Merge pull request #395 from rte-france/dev_1.8.1

Update to version 1.8.1

CHANGELOG.rst

@@ -31,8 +31,16 @@ Change Log
 - [???] "asynch" multienv
 - [???] properly model interconnecting powerlines
 
-[1.8.0] - 2022-12-yy
---------------------
+[1.8.1] - 2023-01-11
+---------------------
+- [FIXED] a deprecation with numpy>= 1.24 (**eg** np.bool and np.str)
+- [ADDED] the baseAgent class now has two new template methods `save_state` and `load_state` to save and
+  load the agent's state during Grid2op simulations. Examples can be found in L2RPN baselines (PandapowerOPFAgent and curriculumagent).
+- [IMPROVED] error message in pandapower backend when the grid do not converge due to disconnected
+   generators or loads.
+
+[1.8.0] - 2022-12-12
+---------------------
 - [BREAKING] now requires numpy >= 1.20 to work (otherwise there are 
   issues with newer versions of pandas).
 - [BREAKING] issue https://github.com/rte-france/Grid2Op/issues/379 requires

grid2op/Agent/baseAgent.py

@@ -7,7 +7,7 @@
 # This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems.
 
 import copy
-
+import os
 from abc import ABC, abstractmethod
 from grid2op.Space import RandomObject
 
@@ -111,3 +111,62 @@ def act(self, observation, reward, done=False):
 
         """
         pass
+
+    def save_state(self, savestate_path :os.PathLike):  
+        """
+        An optional method to save the internal state of your agent.
+        The saved state can later be re-loaded with `self.load_state`, e.g. to repeat 
+        a Grid2Op time step with exactly the same internal parameterization. This
+        can be useful to repeat Grid2Op experiments and analyze why your agent performed 
+        certain actions in past time steps. Concept developed by Fraunhofer IEE KES.
+
+        Notes
+        -----
+        First, the internal state your agent consists of attributes that are contained in 
+        the :class:`grid2op.Agent.BaseAgent` and :class:`grid2op.Agent.BaseAgent.action_space`.
+        Examples are the parameterization and seeds of the random number generator that your
+        agent uses. Such attributes can easily be obtained with the :func:`getattr` and stored
+        in a common file format, such as `.npy`.
+        
+        Second, your agent may contain custom attributes, such as e.g. a vector of line indices 
+        from a Grid2Op observation. You could obtain and save them in the same way as explained 
+        before.
+
+        Third, your agent may contain very specific modules such as `Tensorflow` that
+        do not support the simple :func:`getattr`. However, these modules normally have
+        their own methods to save an internal state. Examples of such methods are
+        :func:`save_weights` that you can integrate in your implementation of `self.save_state`.
+        
+        Parameters
+        ----------
+        savestate_path: ``string``
+            The path to which your agent state variables should be saved
+        """
+        pass
+
+    def load_state(self, loadstate_path :os.PathLike):  
+        """
+        An optional method to re-load the internal agent state that was saved with `self.save_state`. 
+        This can be useful to re-set your agent to an earlier simulation time step and reproduce 
+        past experiments with Grid2Op. Concept developed by Fraunhofer IEE KES.
+
+        Notes
+        -----
+        First, the internal state your agent consists of attributes that are contained in 
+        the :class:`grid2op.Agent.BaseAgent` and :class:`grid2op.Agent.BaseAgent.action_space`.
+        Such attributes can easily be re-set with :func:`setattr`.
+        
+        Second, your agent may contain custom attributes, such as e.g. a vector of line indices 
+        from a Grid2Op observation. You can re-set them with :func:`setattr` as well.
+
+        Third, your agent may contain very specific modules such as `Tensorflow` that
+        do not support the simple :func:`setattr`. However, these modules normally have
+        their own methods to re-load an internal state. Examples of such methods are
+        :func:`load_weights` that you can integrate in your implementation of `self.load_state`.
+        
+        Parameters
+        ----------
+        savestate_path: ``string``
+            The path from which your agent state variables should be loaded
+        """
+        pass

grid2op/Backend/PandaPowerBackend.py

@@ -1003,10 +1003,18 @@ def runpf(self, is_dc=False):
 
                 if np.any(~self._grid.load["in_service"]):
                     # TODO see if there is a better way here -> do not handle this here, but rather in Backend._next_grid_state
-                    raise pp.powerflow.LoadflowNotConverged("Isolated load")
+                    raise pp.powerflow.LoadflowNotConverged("Disconnected load: for now grid2op cannot handle properly"
+                                                            " disconnected load. If you want to disconnect one, say it"
+                                                            " consumes 0. instead. Please check loads: "
+                                                            f"{np.where(~self._grid.load['in_service'])[0]}"
+                                                            )
                 if np.any(~self._grid.gen["in_service"]):
                     # TODO see if there is a better way here -> do not handle this here, but rather in Backend._next_grid_state
-                    raise pp.powerflow.LoadflowNotConverged("Isolated gen")
+                    raise pp.powerflow.LoadflowNotConverged("Disconnected gen: for now grid2op cannot handle properly"
+                                                            " disconnected generators. If you want to disconnect one, say it"
+                                                            " produces 0. instead. Please check generators: "
+                                                            f"{np.where(~self._grid.gen['in_service'])[0]}"
+                                                            )
 
                 if is_dc:
                     pp.rundcpp(self._grid, check_connectivity=False)
@@ -1031,7 +1039,7 @@ def runpf(self, is_dc=False):
                 if self._grid.res_gen.isnull().values.any():
                     # TODO see if there is a better way here -> do not handle this here, but rather in Backend._next_grid_state
                     # sometimes pandapower does not detect divergence and put Nan.
-                    raise pp.powerflow.LoadflowNotConverged("Isolated gen")
+                    raise pp.powerflow.LoadflowNotConverged("Divergence due to Nan values in res_gen table.")
 
                 (
                     self.prod_p[:],
@@ -1337,7 +1345,7 @@ def get_topo_vect(self):
         return self._topo_vect
 
     def _get_topo_vect(self):
-        res = np.full(self.dim_topo, fill_value=np.NaN, dtype=dt_int)
+        res = np.full(self.dim_topo, fill_value=np.iinfo(dt_int).max, dtype=dt_int)
 
         line_status = self.get_line_status()
 

grid2op/Chronics/Settings_L2RPN2019.py

@@ -264,11 +264,11 @@ def from_vect(self, vect):
         next_ = self.n_line
 
         self._switch_line_status = vect[prev_:next_]
-        self._switch_line_status = self._switch_line_status.astype(np.bool)
+        self._switch_line_status = self._switch_line_status.astype(np.bool_)
         prev_ = next_
         next_ += self.dim_topo
         self._change_bus_vect = vect[prev_:next_]
-        self._change_bus_vect = self._change_bus_vect.astype(np.bool)
+        self._change_bus_vect = self._change_bus_vect.astype(np.bool_)
 
         # self.disambiguate_reconnection()
 

grid2op/Space/GridObjects.py

@@ -2264,7 +2264,7 @@ def _check_validity_shunt_data(cls):
         if not isinstance(cls.name_shunt, np.ndarray):
             try:
                 cls.name_shunt = np.array(cls.name_shunt)
-                cls.name_shunt = cls.name_shunt.astype(np.str)
+                cls.name_shunt = cls.name_shunt.astype(np.str_)
             except Exception as exc:
                 raise EnvError(
                     'name_shunt should be convertible to a numpy array with dtype "str".'

grid2op/dtypes.py

@@ -13,4 +13,5 @@
 dt_float = (
     np.float32
 )  # dtype('float64') or dtype('float32') depending on platform  => i force it to float32
-dt_bool = np.bool
+
+dt_bool = np.bool_  # mandatory for numpy >= 1.24

setup.cfg

@@ -6,11 +6,11 @@ keywords = ML powergrid optmization RL power-systems
 license = Mozilla Public License 2.0 (MPL 2.0)
 classifiers = 
     Development Status :: 4 - Beta
-    Programming Language :: Python :: 3.6
     Programming Language :: Python :: 3.7
     Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
     Programming Language :: Python :: 3.10
+    Programming Language :: Python :: 3.11
     License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
     Intended Audience :: Developers
     Intended Audience :: Education