Sourcery refactored main branch

src/hydrotoolbox/baseflow/comparison.py

@@ -29,10 +29,7 @@ def strict_baseflow(Q):
     # 4. Flow data followed by a data point with a larger value of -dQ/dt.
     wet4 = np.concatenate([[True], dQdt[1:] - dQdt[:-1] < 0, [True, True]])
 
-    # Dry points, namely strict baseflow.
-    dry = ~(wet1 + wet2 + wet3 + wet4)
-
-    return dry
+    return ~(wet1 + wet2 + wet3 + wet4)
 
 
 def KGE(simulations, evaluation):
@@ -73,7 +70,4 @@ def KGE(simulations, evaluation):
     beta = np.sum(simulations, axis=0, dtype=np.float64) / (
         np.sum(evaluation, axis=0, dtype=np.float64) + 1e-10
     )
-    # calculate the Kling-Gupta Efficiency KGE
-    kge_ = 1 - np.sqrt((r - 1) ** 2 + (alpha - 1) ** 2 + (beta - 1) ** 2)
-
-    return kge_
+    return 1 - np.sqrt((r - 1) ** 2 + (alpha - 1) ** 2 + (beta - 1) ** 2)

src/hydrotoolbox/baseflow/methods/CM.py

@@ -9,10 +9,7 @@ def CM(Q, b_LH, a, return_exceed=False):
         Q (np.array): streamflow
         a (float): recession coefficient
     """
-    if return_exceed:
-        b = np.zeros(Q.shape[0] + 1)
-    else:
-        b = np.zeros(Q.shape[0])
+    b = np.zeros(Q.shape[0] + 1) if return_exceed else np.zeros(Q.shape[0])
     b[0] = b_LH[0]
     for i in range(Q.shape[0] - 1):
         b[i + 1] = a / (2 - a) * b[i] + (1 - a) / (2 - a) * Q[i + 1]

src/hydrotoolbox/baseflow/methods/EWMA.py

@@ -9,10 +9,7 @@ def EWMA(Q, b_LH, e, return_exceed=False):
         Q (np.array): streamflow
         e (float): smoothing parameter
     """
-    if return_exceed:
-        b = np.zeros(Q.shape[0] + 1)
-    else:
-        b = np.zeros(Q.shape[0])
+    b = np.zeros(Q.shape[0] + 1) if return_exceed else np.zeros(Q.shape[0])
     b[0] = b_LH[0]
     for i in range(Q.shape[0] - 1):
         b[i + 1] = (1 - e) * b[i] + e * Q[i + 1]

src/hydrotoolbox/baseflow/methods/Eckhardt.py

@@ -10,10 +10,7 @@ def Eckhardt(Q, b_LH, a, BFImax, return_exceed=False):
         a (float): recession coefficient
         BFImax (float): maximum value of baseflow index (BFI)
     """
-    if return_exceed:
-        b = np.zeros(Q.shape[0] + 1)
-    else:
-        b = np.zeros(Q.shape[0])
+    b = np.zeros(Q.shape[0] + 1) if return_exceed else np.zeros(Q.shape[0])
     b[0] = b_LH[0]
     for i in range(Q.shape[0] - 1):
         b[i + 1] = ((1 - BFImax) * a * b[i] + (1 - a) * BFImax * Q[i + 1]) / (

src/hydrotoolbox/baseflow/methods/Furey.py

@@ -10,10 +10,7 @@ def Furey(Q, b_LH, a, A, return_exceed=False):
         a (float): recession coefficient
         A (float): calibrated in baseflow.param_estimate
     """
-    if return_exceed:
-        b = np.zeros(Q.shape[0] + 1)
-    else:
-        b = np.zeros(Q.shape[0])
+    b = np.zeros(Q.shape[0] + 1) if return_exceed else np.zeros(Q.shape[0])
     b[0] = b_LH[0]
     for i in range(Q.shape[0] - 1):
         b[i + 1] = (a - A * (1 - a)) * b[i] + A * (1 - a) * Q[i]

src/hydrotoolbox/baseflow/methods/LH.py

@@ -9,11 +9,7 @@ def LH(Q, beta=0.925, return_exceed=False):
         Q (np.array): streamflow
         beta (float): filter parameter, 0.925 recommended by (Nathan & McMahon, 1990)
     """
-    if return_exceed:
-        b = np.zeros(Q.shape[0] + 1)
-    else:
-        b = np.zeros(Q.shape[0])
-
+    b = np.zeros(Q.shape[0] + 1) if return_exceed else np.zeros(Q.shape[0])
     # first pass
     b[0] = Q[0] / 2
     for i in range(Q.shape[0] - 1):

src/hydrotoolbox/baseflow/methods/Local.py

@@ -24,10 +24,7 @@ def hysep_interval(area):
     # and A is the drainage area in square miles (Linsley and others, 1982, p. 210).
     # The interval 2N* used for hydrograph separations is the odd integer between
     # 3 and 11 nearest to 2N (Pettyjohn and Henning, 1979, p. 31).
-    if area is None:
-        N = 5
-    else:
-        N = np.power(0.3861022 * area, 0.2)
+    N = 5 if area is None else np.power(0.3861022 * area, 0.2)
     inN = np.ceil(2 * N)
     if np.mod(inN, 2) == 0:
         inN = np.ceil(2 * N) - 1

src/hydrotoolbox/baseflow/methods/UKIH.py

@@ -30,11 +30,7 @@ def UKIH_turn(Q, idx_min):
 
 
 def linear_interpolation(Q, idx_turn, return_exceed=False):
-    if return_exceed:
-        b = np.zeros(Q.shape[0] + 1)
-    else:
-        b = np.zeros(Q.shape[0])
-
+    b = np.zeros(Q.shape[0] + 1) if return_exceed else np.zeros(Q.shape[0])
     n = 0
     for i in range(idx_turn[0], idx_turn[-1] + 1):
         if i == idx_turn[n + 1]:

src/hydrotoolbox/baseflow/methods/Willems.py

@@ -11,10 +11,7 @@ def Willems(Q, b_LH, a, w, return_exceed=False):
         w (float): case-specific average proportion of the quick flow
                    in the streamflow, calibrated in baseflow.param_estimate
     """
-    if return_exceed:
-        b = np.zeros(Q.shape[0] + 1)
-    else:
-        b = np.zeros(Q.shape[0])
+    b = np.zeros(Q.shape[0] + 1) if return_exceed else np.zeros(Q.shape[0])
     b[0] = b_LH[0]
     v = (1 - w) * (1 - a) / (2 * w)
     for i in range(Q.shape[0] - 1):

src/hydrotoolbox/baseflow/param_estimate.py

@@ -73,15 +73,13 @@ def maxmium_BFI(Q, b_LH, a, date=None):
         idx_end = b.shape[0] // 365 * 365
         annual_b = np.mean(b[:idx_end].reshape(-1, 365), axis=1)
         annual_Q = np.mean(Q[:idx_end].reshape(-1, 365), axis=1)
-        annual_BFI = annual_b / annual_Q
     else:
         idx_year = date.Y - date.Y.min()
         counts = np.bincount(idx_year)
         idx_valid = counts > 0
         annual_b = np.bincount(idx_year, weights=b)[idx_valid] / counts[idx_valid]
         annual_Q = np.bincount(idx_year, weights=Q)[idx_valid] / counts[idx_valid]
-        annual_BFI = annual_b / annual_Q
-
+    annual_BFI = annual_b / annual_Q
     BFI_max = np.max(annual_BFI)
     BFI_max = BFI_max if BFI_max < 0.9 else np.sum(annual_b) / np.sum(annual_Q)
     return BFI_max

src/hydrotoolbox/baseflow/utils.py

@@ -50,11 +50,7 @@ def clean_streamflow(date, Q):
 
 
 def clean_streamflow_jit(year, year_unique, Q):
-    year_delete = []
-    for y in year_unique:
-        if (Q[year == y] >= 0).sum() < 120:
-            year_delete.append(y)
-    return year_delete
+    return [y for y in year_unique if (Q[year == y] >= 0).sum() < 120]
 
 
 def moving_average(x, w):

src/hydrotoolbox/hydrotoolbox.py

@@ -1402,10 +1402,7 @@ def indices(
             lu[(sclass, None)] = lu[(sclass, None)].union(lu[(sclass, fcomp)])
             lu[(None, fcomp)] = lu[(None, fcomp)].union(lu[(sclass, fcomp)])
 
-    hi = {}
-    for icode in indice_codes:
-        hi[icode] = getattr(indice_class, icode)()
-    return hi
+    return {icode: getattr(indice_class, icode)() for icode in indice_codes}
 
 
 @program.command()

src/hydrotoolbox/indices/indices.py

@@ -7,15 +7,14 @@
 
 class Indices:
     def __init__(self, data, use_median=False, water_year="A-SEP"):
-        if isinstance(data, pd.DataFrame):
-            if len(data.columns) != 1:
-                raise ValueError(
-                    tsutils.error_wrapper(
-                        f"""
+        if isinstance(data, pd.DataFrame) and len(data.columns) != 1:
+            raise ValueError(
+                tsutils.error_wrapper(
+                    f"""
 Can only calculate indices on 1 series, you gave {len(data.columns)}.
                                                        """
-                    )
                 )
+            )
 
         self.use_median = use_median
         self.water_year = water_year
@@ -384,9 +383,9 @@ def _lf(self, thresh, than):
                 if eval(f"value {than} thresh"):
                     pdur = pdur + 1
                     flag = flag + 1
-                    allnp[group_name] = allnp[group_name] + 1
+                    allnp[group_name] += 1
                     if flag == 1:
-                        nnp[group_name] = nnp[group_name] + 1
+                        nnp[group_name] += 1
                 else:
                     flag = 0
             if nnp[group_name] > 0:
@@ -571,9 +570,7 @@ def DL18(self):
             self.data[self.data == 0].groupby(pd.Grouper(freq=self.water_year)).count()
         )
         if any(stat):
-            if self.use_median is True:
-                return stat.median()
-            return stat.mean()
+            return stat.median() if self.use_median is True else stat.mean()
         return 0
 
     def DL19(self):
@@ -583,15 +580,11 @@ def DL19(self):
         return stat.std() / stat.mean() * 100
 
     def DL20(self):
-        stat = self.data_monthly_mean[self.data_monthly_mean == 0].count()
-        return stat
+        return self.data_monthly_mean[self.data_monthly_mean == 0].count()
 
     def DH1(self):
         stat = self.data_yearly.max()
-        if self.use_median:
-            stat = stat.median()
-        else:
-            stat = stat.mean()
+        stat = stat.median() if self.use_median else stat.mean()
         return stat
 
     def DH2(self):
@@ -701,18 +694,19 @@ def _pre_ta1_ta2(self):
 
         lq[self.data == 0.0] = np.log10(0.01)
 
-        table = []
-        table.append(lq[lq < 0.1 * lma1])
-        table.append(lq[(lq >= 0.1 * lma1) & (lq < 0.25 * lma1)])
-        table.append(lq[(lq >= 0.25 * lma1) & (lq < 0.5 * lma1)])
-        table.append(lq[(lq >= 0.5 * lma1) & (lq < 0.75 * lma1)])
-        table.append(lq[(lq >= 0.75 * lma1) & (lq < lma1)])
-        table.append(lq[(lq >= lma1) & (lq < 1.25 * lma1)])
-        table.append(lq[(lq >= 1.25 * lma1) & (lq < 1.5 * lma1)])
-        table.append(lq[(lq >= 1.5 * lma1) & (lq < 1.75 * lma1)])
-        table.append(lq[(lq >= 1.75 * lma1) & (lq < 2.0 * lma1)])
-        table.append(lq[(lq >= 2.0 * lma1) & (lq < 2.25 * lma1)])
-        table.append(lq[(lq >= 2.25 * lma1)])
+        table = [
+            lq[lq < 0.1 * lma1],
+            lq[(lq >= 0.1 * lma1) & (lq < 0.25 * lma1)],
+            lq[(lq >= 0.25 * lma1) & (lq < 0.5 * lma1)],
+            lq[(lq >= 0.5 * lma1) & (lq < 0.75 * lma1)],
+            lq[(lq >= 0.75 * lma1) & (lq < lma1)],
+            lq[(lq >= lma1) & (lq < 1.25 * lma1)],
+            lq[(lq >= 1.25 * lma1) & (lq < 1.5 * lma1)],
+            lq[(lq >= 1.5 * lma1) & (lq < 1.75 * lma1)],
+            lq[(lq >= 1.75 * lma1) & (lq < 2.0 * lma1)],
+            lq[(lq >= 2.0 * lma1) & (lq < 2.25 * lma1)],
+            lq[lq >= 2.25 * lma1],
+        ]
 
         ndf = pd.DataFrame()
         for indx, df in enumerate(table):
@@ -727,9 +721,7 @@ def _pre_ta1_ta2(self):
                     axis="columns",
                 )
                 continue
-            ldf = []
-            for day in range(1, 366):
-                ldf.append(df[df.index.dayofyear == day].count())
+            ldf = [df[df.index.dayofyear == day].count() for day in range(1, 366)]
             ndf = pd.concat(
                 [ndf, pd.DataFrame(data=ldf, index=range(1, 366), columns=[indx])],
                 axis="columns",