Prepare some more for NumPy 2.0

.github/workflows/full_test.yml

@@ -15,7 +15,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10","3.11"]      
+        python-version: ["3.9", "3.10","3.11"]      
 
     steps:
     - uses: actions/checkout@v3

modelskill/comparison/_utils.py

@@ -22,8 +22,8 @@ def _add_spatial_grid_to_df(
             bins_y = bins
     else:
         # bins from binsize
-        x_ptp = df.x.values.ptp()  # type: ignore
-        y_ptp = df.y.values.ptp()  # type: ignore
+        x_ptp = np.ptp(df.x.values)  # type: ignore
+        y_ptp = np.ptp(df.y.values)  # type: ignore
         nx = int(np.ceil(x_ptp / binsize))
         ny = int(np.ceil(y_ptp / binsize))
         x_mean = np.round(df.x.mean())

modelskill/metrics.py

@@ -36,33 +36,33 @@
 >>> obs = np.array([0.3, 2.1, -1.0])
 >>> mod = np.array([0.0, 2.3, 1.0])
 >>> bias(obs, mod)
-0.6333333333333332
+np.float64(0.6333333333333332)
 >>> max_error(obs, mod)
-2.0
+np.float64(2.0)
 >>> rmse(obs, mod)
-1.173314393786536
+np.float64(1.173314393786536)
 >>> urmse(obs, mod)
-0.9877021593352702
+np.float64(0.9877021593352702)
 >>> mae(obs, mod)
-0.8333333333333331
+np.float64(0.8333333333333331)
 >>> mape(obs, mod)
-103.17460317460316
+np.float64(103.17460317460316)
 >>> nse(obs, mod)
-0.14786795048143053
+np.float64(0.14786795048143053)
 >>> r2(obs, mod)
-0.14786795048143053
+np.float64(0.14786795048143053)
 >>> mef(obs, mod)
-0.9231099877688299
+np.float64(0.9231099877688299)
 >>> si(obs, mod)
-0.8715019052958266
+np.float64(0.8715019052958266)
 >>> spearmanr(obs, mod)
-0.5
+np.float64(0.5)
 >>> willmott(obs, mod)
-0.7484604452865941
+np.float64(0.7484604452865941)
 >>> hit_ratio(obs, mod, a=0.5)
-0.6666666666666666
+np.float64(0.6666666666666666)
 >>> ev(obs, mod)
-0.39614855570839064
+np.float64(0.39614855570839064)
 """
 
 from __future__ import annotations
@@ -332,7 +332,7 @@ def r2(obs: np.ndarray, model: np.ndarray) -> float:
     >>> obs = np.array([1.0,1.1,1.2,1.3,1.4])
     >>> model = np.array([1.09, 1.16, 1.3 , 1.38, 1.49])
     >>> r2(obs,model)
-    0.6379999999999998
+    np.float64(0.6379999999999998)
     """
     assert obs.size == model.size
     if len(obs) == 0:
@@ -429,9 +429,9 @@ def spearmanr(obs: np.ndarray, model: np.ndarray) -> float:
     >>> obs = np.linspace(-20, 20, 100)
     >>> mod = np.tanh(obs)
     >>> rho(obs, mod)
-    0.9999759973116955
+    np.float64(0.9999759973116955)
     >>> spearmanr(obs, mod)
-    0.9999759973116955
+    np.float64(0.9999759973116955)
 
     See Also
     --------
@@ -533,7 +533,7 @@ def pr(
     model: np.ndarray,
     inter_event_level: float = 0.7,
     AAP: Union[int, float] = 2,
-    inter_event_time: str ="36h",
+    inter_event_time: str = "36h",
 ) -> float:
     """alias for peak_ratio"""
     assert obs.size == model.size
@@ -544,8 +544,8 @@ def peak_ratio(
     obs: pd.Series,
     model: np.ndarray,
     inter_event_level: float = 0.7,
-    AAP: Union[int, float]  = 2,
-    inter_event_time: str="36h",
+    AAP: Union[int, float] = 2,
+    inter_event_time: str = "36h",
 ) -> float:
     r"""Peak Ratio
 
@@ -578,24 +578,24 @@ def peak_ratio(
     # Calculate number of years
     dt_int = time[1:].values - time[0:-1].values
     dt_int_mode = float(stats.mode(dt_int, keepdims=False)[0]) / 1e9  # in seconds
-    N_years = dt_int_mode / 24 / 3600 / 365.25 * len(time)  
+    N_years = dt_int_mode / 24 / 3600 / 365.25 * len(time)
     peak_index, AAP_ = _partial_duration_series(
-            time,
-            obs,
-            inter_event_level=inter_event_level,
-            AAP=AAP,
-            inter_event_time=inter_event_time,
-        )
+        time,
+        obs,
+        inter_event_level=inter_event_level,
+        AAP=AAP,
+        inter_event_time=inter_event_time,
+    )
     peaks = obs[peak_index]
     found_peaks_obs = peaks.sort_values(ascending=False)
 
     peak_index, _ = _partial_duration_series(
-            time,
-            model,
-            inter_event_level=inter_event_level,
-            AAP=AAP,
-            inter_event_time=inter_event_time,
-        )
+        time,
+        model,
+        inter_event_level=inter_event_level,
+        AAP=AAP,
+        inter_event_time=inter_event_time,
+    )
     peaks = model[peak_index]
     found_peaks_mod = peaks.sort_values(ascending=False)
 
@@ -650,7 +650,7 @@ def willmott(obs: np.ndarray, model: np.ndarray) -> float:
     >>> obs = np.array([1.0, 1.1, 1.2, 1.3, 1.4, 1.4, 1.3])
     >>> model = np.array([1.02, 1.16, 1.3, 1.38, 1.49, 1.45, 1.32])
     >>> willmott(obs, model)
-    0.9501403174479723
+    np.float64(0.9501403174479723)
 
     References
     ----------
@@ -682,11 +682,11 @@ def hit_ratio(obs: np.ndarray, model: np.ndarray, a=0.1) -> float:
     >>> obs = np.array([1.0, 1.1, 1.2, 1.3, 1.4, 1.4, 1.3])
     >>> model = np.array([1.02, 1.16, 1.3, 1.38, 1.49, 1.45, 1.32])
     >>> hit_ratio(obs, model, a=0.05)
-    0.2857142857142857
+    np.float64(0.2857142857142857)
     >>> hit_ratio(obs, model, a=0.1)
-    0.8571428571428571
+    np.float64(0.8571428571428571)
     >>> hit_ratio(obs, model, a=0.15)
-    1.0
+    np.float64(1.0)
     """
     assert obs.size == model.size
 
@@ -916,7 +916,7 @@ def c_bias(obs: np.ndarray, model: np.ndarray) -> float:
     >>> obs = np.array([10., 355., 170.])
     >>> mod = np.array([20., 5., -180.])
     >>> c_bias(obs, mod)
-    10.0
+    np.float64(10.0)
     """
     from scipy.stats import circmean
 
@@ -946,7 +946,7 @@ def c_max_error(obs: np.ndarray, model: np.ndarray) -> float:
     >>> obs = np.array([10., 350., 10.])
     >>> mod = np.array([20., 10., 350.])
     >>> c_max_error(obs, mod)
-    20.0
+    np.float64(20.0)
     """
 
     resi = _c_residual(obs, model)