] = pd.np.nan
self.equation_multiple_cols_04 = pd.DataFrame({'Value': ts1,
'Value::equation': ts2},
index=dindex)
self.equation_multiple_cols_04.index.name = 'Datetime'
self.equation_multiple_cols_04 = tsutils.memory_optimize(self.equation_multiple_cols_04)
self.equation_multiple_cols_04_cli = capture.capture(tsutils._printiso,
self.equation_multiple_cols_04)
def setUp(self):
self.read_direct = pd.read_csv(
'tests/data_sunspot_EST.csv', index_col=0,
parse_dates=[0]).tz_localize('UTC').tz_convert('EST')
self.read_direct = tsutils.memory_optimize(self.read_direct)
def setUp(self):
self.read_direct = pd.read_csv(
"tests/data_gainesville_daily_precip_index.csv", index_col=0, header=0
)
self.read_direct = tsutils.memory_optimize(self.read_direct)
