fix the function error; update docs

docs/build/html/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: c6fe713e7023aba90a76f429f2752a9a
+config: 3d3df99b1c0acfa6364caedccfe50474
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/build/html/_sources/autoapi/gym_power_res/envs/DS_pyomo/index.rst.txt

@@ -1,5 +1,5 @@
-:mod:`gym_power_res.envs.DS_pyomo`
-==================================
+:py:mod:`gym_power_res.envs.DS_pyomo`
+=====================================
 
 .. py:module:: gym_power_res.envs.DS_pyomo
 
@@ -26,92 +26,91 @@ Attributes
    gym_power_res.envs.DS_pyomo.ppc
 
 
-.. class:: SolutionDict
-
+.. py:class:: SolutionDict
 
    Bases: :py:obj:`collections.OrderedDict`
 
    Solution dictionary is an ordered dictionary that stores the optimization results
    Solution dictionary struture: D[name key]=time series data
 
-   .. method:: copy_data(self, DictData)
+   .. py:method:: copy_data(self, DictData)
 
 
-   .. method:: plot_2d(self, x_str='Time', y_str='Value', title_str='Results', figsize=(15, 7), legendlist=None, show=True, save=False, folder=None)
+   .. py:method:: plot_2d(self, x_str='Time', y_str='Value', title_str='Results', figsize=(15, 7), legendlist=None, show=True, save=False, folder=None)
 
       step plot
 
 
-   .. method:: plot_step_2d(self, x_str='Time', y_str='Value', title_str='Results', figsize=(15, 7), legendlist=None, show=True, save=False, folder=None)
+   .. py:method:: plot_step_2d(self, x_str='Time', y_str='Value', title_str='Results', figsize=(15, 7), legendlist=None, show=True, save=False, folder=None)
 
       step plot
 
 
-   .. method:: plot_bin_2d(self, x_str='Time', y_str='Value', title_str='Results', figsize=(15, 7), show=True, save=False, folder=None)
+   .. py:method:: plot_bin_2d(self, x_str='Time', y_str='Value', title_str='Results', figsize=(15, 7), show=True, save=False, folder=None)
 
       step plot
 
 
 
-.. class:: OutageManage
+.. py:class:: OutageManage
 
    Distribution system restoration class
 
-   .. attribute:: BigM
+   .. py:attribute:: BigM
       :annotation: = 100
 
 
 
-   .. attribute:: FicFlowBigM
+   .. py:attribute:: FicFlowBigM
       :annotation: = 100
 
 
 
-   .. attribute:: epsilon
+   .. py:attribute:: epsilon
       :annotation: = 0.1
 
 
 
-   .. attribute:: BasePower
+   .. py:attribute:: BasePower
       :annotation: = 1000
 
 
 
-   .. method:: data_preparation(self, ppc, line_damaged, VS=1.05, dV=0.05)
+   .. py:method:: data_preparation(self, ppc, line_damaged, VS=1.05, dV=0.05)
 
       read data of distribution network and vehicle routing problem
       Data is in the format of dictionary
 
 
-   .. method:: solve_network_restoration_varcon(self, Total_Step, Line_Initial, VarCon_Initial, Load_Initial)
+   .. py:method:: solve_network_restoration_varcon(self, Total_Step, Line_Initial, VarCon_Initial, Load_Initial)
 
       solve a full-step restoration problem with reactive power dispatch
       :param Total_Step: total step of the problem
       :param Line_Initial: initial line status
       :return: restoration plan
 
 
-   .. method:: solve_network_restoration(self, Total_Step, Line_Initial, Load_Initial)
+   .. py:method:: solve_network_restoration(self, Total_Step, Line_Initial, Load_Initial)
 
       solve a full-step restoration problem
       :param Total_Step: total step of the problem
       :param Line_Initial: initial line status
       :return: restoration plan
 
 
-   .. method:: solve_load_pickup(self, line_status, load_status=None)
+   .. py:method:: solve_load_pickup(self, line_status, load_status=None)
 
       Given fixed line status, obtain optimal load status
       -- line status should be a dict with "key" as line name and "int or array" describing status over time interval
 
 
-   .. method:: solve_load_pickup_varcon(self, line_status, varcon, load_status=None)
+   .. py:method:: solve_load_pickup_varcon(self, line_status, varcon, load_status=None)
 
       Given fixed line status and reactive power dispatch, obtain optimal load status
       -- line status should be a dict with "key" as line name and "int or array" describing status over time interval
 
 
-   .. method:: get_solution_2d(self, VariableName, NameKey, ListIndex, SolDict=None)
+   .. py:method:: get_solution_2d(self, VariableName, NameKey, ListIndex, SolDict=None)
 
       get solution and store into a one name key structured dictionary
       :param VariableName: variable name in string format
@@ -122,7 +121,7 @@ Attributes
 
 
 
-.. data:: ppc
+.. py:data:: ppc
    
 
    

docs/build/html/_sources/autoapi/gym_power_res/envs/GYM_ENV_restoration_distribution/index.rst.txt

@@ -1,5 +1,5 @@
-:mod:`gym_power_res.envs.GYM_ENV_restoration_distribution`
-==========================================================
+:py:mod:`gym_power_res.envs.GYM_ENV_restoration_distribution`
+=============================================================
 
 .. py:module:: gym_power_res.envs.GYM_ENV_restoration_distribution
 
@@ -20,19 +20,18 @@ Classes
 
 
 
-.. class:: RestorationDisEnvRL(max_disturbance, min_disturbance)
-
+.. py:class:: RestorationDisEnvRL(max_disturbance, min_disturbance)
 
    Bases: :py:obj:`gym.Env`
 
    Restoration env for StableBaseline3 (reinforcement learning algorithm collections)
 
-   .. attribute:: metadata
+   .. py:attribute:: metadata
       
 
       
 
-   .. method:: seed(self, seed=None)
+   .. py:method:: seed(self, seed=None)
 
       Sets the seed for this env's random number generator(s).
 
@@ -49,22 +48,22 @@ Classes
             this won't be true if seed=None, for example.
 
 
-   .. method:: reset(self, disturbance=None)
+   .. py:method:: reset(self, disturbance=None)
 
       Here we should setup the episode control for the total time horizon, which will be independent of individual segment simulation
 
 
-   .. method:: step(self, action)
+   .. py:method:: step(self, action)
 
       Apply the given actions to the environment for one step
 
 
-   .. method:: dict2list(self, dict)
+   .. py:method:: dict2list(self, dict)
 
       convert an order dict to a list
 
 
-   .. method:: render(self, mode='human')
+   .. py:method:: render(self, mode='human')
 
       Renders the environment.
 
@@ -103,7 +102,7 @@ Classes
                   super(MyEnv, self).render(mode=mode) # just raise an exception
 
 
-   .. method:: close(self)
+   .. py:method:: close(self)
 
       Override close in your subclass to perform any necessary cleanup.
 
@@ -112,19 +111,18 @@ Classes
 
 
 
-.. class:: RestorationDisEnv(max_disturbance, min_disturbance)
-
+.. py:class:: RestorationDisEnv(max_disturbance, min_disturbance)
 
    Bases: :py:obj:`gym.Env`
 
    Restoration env for imitation learning
 
-   .. attribute:: metadata
+   .. py:attribute:: metadata
       
 
       
 
-   .. method:: seed(self, seed=None)
+   .. py:method:: seed(self, seed=None)
 
       Sets the seed for this env's random number generator(s).
 
@@ -141,22 +139,22 @@ Classes
             this won't be true if seed=None, for example.
 
 
-   .. method:: reset(self, disturbance=None)
+   .. py:method:: reset(self, disturbance=None)
 
       Here we should setup the episode control for the total time horizon, which will be independent of individual segment simulation
 
 
-   .. method:: step(self, action)
+   .. py:method:: step(self, action)
 
       Apply the given actions to the environment for one step
 
 
-   .. method:: dict2list(self, dict)
+   .. py:method:: dict2list(self, dict)
 
       convert an order dict to a list
 
 
-   .. method:: render(self, mode='human')
+   .. py:method:: render(self, mode='human')
 
       Renders the environment.
 
@@ -195,7 +193,7 @@ Classes
                   super(MyEnv, self).render(mode=mode) # just raise an exception
 
 
-   .. method:: close(self)
+   .. py:method:: close(self)
 
       Override close in your subclass to perform any necessary cleanup.
 
@@ -204,19 +202,18 @@ Classes
 
 
 
-.. class:: RestorationDisEnv119(max_disturbance, min_disturbance)
-
+.. py:class:: RestorationDisEnv119(max_disturbance, min_disturbance)
 
    Bases: :py:obj:`gym.Env`
 
    Restoration env for imitation learning
 
-   .. attribute:: metadata
+   .. py:attribute:: metadata
       
 
       
 
-   .. method:: seed(self, seed=None)
+   .. py:method:: seed(self, seed=None)
 
       Sets the seed for this env's random number generator(s).
 
@@ -233,22 +230,22 @@ Classes
             this won't be true if seed=None, for example.
 
 
-   .. method:: reset(self, disturbance=None)
+   .. py:method:: reset(self, disturbance=None)
 
       Here we should setup the episode control for the total time horizon, which will be independent of individual segment simulation
 
 
-   .. method:: step(self, action)
+   .. py:method:: step(self, action)
 
       Apply the given actions to the environment for one step
 
 
-   .. method:: dict2list(self, dict)
+   .. py:method:: dict2list(self, dict)
 
       convert an order dict to a list
 
 
-   .. method:: render(self, mode='human')
+   .. py:method:: render(self, mode='human')
 
       Renders the environment.
 
@@ -287,7 +284,7 @@ Classes
                   super(MyEnv, self).render(mode=mode) # just raise an exception
 
 
-   .. method:: close(self)
+   .. py:method:: close(self)
 
       Override close in your subclass to perform any necessary cleanup.
 
@@ -296,19 +293,18 @@ Classes
 
 
 
-.. class:: RestorationDisVarConEnv(max_disturbance, min_disturbance)
-
+.. py:class:: RestorationDisVarConEnv(max_disturbance, min_disturbance)
 
    Bases: :py:obj:`gym.Env`
 
    Restoration env with tieline and reactive power dispatch control
 
-   .. attribute:: metadata
+   .. py:attribute:: metadata
       
 
       
 
-   .. method:: seed(self, seed=None)
+   .. py:method:: seed(self, seed=None)
 
       Sets the seed for this env's random number generator(s).
 
@@ -325,21 +321,21 @@ Classes
             this won't be true if seed=None, for example.
 
 
-   .. method:: reset(self, disturbance=None)
+   .. py:method:: reset(self, disturbance=None)
 
       Here we should setup the episode control for the total time horizon, which will be independent of individual segment simulation
 
 
-   .. method:: step(self, action, logger=None)
+   .. py:method:: step(self, action, logger=None)
 
       Apply the given actions to the environment for one step
       Action here is a dictionary with "tieline" and "varcon"
 
 
-   .. method:: view_grid(self)
+   .. py:method:: view_grid(self)
 
 
-   .. method:: render(self, mode='human')
+   .. py:method:: render(self, mode='human')
 
       Renders the environment.
 
@@ -378,7 +374,7 @@ Classes
                   super(MyEnv, self).render(mode=mode) # just raise an exception
 
 
-   .. method:: close(self)
+   .. py:method:: close(self)
 
       Override close in your subclass to perform any necessary cleanup.