From 080d56f8eabf11f0214be738aa5c57e7a33ebddf Mon Sep 17 00:00:00 2001
From: thouska <tobias.houska@umwelt.uni-giessen.de>
Date: Mon, 27 Feb 2023 16:01:24 +0100
Subject: [PATCH] WIP fix black style and testing issues in analyser

---
 src/spotpy/analyser.py                    | 15 +++------------
 src/spotpy/examples/hymod_python/hymod.py |  8 ++++----
 tests/test_analyser.py                    |  4 ++--
 tutorials/tutorial_sceua_hymod.py         |  2 --
 4 files changed, 9 insertions(+), 20 deletions(-)

diff --git a/src/spotpy/analyser.py b/src/spotpy/analyser.py
index 173871d9..fa23811c 100644
--- a/src/spotpy/analyser.py
+++ b/src/spotpy/analyser.py
@@ -176,10 +176,7 @@ def get_maxlikeindex(results, like_index=1, verbose=True):
         value and value of the maximum objectivefunction of your result array
     :rtype: int and float
     """
-    try:
-        likes = results["like"]
-    except ValueError:
-        likes = results["like" + str(like_index)]
+    likes = results["like" + str(like_index)]
     maximum = np.nanmax(likes)
     value = str(round(maximum, 4))
     text = str("Run number ")
@@ -202,10 +199,7 @@ def get_minlikeindex(results, like_index=1, verbose=True):
         value and value of the minimum objectivefunction of your result array
     :rtype: int and float
     """
-    try:
-        likes = results["like"]
-    except ValueError:
-        likes = results["like" + str(like_index)]
+    likes = results["like" + str(like_index)]
     minimum = np.nanmin(likes)
     value = str(round(minimum, 4))
     text = str("Run number ")
@@ -401,10 +395,7 @@ def get_best_parameterset(results, like_index=1, maximize=True):
     :return: Best parameter set
     :rtype: array
     """
-    try:
-        likes = results["like"]
-    except ValueError:
-        likes = results["like" + str(like_index)]
+    likes = results["like" + str(like_index)]
     if maximize:
         best = np.nanmax(likes)
     else:
diff --git a/src/spotpy/examples/hymod_python/hymod.py b/src/spotpy/examples/hymod_python/hymod.py
index dc4d0af1..d6dab063 100644
--- a/src/spotpy/examples/hymod_python/hymod.py
+++ b/src/spotpy/examples/hymod_python/hymod.py
@@ -11,7 +11,7 @@
 PLoS ONE, 10(12), e0145180, doi:10.1371/journal.pone.0145180, 2015.
 """
 
-from numba import jit
+#from numba import jit
 
 
 def hymod(Precip, PET, cmax, bexp, alpha, Rs, Rq):
@@ -69,13 +69,13 @@ def hymod(Precip, PET, cmax, bexp, alpha, Rs, Rq):
     return output
 
 
-@jit
+#@jit
 def power(X, Y):
     X = abs(X)  # Needed to capture invalid overflow with netgative values
     return X**Y
 
 
-@jit
+#@jit
 def linres(x_slow, inflow, Rs):
     # Linear reservoir
     x_slow = (1 - Rs) * x_slow + (1 - Rs) * inflow
@@ -83,7 +83,7 @@ def linres(x_slow, inflow, Rs):
     return x_slow, outflow
 
 
-@jit
+#@jit
 def excess(x_loss, cmax, bexp, Pval, PETval):
     # this function calculates excess precipitation and evaporation
     xn_prev = x_loss
diff --git a/tests/test_analyser.py b/tests/test_analyser.py
index fec141cd..35d72873 100644
--- a/tests/test_analyser.py
+++ b/tests/test_analyser.py
@@ -95,10 +95,10 @@ def test_calc_like(self):
 
     def test_get_best_parameterset(self):
         get_best_parameterset_true = spotpy.analyser.get_best_parameterset(
-            self.results, True
+            self.results, maximize=True
         )
         get_best_parameterset_false = spotpy.analyser.get_best_parameterset(
-            self.results, False
+            self.results, maximize=False
         )
         self.assertEqual(len(get_best_parameterset_true[0]), 3)
         self.assertEqual(type(get_best_parameterset_true[0]), np.void)
diff --git a/tutorials/tutorial_sceua_hymod.py b/tutorials/tutorial_sceua_hymod.py
index 73e89386..18072f32 100644
--- a/tutorials/tutorial_sceua_hymod.py
+++ b/tutorials/tutorial_sceua_hymod.py
@@ -9,14 +9,12 @@
 """
 
 import matplotlib.pyplot as plt
-import numpy as np
 
 import spotpy
 from spotpy.examples.spot_setup_hymod_python import spot_setup
 
 if __name__ == "__main__":
     parallel = "seq"  # Runs everthing in sequential mode
-    np.random.seed(2000)  # Makes the results reproduceable
 
     # Initialize the Hymod example
     # In this case, we tell the setup which algorithm we want to use, so