reconnect moved files to git repo

venv/lib/python3.11/site-packages/statsmodels/tsa/base/tests/test_base.py

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+from statsmodels.compat.pandas import PD_LT_2_2_0
+
+from datetime import datetime
+
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
+import pytest
+
+from statsmodels.tools.sm_exceptions import ValueWarning
+from statsmodels.tools.testing import assert_equal
+from statsmodels.tsa.base.tsa_model import TimeSeriesModel
+
+YE_APR = "A-APR" if PD_LT_2_2_0 else "YE-APR"
+
+def test_pandas_nodates_index():
+
+    data = [988, 819, 964]
+    dates = ['a', 'b', 'c']
+    s = pd.Series(data, index=dates)
+
+    # TODO: Remove this, this is now valid
+    # npt.assert_raises(ValueError, TimeSeriesModel, s)
+
+    # Test with a non-date index that does not raise an exception because it
+    # can be coerced into a nanosecond DatetimeIndex
+    data = [988, 819, 964]
+    # index=pd.date_range('1970-01-01', periods=3, freq='QS')
+    index = pd.to_datetime([100, 101, 102])
+    s = pd.Series(data, index=index)
+
+    actual_str = (index[0].strftime('%Y-%m-%d %H:%M:%S.%f') +
+                  str(index[0].value))
+    assert_equal(actual_str, '1970-01-01 00:00:00.000000100')
+
+    with pytest.warns(ValueWarning, match="No frequency information"):
+        mod = TimeSeriesModel(s)
+
+    start, end, out_of_sample, _ = mod._get_prediction_index(0, 4)
+    assert_equal(len(mod.data.predict_dates), 5)
+
+
+def test_predict_freq():
+    # test that predicted dates have same frequency
+    x = np.arange(1,36.)
+
+    # there's a bug in pandas up to 0.10.2 for YearBegin
+    #dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR")
+
+    dates = pd.date_range("1972-4-30", "2006-4-30", freq=YE_APR)
+    series = pd.Series(x, index=dates)
+    model = TimeSeriesModel(series)
+    #npt.assert_(model.data.freq == "AS-APR")
+    # two possabilities due to future changes in pandas 2.2+
+    assert model._index.freqstr in ("Y-APR", "A-APR", "YE-APR")
+
+    start, end, out_of_sample, _ = (
+        model._get_prediction_index("2006-4-30", "2016-4-30"))
+
+    predict_dates = model.data.predict_dates
+
+    #expected_dates = date_range("2006-12-31", "2016-12-31",
+    #                            freq="AS-APR")
+    expected_dates = pd.date_range("2006-4-30", "2016-4-30", freq=YE_APR)
+    assert_equal(predict_dates, expected_dates)
+    #ptesting.assert_series_equal(predict_dates, expected_dates)
+
+
+def test_keyerror_start_date():
+    x = np.arange(1,36.)
+
+    # dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR")
+    dates = pd.date_range("1972-4-30", "2006-4-30", freq=YE_APR)
+    series = pd.Series(x, index=dates)
+    model = TimeSeriesModel(series)
+
+    npt.assert_raises(KeyError, model._get_prediction_index, "1970-4-30", None)
+
+
+def test_period_index():
+    # test 1285
+
+    dates = pd.period_range(start="1/1/1990", periods=20, freq="M")
+    x = np.arange(1, 21.)
+
+    model = TimeSeriesModel(pd.Series(x, index=dates))
+    assert_equal(model._index.freqstr, "M")
+    model = TimeSeriesModel(pd.Series(x, index=dates))
+    npt.assert_(model.data.freq == "M")
+
+
+def test_pandas_dates():
+
+    data = [988, 819, 964]
+    dates = ['2016-01-01 12:00:00', '2016-02-01 12:00:00', '2016-03-01 12:00:00']
+
+    datetime_dates = pd.to_datetime(dates)
+
+    result = pd.Series(data=data, index=datetime_dates, name='price')
+    df = pd.DataFrame(data={'price': data}, index=pd.DatetimeIndex(dates, freq='MS'))
+
+    model = TimeSeriesModel(df['price'])
+
+    assert_equal(model.data.dates, result.index)
+
+
+def test_get_predict_start_end():
+    index = pd.date_range(start='1970-01-01', end='1990-01-01', freq='YS')
+    endog = pd.Series(np.zeros(10), index[:10])
+    model = TimeSeriesModel(endog)
+
+    predict_starts = [1, '1971-01-01', datetime(1971, 1, 1), index[1]]
+    predict_ends = [20, '1990-01-01', datetime(1990, 1, 1), index[-1]]
+
+    desired = (1, 9, 11)
+    for start in predict_starts:
+        for end in predict_ends:
+            assert_equal(model._get_prediction_index(start, end)[:3], desired)

venv/lib/python3.11/site-packages/statsmodels/tsa/base/tests/test_datetools.py

@@ -0,0 +1,88 @@
+from datetime import datetime
+
+import numpy.testing as npt
+
+from statsmodels.tsa.base.datetools import date_parser, dates_from_range
+
+
+def test_regex_matching_month():
+    t1 = "1999m4"
+    t2 = "1999:m4"
+    t3 = "1999:mIV"
+    t4 = "1999mIV"
+    result = datetime(1999, 4, 30)
+    npt.assert_equal(date_parser(t1), result)
+    npt.assert_equal(date_parser(t2), result)
+    npt.assert_equal(date_parser(t3), result)
+    npt.assert_equal(date_parser(t4), result)
+
+
+def test_regex_matching_quarter():
+    t1 = "1999q4"
+    t2 = "1999:q4"
+    t3 = "1999:qIV"
+    t4 = "1999qIV"
+    result = datetime(1999, 12, 31)
+    npt.assert_equal(date_parser(t1), result)
+    npt.assert_equal(date_parser(t2), result)
+    npt.assert_equal(date_parser(t3), result)
+    npt.assert_equal(date_parser(t4), result)
+
+
+def test_dates_from_range():
+    results = [datetime(1959, 3, 31, 0, 0),
+               datetime(1959, 6, 30, 0, 0),
+               datetime(1959, 9, 30, 0, 0),
+               datetime(1959, 12, 31, 0, 0),
+               datetime(1960, 3, 31, 0, 0),
+               datetime(1960, 6, 30, 0, 0),
+               datetime(1960, 9, 30, 0, 0),
+               datetime(1960, 12, 31, 0, 0),
+               datetime(1961, 3, 31, 0, 0),
+               datetime(1961, 6, 30, 0, 0),
+               datetime(1961, 9, 30, 0, 0),
+               datetime(1961, 12, 31, 0, 0),
+               datetime(1962, 3, 31, 0, 0),
+               datetime(1962, 6, 30, 0, 0)]
+    dt_range = dates_from_range('1959q1', '1962q2')
+    npt.assert_(results == dt_range)
+
+    # test with starting period not the first with length
+    results = results[2:]
+    dt_range = dates_from_range('1959q3', length=len(results))
+    npt.assert_(results == dt_range)
+
+    # check month
+    results = [datetime(1959, 3, 31, 0, 0),
+               datetime(1959, 4, 30, 0, 0),
+               datetime(1959, 5, 31, 0, 0),
+               datetime(1959, 6, 30, 0, 0),
+               datetime(1959, 7, 31, 0, 0),
+               datetime(1959, 8, 31, 0, 0),
+               datetime(1959, 9, 30, 0, 0),
+               datetime(1959, 10, 31, 0, 0),
+               datetime(1959, 11, 30, 0, 0),
+               datetime(1959, 12, 31, 0, 0),
+               datetime(1960, 1, 31, 0, 0),
+               datetime(1960, 2, 28, 0, 0),
+               datetime(1960, 3, 31, 0, 0),
+               datetime(1960, 4, 30, 0, 0),
+               datetime(1960, 5, 31, 0, 0),
+               datetime(1960, 6, 30, 0, 0),
+               datetime(1960, 7, 31, 0, 0),
+               datetime(1960, 8, 31, 0, 0),
+               datetime(1960, 9, 30, 0, 0),
+               datetime(1960, 10, 31, 0, 0),
+               datetime(1960, 12, 31, 0, 0),
+               datetime(1961, 1, 31, 0, 0),
+               datetime(1961, 2, 28, 0, 0),
+               datetime(1961, 3, 31, 0, 0),
+               datetime(1961, 4, 30, 0, 0),
+               datetime(1961, 5, 31, 0, 0),
+               datetime(1961, 6, 30, 0, 0),
+               datetime(1961, 7, 31, 0, 0),
+               datetime(1961, 8, 31, 0, 0),
+               datetime(1961, 9, 30, 0, 0),
+               datetime(1961, 10, 31, 0, 0)]
+
+    dt_range = dates_from_range("1959m3", length=len(results))

venv/lib/python3.11/site-packages/statsmodels/tsa/base/tests/test_prediction.py

@@ -0,0 +1,47 @@
+from statsmodels.compat.pandas import MONTH_END
+
+import numpy as np
+import pandas as pd
+import pytest
+from scipy import stats
+
+from statsmodels.tsa.base.prediction import PredictionResults
+
+
+@pytest.fixture(params=[True, False])
+def data(request):
+    mean = np.arange(10.0)
+    variance = np.arange(1, 11.0)
+    if not request.param:
+        return mean, variance
+    idx = pd.date_range("2000-1-1", periods=10, freq=MONTH_END)
+    return pd.Series(mean, index=idx), pd.Series(variance, index=idx)
+
+
+def test_basic(data):
+    is_pandas = isinstance(data[0], pd.Series)
+    pred = PredictionResults(data[0], data[1])
+    np.testing.assert_allclose(data[0], pred.predicted_mean)
+    np.testing.assert_allclose(data[1], pred.var_pred_mean)
+    if is_pandas:
+        assert isinstance(pred.predicted_mean, pd.Series)
+        assert isinstance(pred.var_pred_mean, pd.Series)
+        assert isinstance(pred.se_mean, pd.Series)
+    frame = pred.summary_frame()
+    assert isinstance(frame, pd.DataFrame)
+    assert list(
+        frame.columns == ["mean", "mean_se", "mean_ci_lower", "mean_ci_upper"]
+    )
+
+
+@pytest.mark.parametrize("dist", [None, "norm", "t", stats.norm()])
+def test_dist(data, dist):
+    df = 10 if dist == "t" else None
+    pred = PredictionResults(data[0], data[1], dist=dist, df=df)
+    basic = PredictionResults(data[0], data[1])
+    ci = pred.conf_int()
+    basic_ci = basic.conf_int()
+    if dist == "t":
+        assert np.all(np.asarray(ci != basic_ci))
+    else:
+        assert np.all(np.asarray(ci == basic_ci))