def test__to_text__without_metadata(self):
path = self.target_dir + "/to_text_without_metadata"
my_time_series = TimeSeries(self.my_series)
my_time_series.to_text(path)
data_csv_path = "{}/{}.{}".format(path, TIME_SERIES_FILENAME,
TIME_SERIES_EXT)
does_data_csv_exist = os_path.exists(data_csv_path)
self.assertTrue(does_data_csv_exist)
meta_json_path = "{}/{}.{}".format(path, METADATA_FILENAME,
METADATA_EXT)
does_meta_json_exist = os_path.exists(meta_json_path)
self.assertFalse(does_meta_json_exist)
class TestTimeSeries(TestCase):
def setUp(self) -> None:
# Create a time indexed series
index = DatetimeIndex(
['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04'])
self.my_series = Series([0.4, 1.0, 0.7, 0.6], index=index)
# Create metadata
my_unit = {"name": "power", "symbol": "W", "data_type": "float"}
my_coordinates = {"lat": 46.796611, "lon": 7.147563}
my_dict = {"unit": my_unit, "coordinates": my_coordinates}
self.my_metadata = Metadata(my_dict)
self.my_time_series = TimeSeries(self.my_series, self.my_metadata)
# Define a target directory
self.target_dir = "data/test-export"
def test__init__is_instance(self):
self.my_time_series = TimeSeries()
self.assertIsInstance(self.my_time_series, TimeSeries,
"The TimeSeries hasn't the right type")
def test__init__has_right_types(self):
# Add some data
index = DatetimeIndex(
['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04'])
my_series = DataFrame([0.4, 1.0, 0.7, 0.6], index=index)
my_metadata = Metadata()
my_ts = TimeSeries(my_series, my_metadata)
# Check types
self.assertIsInstance(
my_ts.series, DataFrame,
"The TimeSeries series is not a Pandas DataFrame")
self.assertIsInstance(
my_ts.metadata, Metadata,
"The TimeSeries Metadata hasn't got the right type")
def test__init__contains_metadata(self):
# Add some data
index = DatetimeIndex(
['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04'])
my_series = DataFrame([0.4, 1.0, 0.7, 0.6], index=index)
my_metadata = Metadata()
my_ts = TimeSeries(my_series, my_metadata)
# Check types
self.assertNotEqual(my_ts.metadata, None,
"The TimeSeries Metadata is probably None")
def test__init__has_values_as_column_name(self):
index = DatetimeIndex(
['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04'])
my_series = Series([0.4, 1.0, 0.7, 0.6], index=index)
ts = TimeSeries(my_series)
self.assertTrue(TIME_SERIES_VALUES in ts.series.columns)
def test__init__wrong_index_type(self):
values = Series([0.4, 1.0, 0.7, 0.6])
with self.assertRaises(AssertionError):
TimeSeries(values)
def test__init__with_Series_input(self):
index = DatetimeIndex(
['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04'])
my_series = Series([0.4, 1.0, 0.7, 0.6], index=index)
ts = TimeSeries(my_series)
self.assertTrue(TIME_SERIES_VALUES in ts.series.columns)
self.assertIsInstance(ts, TimeSeries)
def test__init__with_DataFrame_input_single_column(self):
index = DatetimeIndex(
['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04'])
my_series = Series([0.4, 1.0, 0.7, 0.6], index=index)
df = DataFrame(data=my_series)
ts = TimeSeries(df)
self.assertTrue(TIME_SERIES_VALUES in ts.series.columns)
self.assertIsInstance(ts, TimeSeries)
def test__init__with_DataFrame_input_many_columns__without_values(self):
index = DatetimeIndex(
['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04'])
my_series = Series([0.4, 1.0, 0.7, 0.6], index=index)
df = DataFrame({"one": my_series, "two": my_series})
with self.assertRaises(AssertionError):
ts = TimeSeries(df)
def test__init__with_DataFrame_input_many_columns__with_values(self):
index = DatetimeIndex(
['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04'])
my_series = Series([0.4, 1.0, 0.7, 0.6], index=index)
df = DataFrame({TIME_SERIES_VALUES: my_series, "two": my_series})
ts = TimeSeries(df)
self.assertIsInstance(ts, TimeSeries)
def test__init__freq_is_infered(self):
index = DatetimeIndex(
['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04'])
my_series = Series([0.4, 1.0, 0.7, 0.6], index=index)
ts = TimeSeries(my_series)
self.assertEqual(infer_freq(index), ts.series.index.freq)
def test__create(self):
ts = TimeSeries.create("01-01-2020", "02-01-2020")
self.assertIsInstance(ts, TimeSeries)
# Test if all elements are NaNs
self.assertTrue(ts.series.isna().all().values[0])
# Test if frequency is daily
self.assertEqual(ts.series.index.inferred_freq, 'D')
def test__create__is_regular(self):
ts = TimeSeries.create("01-01-2020", "02-01-2020")
no_duration_diff = ts.index.to_series().diff().diff()[2:] == \
Timedelta(0)
is_regular = no_duration_diff.eq(True).all()
self.assertTrue(is_regular)
def test__create__with_freq_as_str(self):
ts = TimeSeries.create("01-01-2020", "02-01-2020", "H")
self.assertIsInstance(ts, TimeSeries)
# Test if all elements are NaNs
self.assertTrue(ts.series.isna().all().values[0])
# Test if frequency is daily
self.assertEqual(ts.series.index.inferred_freq, 'H')
def test__create__with_freq_as_time_series(self):
ts_freq = TimeSeries.create("01-01-2020", "02-01-2020", "H")
ts = TimeSeries.create("01-01-2020", "02-01-2020", ts_freq)
self.assertIsInstance(ts, TimeSeries)
# Test if all elements are NaNs
self.assertTrue(ts.series.isna().all().values[0])
# Test if frequency is daily
self.assertEqual(ts.series.index.inferred_freq,
ts_freq.series.index.inferred_freq)
def test__plot__returns_graph_object_axes(self):
ts = TimeSeries.create("01-01-2020", "02-01-2020", "H")
my_fig = ts.plot()
self.assertIsInstance(my_fig, Axes)
def test__plot__returns_graph_object_plotly(self):
ts = TimeSeries.create("01-01-2020", "02-01-2020", "H")
my_fig = ts.plot(context="notebook")
self.assertIsInstance(my_fig, Figure)
def test__copy__shallow(self):
# object creation
ts = TimeSeries.create("01-2020", "03-2020", "H")
copy = ts.copy(deep=False)
self.assertNotEqual(id(ts), id(copy))
def test__copy__deep(self):
# object creation
ts = TimeSeries.create("01-2020", "03-2020", "H")
copy = ts.copy(deep=True)
self.assertNotEqual(id(ts), id(copy))
def test__split_at(self):
# Create TimeSeries and split it
ts = TimeSeries.create("01-01-2020", "03-01-2020", "H")
a, b = ts.split_at("02-01-2020 00:00")
# Get all the indexes
ts_start = ts.series[TIME_SERIES_VALUES].index[0]
ts_end = ts.series[TIME_SERIES_VALUES].index[-1]
a_start = a.series[TIME_SERIES_VALUES].index[0]
a_end = a.series[TIME_SERIES_VALUES].index[-1]
b_start = b.series[TIME_SERIES_VALUES].index[0]
b_end = b.series[TIME_SERIES_VALUES].index[-1]
# Test boundaries
self.assertEqual(ts_start, a_start)
self.assertEqual(ts_end, b_end)
# Test split point
self.assertEqual(a_end, b_start)
def test__split_in_chunks(self):
ts = TimeSeries.create("01-01-2020", "03-01-2020", "H")
chunk_len = 5
# method at test
chunks = ts.split_in_chunks(chunk_len)
# test all element but last
for ts_chunk in chunks[:-1]:
self.assertEqual(len(ts_chunk), chunk_len)
# test last element
self.assertLessEqual(len(chunks[-1]), chunk_len)
def test__fill(self):
ts = TimeSeries.create("01-01-2020", "03-01-2020", "H")
val = 42
# method at test
ts = ts.fill(val)
# test
for i in ts:
self.assertEqual(val, i)
def test__empty(self):
ts = TimeSeries.create("01-01-2020", "03-01-2020", "H")
# method at test
ts = ts.empty()
# test
for i in ts:
self.assertTrue(np.isnan(i))
def test__pad(self):
def is_regular(ts):
# test if double timestamps in ts
no_duration_diff = ts.index.to_series().diff().diff()[2:] == \
Timedelta(0)
return no_duration_diff.eq(True).all()
def is_monotonic(ts):
# test if monotonic
return ts.series.index.is_monotonic
def is_freq_similar(ts_before, ts_after):
# test if freq is the same
return ts_before.frequency() == ts_after.frequency()
# Create TimeSeries
ts_1 = TimeSeries.create("04-2020", "05-2020", "D")
# Pad before
ts_1_padded_before = ts_1.pad("03-2020")["2020-03-31":"2020-04-02"]
self.assertTrue(is_regular(ts_1_padded_before))
self.assertTrue(is_monotonic(ts_1_padded_before))
self.assertTrue(is_freq_similar(ts_1, ts_1_padded_before))
# Pad after
ts_1_padded_after = ts_1.pad("06-2020")["2020-05-29":"2020-06-02"]
self.assertTrue(is_regular(ts_1_padded_after))
self.assertTrue(is_monotonic(ts_1_padded_after))
self.assertTrue(is_freq_similar(ts_1, ts_1_padded_after))
# Pad during (wrong case)
with self.assertRaises(ValueError):
ts_1.pad("2020-04-15")
def test__trim__both_side_by_default(self):
# Prepare TimeSeries with int as values
start = Timestamp("01-01-2020")
end = Timestamp("03-01-2020")
ts_initial = TimeSeries.create(start, end, "H")
ts_initial = ts_initial.fill(42)
# Pad the TimeSeries with np.nans
ts_pad = ts_initial.pad("01-12-2019").pad("05-01-2020")
# Method at test
ts_trimmed = ts_pad.trim()
# test if no NaNs
self.assertFalse(ts_trimmed.series.isna().values.any())
# test boundaries
new_start, new_end = ts_trimmed.boundaries()
self.assertEqual(start, new_start)
self.assertEqual(end, new_end)
def test__trim__only_start(self):
# Prepare TimeSeries with int as values
start = Timestamp("01-01-2020")
end = Timestamp("03-01-2020")
ts_initial = TimeSeries.create(start, end, "H")
ts_initial = ts_initial.fill(42)
# Pad the TimeSeries with np.nans
pad_start = Timestamp("01-12-2019")
pad_end = Timestamp("05-01-2020")
ts_pad = ts_initial.pad(pad_start).pad(pad_end)
# Method at test
ts_trimmed = ts_pad.trim(side="start")
# test if NaNs
self.assertTrue(ts_trimmed.series.isna().values.any())
# test boundaries
new_start, new_end = ts_trimmed.boundaries()
self.assertEqual(start, new_start)
self.assertEqual(pad_end, new_end)
def test__trim__only_end(self):
# Prepare TimeSeries with int as values
start = Timestamp("01-01-2020")
end = Timestamp("03-01-2020")
ts_initial = TimeSeries.create(start, end, "H")
ts_initial = ts_initial.fill(42)
# Pad the TimeSeries with np.nans
pad_start = Timestamp("01-12-2019")
pad_end = Timestamp("05-01-2020")
ts_pad = ts_initial.pad(pad_start).pad(pad_end)
# Method at test
ts_trimmed = ts_pad.trim(side="end")
# test if NaNs
self.assertTrue(ts_trimmed.series.isna().values.any())
# test boundaries
new_start, new_end = ts_trimmed.boundaries()
self.assertEqual(pad_start, new_start)
self.assertEqual(end, new_end)
def test__merge(self):
# Prepare test
ts1 = TimeSeries.create("01-2020", "03-2020", "H")
ts2 = TimeSeries.create("02-2020", "04-2020", "H")
# Call function
ts = ts1.merge(ts2)
# Test if index is monotonic increasing
self.assertTrue(ts.series.index.is_monotonic_increasing)
# Test if all values are there
len1 = len(ts1) + len(ts2)
len2 = len(ts)
self.assertTrue(len1 == len2)
def test__apply__on_self(self):
# Prepare test
val = 21
ts = TimeSeries.create("01-2020", "02-2020", "H").fill(val)
# Method at test
ts = ts.apply(lambda x: x * 2)
# Test
for i in ts:
self.assertEqual(i, val * 2)
def test__apply__on_other_time_series(self):
# Prepare test
val_1 = 21
val_2 = 3
ts_1 = TimeSeries.create("01-2020", "02-2020", "H").fill(val_1)
ts_2 = TimeSeries.create("01-2020", "02-2020", "H").fill(val_2)
# Method at test
ts = ts_1.apply(lambda x, y: x * y, ts_2)
# Test
for i in ts:
self.assertEqual(i, val_1 * val_2)
def test__apply__on_other_time_series_with_different_length(self):
# Prepare test
val_1 = 21
val_2 = 3
ts_1 = TimeSeries.create("01-2020", "02-2020", "H").fill(val_1)
ts_2 = TimeSeries.create("01-2020", "04-2020", "H").fill(val_2)
# Method at test
with self.assertRaises(AssertionError):
ts = ts_1.apply(lambda x, y: x * y, ts_2)
def test__time_deltas(self):
ts = TimeSeries.create("01-2020", "03-2020", "H")
deltas = ts.time_detlas()
for i in deltas[1:]:
self.assertEqual(i, 3600.0)
self.assertIs(type(i), float)
def test__to_text__without_metadata(self):
path = self.target_dir + "/to_text_without_metadata"
my_time_series = TimeSeries(self.my_series)
my_time_series.to_text(path)
data_csv_path = "{}/{}.{}".format(path, TIME_SERIES_FILENAME,
TIME_SERIES_EXT)
does_data_csv_exist = os_path.exists(data_csv_path)
self.assertTrue(does_data_csv_exist)
meta_json_path = "{}/{}.{}".format(path, METADATA_FILENAME,
METADATA_EXT)
does_meta_json_exist = os_path.exists(meta_json_path)
self.assertFalse(does_meta_json_exist)
def test__to_text__with_metadata(self):
path = self.target_dir + "/to_text_with_metadata"
self.my_time_series.to_text(path)
data_csv_path = "{}/{}.{}".format(path, TIME_SERIES_FILENAME,
TIME_SERIES_EXT)
does_data_csv_exist = os_path.exists(data_csv_path)
self.assertTrue(does_data_csv_exist)
meta_json_path = "{}/{}.{}".format(path, METADATA_FILENAME,
METADATA_EXT)
does_meta_json_exist = os_path.exists(meta_json_path)
self.assertTrue(does_meta_json_exist)
def test__to_pickle(self):
pickle_path = "{}/{}.{}".format(self.target_dir,
DEFAULT_EXPORT_FILENAME, PICKLE_EXT)
self.my_time_series.to_pickle(pickle_path)
does_pickle_exist = os_path.exists(pickle_path)
self.assertTrue(does_pickle_exist)
def test__to_df(self):
df = self.my_time_series.to_df()
self.assertTrue(df['values'].equals(self.my_series))
self.assertIsInstance(df, DataFrame)
def test__to_darts__type_check(self):
ts = TimeSeries.create("01-2020", "02-2020", "H")
ts = ts.fill(np.random.randint(0, 1000, len(ts)))
self.assertIsInstance(ts, TimeSeries)
dts = ts.to_darts()
from darts import TimeSeries as DartsTimeSeries
self.assertIsInstance(dts, DartsTimeSeries)
def test__to_darts__series_equality(self):
ts = TimeSeries.create("01-2020", "02-2020", "H")
ts = ts.fill(np.random.randint(0, 1000, len(ts)))
dts = ts.to_darts()
is_equal = ts.series[TIME_SERIES_VALUES].equals(dts.pd_series())
self.assertTrue(is_equal)
def tearDown(self) -> None:
del self.my_time_series
shutil.rmtree(self.target_dir, ignore_errors=True)