fix tests

doc/examples/example04_package_comparison.ipynb

@@ -28,7 +28,9 @@
     "import pastas as ps\n",
     "import pandas as pd\n",
     "import scipy.stats as scs\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "print(si.show_versions())"
    ]
   },
   {

src/spei/__init__.py

@@ -1,4 +1,4 @@
 # flake8: noqa
 from . import climdex, dist, plot, si, utils
 from ._version import __version__, show_versions
-from .si import sgi, spei, spi, ssfi, SI
+from .si import SI, sgi, spei, spi, ssfi

src/spei/dist.py

@@ -72,7 +72,9 @@ def __post_init__(self):
             self.p0 = (data_fit == 0.0).sum() / len(data_fit)
 
     @staticmethod
-    def fit_dist(data: Series, dist: ContinuousDist) -> Tuple[Optional[List[float]], float, float]:
+    def fit_dist(
+        data: Series, dist: ContinuousDist
+    ) -> Tuple[Optional[List[float]], float, float]:
         """
         Fits a Scipy continuous distribution to the data.
 

src/spei/si.py

@@ -312,7 +312,9 @@ def fit_distribution(self):
             dfval_window = daily_window_group_yearly_df(
                 dfval=self._grouped_year, period=period
             )
-            for dfval_rwindow in dfval_window.rolling(window=window, min_periods=window, closed="right"):
+            for dfval_rwindow in dfval_window.rolling(
+                window=window, min_periods=window, closed="right"
+            ):
                 if len(dfval_rwindow) < window:
                     continue  # min_periods ignored by Rolling.__iter__
                 date = dfval_rwindow.index[period]
@@ -327,7 +329,9 @@ def fit_distribution(self):
                 self._dist_dict[date] = fd
         else:
             logging.info("Using groupby fit by frequency method")
-            for date, grval in self._grouped_year.groupby(Grouper(freq=self.fit_freq)):
+            for date, grval in self._grouped_year.groupby(
+                Grouper(freq=str(self.fit_freq))
+            ):
                 data = get_data_series(grval)
                 fd = Dist(
                     data=data,
@@ -370,7 +374,7 @@ def cdf_nsf(self) -> Series:
         """
         logging.info("Using the normal scores transform")
         cdf = Series(nan, index=self.series.index, dtype=float)
-        for _, grval in self._grouped_year.groupby(Grouper(freq=self.fit_freq)):
+        for _, grval in self._grouped_year.groupby(Grouper(freq=str(self.fit_freq))):
             data = get_data_series(grval).sort_values()
             n = len(data)
             cdf.loc[data.index] = linspace(1 / (2 * n), 1 - 1 / (2 * n), n)
@@ -397,5 +401,5 @@ def get_dist(self, date: Timestamp) -> Dist:
             dist = self._dist_dict[k]
             if date in dist.data.index:
                 return dist
-            else:
-                raise KeyError("Date not found in distributions")
+
+        raise KeyError("Date not found in distributions")

src/spei/utils.py

@@ -119,10 +119,10 @@ def daily_window_group_yearly_df(dfval: DataFrame, period: int) -> DataFrame:
     """
     dfval_window_index_start = [
         dfval.index[0] + Timedelta(value=-i, unit="D")
-        for i in reversed(range(1, period+1))
+        for i in reversed(range(1, period + 1))
     ]
     dfval_window_index_end = [
-        dfval.index[-1] + Timedelta(value=i, unit="D") for i in range(1, period+1)
+        dfval.index[-1] + Timedelta(value=i, unit="D") for i in range(1, period + 1)
     ]
     dfval_window_index = DatetimeIndex(
         dfval_window_index_start + dfval.index.to_list() + dfval_window_index_end
@@ -132,6 +132,6 @@ def daily_window_group_yearly_df(dfval: DataFrame, period: int) -> DataFrame:
         nan, index=dfval_window_index, columns=dfval.columns, dtype=float
     )
     dfval_window.loc[dfval.index, dfval.columns] = dfval.values
-    dfval_window.iloc[: period] = dfval.iloc[-period :].values
-    dfval_window.iloc[-period :] = dfval.iloc[: period].values
+    dfval_window.iloc[:period] = dfval.iloc[-period:].values
+    dfval_window.iloc[-period:] = dfval.iloc[:period].values
     return dfval_window