add some more tests to up coverage

tests/test_si.py

@@ -1,6 +1,7 @@
-from pandas import Series, Timestamp
+from pandas import DataFrame, Series, Timestamp
 from scipy.stats import norm
 from spei import SI, sgi, spei, spi, ssfi
+from spei.dist import Dist
 
 
 def test_spi(prec: Series) -> None:
@@ -32,3 +33,75 @@ def test_SI(prec: Series) -> None:
     si.pdf()
     dist = si.get_dist(Timestamp("2010-01-01"))
     dist.ks_test()
+
+
+def test_SI_post_init_timescale(prec: Series) -> None:
+    si = SI(prec, dist=norm, timescale=30, fit_freq="ME")
+    assert si.series.equals(
+        prec.rolling(30, min_periods=30).sum().dropna()
+    ), "Timescale rolling sum not applied correctly"
+
+
+def test_SI_post_init_fit_freq_infer(prec: Series) -> None:
+    si = SI(prec, dist=norm, timescale=0)
+    assert si.fit_freq is not None, "Frequency inference failed"
+
+
+def test_SI_post_init_grouped_year(prec: Series) -> None:
+    si = SI(prec, dist=norm, timescale=0, fit_freq="ME")
+    assert isinstance(si._grouped_year, DataFrame), "Grouped year DataFrame not created"
+
+
+def test_SI_post_init_fit_window_adjustment(prec: Series) -> None:
+    si = SI(prec, dist=norm, timescale=0, fit_freq="D", fit_window=2)
+    assert si.fit_window == 3, "Fit window not adjusted to odd number"
+
+
+def test_SI_post_init_fit_window_minimum(prec: Series) -> None:
+    si = SI(prec, dist=norm, timescale=0, fit_freq="D", fit_window=1)
+    assert si.fit_window == 3, "Fit window not adjusted to minimum value"
+
+
+def test_fit_distribution_normal_scores_transform(prec: Series) -> None:
+    si = SI(prec, dist=norm, timescale=30, fit_freq="ME", normal_scores_transform=True)
+    si.fit_distribution()
+    assert (
+        not si._dist_dict
+    ), "Distribution dictionary should be empty when using normal scores transform"
+
+
+def test_fit_distribution_with_fit_window(prec: Series) -> None:
+    si = SI(prec, dist=norm, timescale=30, fit_freq="D", fit_window=5)
+    si.fit_distribution()
+    assert (
+        si._dist_dict
+    ), "Distribution dictionary should not be empty when using fit window"
+    for dist in si._dist_dict.values():
+        assert isinstance(
+            dist, Dist
+        ), "Items in distribution dictionary should be of type Dist"
+
+
+def test_fit_distribution_with_fit_freq(prec: Series) -> None:
+    si = SI(prec, dist=norm, timescale=30, fit_freq="M")
+    si.fit_distribution()
+    assert (
+        si._dist_dict
+    ), "Distribution dictionary should not be empty when using fit frequency"
+    for dist in si._dist_dict.values():
+        assert isinstance(
+            dist, Dist
+        ), "Items in distribution dictionary should be of type Dist"
+
+
+def test_fit_distribution_invalid_fit_freq_with_window(prec: Series) -> None:
+    si = SI(prec, dist=norm, timescale=30, fit_freq="M", fit_window=5)
+    try:
+        si.fit_distribution()
+    except ValueError as e:
+        assert (
+            str(e)
+            == "Frequency fit_freq must be 'D' or 'W', not 'M', if a fit_window is provided."
+        )
+    else:
+        assert False, "ValueError not raised for invalid fit frequency with fit window"

tests/test_validate.py

@@ -1,8 +1,8 @@
 import logging
 
 import pytest
-from pandas import DataFrame, DatetimeIndex, Series, Timestamp, to_datetime
-from spei.utils import validate_index, validate_series
+from pandas import DataFrame, DatetimeIndex, Index, Series, Timestamp, to_datetime
+from spei.utils import infer_frequency, validate_index, validate_series
 
 
 def test_validate_index(caplog) -> None:
@@ -50,3 +50,34 @@ def test_validate_series_df_2d() -> None:
     with pytest.raises(TypeError):
         df = DataFrame({"s1": [1, 2, 3], "s2": [1, 2, 3]}, index=to_datetime([1, 2, 3]))
         validate_series(df)
+
+
+def test_infer_frequency_monthly():
+    index = DatetimeIndex(["2020-01-01", "2020-02-01", "2020-03-01"])
+    assert infer_frequency(index) == "M"
+
+
+def test_infer_frequency_weekly():
+    index = DatetimeIndex(["2020-01-01", "2020-01-08", "2020-01-15"])
+    assert infer_frequency(index) == "W"
+
+
+def test_infer_frequency_daily():
+    index = DatetimeIndex(["2020-01-01", "2020-01-02", "2020-01-03"])
+    assert infer_frequency(index) == "D"
+
+
+def test_infer_frequency_no_infer():
+    index = DatetimeIndex(["2020-01-01", "2020-01-03", "2020-01-07"])
+    assert infer_frequency(index) == "ME"  # Assuming pandas version >= 2.2.0
+
+
+def test_infer_frequency_non_datetime_index():
+    index = Index(["2020-01-01", "2020-02-01", "2020-03-01"])
+    assert infer_frequency(index) == "M"
+
+
+def test_infer_frequency_invalid_index():
+    index = Index(["a", "b", "c"])
+    with pytest.raises(ValueError):
+        infer_frequency(index)