Fix the programmatic route to running Temoa in README

README.md

@@ -152,29 +152,56 @@ uv run temoa tutorial             # Create tutorial files
 You can use Temoa as a Python library:
 
 ```python
-import temoa
+from logging import getLogger
 from pathlib import Path
-from temoa import TemoaModel, TemoaConfig, TemoaMode
+from temoa._internal.temoa_sequencer import TemoaSequencer
+from temoa.core.config import TemoaConfig
+from temoa.core.modes import TemoaMode
+from temoa.cli import setup_logging
+from pyomo.environ import value, check_optimal_termination
 
-# Create configuration
+output_path = Path("output_files/my_run")
+output_path.mkdir(parents=True, exist_ok=True)
+
+setup_logging(output_path, debug=False, silent=False)
+
+logger = getLogger(__name__)
+
+#  ======================================================================
+#  Choose ONE of the following two options for configuring the model run:
+#  ======================================================================
+
+# OPTION 1: build the config in code
 config = TemoaConfig(
-    scenario="my_scenario",
+    scenario="zulu",
     scenario_mode=TemoaMode.PERFECT_FORESIGHT,
-    input_database=Path("path/to/input.db"),
-    output_database=Path("path/to/output.db"),
-    output_path=Path("path/to/output"),
-    solver_name="appsi_highs"
+    time_sequencing="seasonal_timeslices",
+    input_database="tutorial_database.sqlite",
+    output_database="tutorial_database.sqlite",
+    solver_name="appsi_highs",
+    output_path=output_path,
+    silent=False,
+)
+# OPTION 2: load the config from a file
+config = TemoaConfig.build_config(
+    config_file=Path("tutorial_config.toml"),
+    output_path=output_path,
+    silent=False,
 )
 
-# Build and solve model
-model = TemoaModel(config)
-result = model.run()  # Equivalent to: temoa run tutorial_config.toml
+ts = TemoaSequencer(config=config)
+ts.start() # TemoaSequencer runs the model and handles results
+
+# Results are saved to your output_database but can also be accessed in code, here
+solved_instance = ts.pf_solved_instance # solved Temoa instance with solution values
+solver_results = ts.pf_results # information from the solver
 
-# Check if run was successful
-if result:
-    print("Model solved successfully!")
-else:
-    print("Model failed to solve")
+# Example code usage of the solved instance and solver results
+logger.info(f"Solver results: {solver_results["solver"]}")
+if check_optimal_termination(solver_results):
+    logger.info(f"Model solved! Objective value: {value(solved_instance.total_cost):.2f}")
+    built_capacity = value(solved_instance.v_new_capacity["utopia", "E51", 2010])
+    logger.info(f"Built {built_capacity:.2f} GW of storage in 2010")
 ```
 
 ## Database Setup

temoa/cli.py

@@ -31,7 +31,7 @@ def _create_output_folder() -> Path:
     return output_path
 
 
-def _setup_logging(output_path: Path, debug: bool = False, silent: bool = False) -> None:
+def setup_logging(output_path: Path, debug: bool = False, silent: bool = False) -> None:
     """Set up logging with different levels for console and file."""
     # The root logger should be set to the most verbose level required by any handler.
     # The file handler will always be more verbose than the console in silent mode.
@@ -90,7 +90,7 @@ def _setup_sequencer(
     final_output_path.mkdir(parents=True, exist_ok=True)
 
     # Pass the silent flag to the logging setup
-    _setup_logging(final_output_path, debug=debug, silent=silent)
+    setup_logging(final_output_path, debug=debug, silent=silent)
 
     config = TemoaConfig.build_config(
         config_file=config_file, output_path=final_output_path, silent=silent

temoa/tutorial_assets/config_sample.toml

@@ -87,7 +87,7 @@ solver_name = "appsi_highs"
 save_excel = true
 
 # save the duals in the output Database (may slow execution slightly?)
-save_duals = true
+save_duals = false
 
 # save storage levels by time slice (may be a large amount of data)
 save_storage_levels = true