Change coincident switch on

Move the code which determines whether the indexes are within or out of
the peak window outside the function, so it makes the argument of the
method more meaningful

ramp/core/core.py

@@ -693,8 +693,8 @@ def calc_indexes_for_rand_switch_on(self, switch_on, rand_time, max_free_spot, r
             indexes = np.arange(switch_on, switch_on + upper_limit)
         return indexes
 
-    def calc_coincident_switch_on(self, peak_time_range, indexes):
-        """Computes how many of the 'n' of the Appliance instance are switched on simultaneously
+    def calc_coincident_switch_on(self, inside_peak_window=True):
+        """Computes how many of the 'n' Appliance instance are switched on simultaneously
 
             Implement eqs. 3 and 4 of [1]
 
@@ -706,13 +706,13 @@ def calc_coincident_switch_on(self, peak_time_range, indexes):
         """
         s_peak, mu_peak, op_factor = switch_on_parameters()
 
-        # check if indexes are in peak window
-        if np.in1d(peak_time_range, indexes).any() is True and self.fixed == 'no':
+        # check if indexes are within peak window
+        if inside_peak_window is True and self.fixed == 'no':
             # calculates coincident behaviour within the peak time range
             # eq. 4 of [1]
             coincidence = min(self.number, max(1, math.ceil(random.gauss(mu=(self.number * mu_peak + 0.5), sigma=(s_peak * self.number * mu_peak)))))
         # check if indexes are off-peak
-        elif np.in1d(peak_time_range, indexes).any() is False and self.fixed == 'no':
+        elif inside_peak_window is False and self.fixed == 'no':
             # calculates probability of coincident switch_ons off-peak
             # eq. 3 of [1]
             prob = random.uniform(0, (self.number - op_factor) / self.number)
@@ -777,10 +777,10 @@ def generate_load_profile(self, rand_time, peak_time_range, rand_window_1, rand_
                 if tot_time > rand_time:
                     # the total functioning time is reached, a correction is applied to avoid overflow of indexes
                     indexes_adj = indexes[:-(tot_time - rand_time)]
+                    inside_peak_window = np.in1d(peak_time_range, indexes_adj).any()
                     # Computes how many of the 'n' of the Appliance instance are switched on simultaneously
                     coincidence = self.calc_coincident_switch_on(
-                        peak_time_range=peak_time_range,
-                        indexes=indexes_adj,
+                        inside_peak_window
                     )
                     # Update the daily use depending on existence of duty cycles of the Appliance instance
                     self.update_daily_use(
@@ -793,9 +793,9 @@ def generate_load_profile(self, rand_time, peak_time_range, rand_window_1, rand_
                 else:
                     # the total functioning time has not yet exceeded the rand_time
                     # Computes how many of the 'n' of the Appliance instance are switched on simultaneously
+                    inside_peak_window = np.in1d(peak_time_range, indexes).any()
                     coincidence = self.calc_coincident_switch_on(
-                        peak_time_range=peak_time_range,
-                        indexes=indexes,
+                        inside_peak_window
                     )
                     # Update the daily use depending on existence of duty cycles of the Appliance instance
                     self.update_daily_use(