Merge branch 'main' into pandas_wrapper

assume/scenario/loader_amiris.py

@@ -155,7 +155,7 @@ def add_agent_to_world(
     match agent["Type"]:
         case "SupportPolicy":
             support_data = agent["Attributes"]["SetSupportData"]
-            supports |= {x.pop("Set"): x for x in support_data}
+            supports |= {x.pop("PolicySet"): x for x in support_data}
             world.add_unit_operator(agent["Id"])
 
             for name, support in supports.items():
@@ -480,14 +480,12 @@ def load_amiris(
     start += timedelta(minutes=2)
     end += timedelta(minutes=2)
     sim_id = f"{scenario}_{study_case}"
-    save_interval = amiris_scenario["GeneralProperties"]["Output"]["Interval"]
     prices = {}
     index = pd.date_range(start=start, end=end, freq="1h", inclusive="left")
     world.bidding_strategies["support"] = SupportStrategy
     world.setup(
         start=start,
         end=end,
-        save_frequency_hours=save_interval,
         simulation_id=sim_id,
     )
     # helper dict to map trader markups/markdowns to powerplants

docs/source/release_notes.rst

@@ -13,6 +13,9 @@ Upcoming Release
   The features in this section are not released yet, but will be part of the next release! To use the features already you have to install the main branch,
   e.g. ``pip install git+https://github.com/assume-framework/assume``
 
+  **Bugfixes:**
+  - **Tutorial 07**: Aligned Amiris loader with changes in format in Amiris compare (https://gitlab.com/fame-framework/fame-io/-/issues/203 and https://gitlab.com/fame-framework/fame-io/-/issues/208)
+
 v0.4.3 - (11th November 2024)
 ===========================================
 

examples/inputs/example_02a/config.yaml

@@ -2,136 +2,124 @@
 #
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
-tiny:
-  start_date: 2019-01-01 00:00
-  end_date: 2019-01-05 00:00
-  time_step: 1h
-  save_frequency_hours: null
-  learning_mode: True
-
+base:
+  end_date: 2019-03-31 00:00
   learning_config:
-    continue_learning: False
-    trained_policies_save_path: null
-    max_bid_price: 100
     algorithm: matd3
-    actor_architecture: mlp
-    learning_rate: 0.001
-    training_episodes: 10
-    episodes_collecting_initial_experience: 3
-    train_freq: 24h
-    gradient_steps: -1
-    batch_size: 64
-    gamma: 0.99
+    batch_size: 256
+    continue_learning: false
     device: cpu
-    noise_sigma: 0.1
-    noise_scale: 1
+    episodes_collecting_initial_experience: 5
+    gamma: 0.99
+    gradient_steps: -1
+    learning_rate: 0.001
+    max_bid_price: 100
     noise_dt: 1
+    noise_scale: 1
+    noise_sigma: 0.1
+    train_freq: 24h
+    trained_policies_save_path: null
+    training_episodes: 100
     validation_episodes_interval: 5
-
+  learning_mode: true
   markets_config:
     EOM:
+      market_mechanism: pay_as_clear
+      maximum_bid_price: 3000
+      maximum_bid_volume: 100000
+      minimum_bid_price: -500
+      opening_duration: 1h
+      opening_frequency: 1h
       operator: EOM_operator
+      price_unit: EUR/MWh
       product_type: energy
       products:
-        - duration: 1h
-          count: 1
-          first_delivery: 1h
-      opening_frequency: 1h
-      opening_duration: 1h
+      - count: 1
+        duration: 1h
+        first_delivery: 1h
       volume_unit: MWh
-      maximum_bid_volume: 100000
-      maximum_bid_price: 3000
-      minimum_bid_price: -500
-      price_unit: EUR/MWh
-      market_mechanism: pay_as_clear
-
-
-base:
+  save_frequency_hours: null
   start_date: 2019-03-01 00:00
-  end_date: 2019-03-31 00:00
   time_step: 1h
-  save_frequency_hours: null
-  learning_mode: True
-
+base_lstm:
+  end_date: 2019-03-31 00:00
   learning_config:
-    continue_learning: False
-    trained_policies_save_path: null
-    max_bid_price: 100
+    actor_architecture: lstm
     algorithm: matd3
-    actor_architecture: mlp
-    learning_rate: 0.001
-    training_episodes: 50
-    episodes_collecting_initial_experience: 5
-    train_freq: 24h
-    gradient_steps: -1
     batch_size: 256
-    gamma: 0.99
+    continue_learning: false
     device: cpu
-    noise_sigma: 0.1
-    noise_scale: 1
-    noise_dt: 1
-    validation_episodes_interval: 5
     early_stopping_steps: 10
     early_stopping_threshold: 0.05
-
+    episodes_collecting_initial_experience: 5
+    gamma: 0.99
+    gradient_steps: -1
+    learning_rate: 0.001
+    max_bid_price: 100
+    noise_dt: 1
+    noise_scale: 1
+    noise_sigma: 0.1
+    train_freq: 24h
+    trained_policies_save_path: null
+    training_episodes: 50
+    validation_episodes_interval: 5
+  learning_mode: true
   markets_config:
     EOM:
+      market_mechanism: pay_as_clear
+      maximum_bid_price: 3000
+      maximum_bid_volume: 100000
+      minimum_bid_price: -500
+      opening_duration: 1h
+      opening_frequency: 1h
       operator: EOM_operator
+      price_unit: EUR/MWh
       product_type: energy
       products:
-        - duration: 1h
-          count: 1
-          first_delivery: 1h
-      opening_frequency: 1h
-      opening_duration: 1h
+      - count: 1
+        duration: 1h
+        first_delivery: 1h
       volume_unit: MWh
-      maximum_bid_volume: 100000
-      maximum_bid_price: 3000
-      minimum_bid_price: -500
-      price_unit: EUR/MWh
-      market_mechanism: pay_as_clear
-
-base_lstm:
+  save_frequency_hours: null
   start_date: 2019-03-01 00:00
-  end_date: 2019-03-31 00:00
   time_step: 1h
-  save_frequency_hours: null
-  learning_mode: True
-
+tiny:
+  end_date: 2019-01-05 00:00
   learning_config:
-    continue_learning: False
-    trained_policies_save_path: null
-    max_bid_price: 100
+    actor_architecture: mlp
     algorithm: matd3
-    actor_architecture: lstm
-    learning_rate: 0.001
-    training_episodes: 50
-    episodes_collecting_initial_experience: 5
-    train_freq: 24h
-    gradient_steps: -1
-    batch_size: 256
-    gamma: 0.99
+    batch_size: 64
+    continue_learning: false
     device: cpu
-    noise_sigma: 0.1
-    noise_scale: 1
+    episodes_collecting_initial_experience: 3
+    gamma: 0.99
+    gradient_steps: -1
+    learning_rate: 0.001
+    max_bid_price: 100
     noise_dt: 1
+    noise_scale: 1
+    noise_sigma: 0.1
+    train_freq: 24h
+    trained_policies_save_path: null
+    training_episodes: 10
     validation_episodes_interval: 5
-    early_stopping_steps: 10
-    early_stopping_threshold: 0.05
-
+  learning_mode: true
   markets_config:
     EOM:
+      market_mechanism: pay_as_clear
+      maximum_bid_price: 3000
+      maximum_bid_volume: 100000
+      minimum_bid_price: -500
+      opening_duration: 1h
+      opening_frequency: 1h
       operator: EOM_operator
+      price_unit: EUR/MWh
       product_type: energy
       products:
-        - duration: 1h
-          count: 1
-          first_delivery: 1h
-      opening_frequency: 1h
-      opening_duration: 1h
+      - count: 1
+        duration: 1h
+        first_delivery: 1h
       volume_unit: MWh
-      maximum_bid_volume: 100000
-      maximum_bid_price: 3000
-      minimum_bid_price: -500
-      price_unit: EUR/MWh
-      market_mechanism: pay_as_clear
+  save_frequency_hours: null
+  start_date: 2019-01-01 00:00
+  time_step: 1h