Merge branch 'develop' into develop

pandapower/plotting/collections.py

@@ -439,7 +439,7 @@ def create_line_collection(net, lines=None, line_geodata=None, bus_geodata=None,
             bus_geodata if bus_geodata is not None else net["bus_geodata"], "line")
     else:
         lines_with_geo = lines[np.isin(lines, line_geodata.index.values)]
-        coords = list(line_geodata[lines_with_geo])
+        coords = list(line_geodata.loc[lines_with_geo, 'coords'])
         lines_without_geo = set(lines) - set(lines_with_geo)
         if lines_without_geo:
             logger.warning("Could not plot lines %s. %s geodata is missing for those lines!"

pandapower/test/timeseries/test_timeseries_recycle.py

@@ -109,8 +109,9 @@ def _run_recycle(net):
     return vm_pu, ll
 
 
-def _run_normal(net):
+def _run_normal(net, time_steps=time_steps):
     ow = OutputWriter(net, output_path=tempfile.gettempdir(), output_file_type=".json")
+    ow.log_variable("res_bus", "va_degree")
     run_timeseries(net, time_steps, recycle=False)
     return ow
 

pandapower/timeseries/output_writer.py

@@ -120,6 +120,10 @@ def __repr__(self):
             s += "\n'" + str(table) + "." + str(variable) + "'"
         return s
 
+    def _monkey_patch(self, method, new):
+        from types import MethodType
+        setattr(self, method, MethodType(new, self))
+
     def _add_log_defaults(self):
         if self.log_variables is None:
             self.log_variables = list()

pandapower/timeseries/run_time_series.py

@@ -256,6 +256,8 @@ def init_time_series(net, time_steps, continue_on_divergence=False, verbose=True
     ts_variables["time_steps"] = time_steps
     # If True, a diverged power flow is ignored and the next step is calculated
     ts_variables["continue_on_divergence"] = continue_on_divergence
+    # print settings
+    ts_variables["verbose"] = verbose
 
     if logger.level is not 10 and verbose:
         # simple progress bar
@@ -266,7 +268,7 @@ def init_time_series(net, time_steps, continue_on_divergence=False, verbose=True
 
 def cleanup(ts_variables):
     if isinstance(ts_variables["recycle_options"], dict):
-        # Todo: delete internal variaables and dumped results which are not needed
+        # Todo: delete internal variables and dumped results which are not needed
         pass
 
 
@@ -285,6 +287,20 @@ def print_progress(i, time_step, time_steps, verbose, **kwargs):
         func = kwargs["progress_function"]
         func(i, time_step, time_steps, **kwargs)
 
+def run_loop(net, ts_variables, **kwargs):
+    """
+    runs the time series loop which calls pp.runpp (or another run function) in each iteration
+
+    Parameters
+    ----------
+    net - pandapower net
+    ts_variables - settings for time series
+
+    """
+    for i, time_step in enumerate(ts_variables["time_steps"]):
+        print_progress(i, time_step, ts_variables["time_steps"], ts_variables["verbose"], **kwargs)
+        run_time_step(net, time_step, ts_variables, **kwargs)
+
 
 def run_timeseries(net, time_steps=None, continue_on_divergence=False, verbose=True, **kwargs):
     """
@@ -311,9 +327,7 @@ def run_timeseries(net, time_steps=None, continue_on_divergence=False, verbose=T
     ts_variables = init_time_series(net, time_steps, continue_on_divergence, verbose, **kwargs)
 
     control_diagnostic(net)
-    for i, time_step in enumerate(ts_variables["time_steps"]):
-        print_progress(i, time_step, ts_variables["time_steps"], verbose, **kwargs)
-        run_time_step(net, time_step, ts_variables, **kwargs)
+    run_loop(net, ts_variables, **kwargs)
 
     # cleanup functions after the last time step was calculated
     cleanup(ts_variables)