Migrate from Flask to FastAPI (#163)

* Migrate from Flask to FastAPI

 * FastAPI migration:
    - Use pydantic model classes as input parameters to the
      data/calculation classes.
    - Interface field names changed to constructor parameter names (for
      simplicity only during transition, should be updated in a followup
      PR).
    - Add basic interface requirements (e.g. some values > 0, etc.).
 * Update tests for new data format.
 * Python requirement down to 3.9 (TypeGuard no longer needed)
 * Makefile: Add helpful targets (e.g. development server with reload)

* Move API doc from README to pydantic model classes (swagger)

 * Link to swagger.io with own openapi.yml.
 * Commit openapi.json and check with pytest for changes so the
   documentation is always up-to-date.

* Streamline docker

* FastAPI: Run startup action on dev server

 * Fix config for /strompreis, endpoint still broken however.

* test_openapi: Compare against docs/.../openapi.json

* Move fastapi to server/ submodule

 * See #187 for new repository structure.

single_test_optimization.py

@@ -5,7 +5,11 @@ import time
 import numpy as np
 
 from akkudoktoreos.class_numpy_encoder import NumpyEncoder
-from akkudoktoreos.class_optimize import optimization_problem
+from akkudoktoreos.class_optimize import (
+    OptimizationParameters,
+    OptimizeResponse,
+    optimization_problem,
+)
 from akkudoktoreos.config import get_working_dir, load_config
 from akkudoktoreos.visualize import visualisiere_ergebnisse
 
@@ -229,48 +233,52 @@ gesamtlast = [
 start_solution = None
 
 # Define parameters for the optimization problem
-parameter = {
-    # Value of energy in battery (per Wh)
-    "preis_euro_pro_wh_akku": 0e-05,
-    # Initial state of charge (SOC) of PV battery (%)
-    "pv_soc": 15,
-    # Battery capacity (in Wh)
-    "pv_akku_cap": 26400,
-    # Yearly energy consumption (in Wh)
-    "year_energy": 4100000,
-    # Feed-in tariff for exporting electricity (per Wh)
-    "einspeiseverguetung_euro_pro_wh": 7e-05,
-    # Maximum heating power (in W)
-    "max_heizleistung": 1000,
-    # Overall load on the system
-    "gesamtlast": gesamtlast,
-    # PV generation forecast (48 hours)
-    "pv_forecast": pv_forecast,
-    # Temperature forecast (48 hours)
-    "temperature_forecast": temperature_forecast,
-    # Electricity price forecast (48 hours)
-    "strompreis_euro_pro_wh": strompreis_euro_pro_wh,
-    # Minimum SOC for electric car
-    "eauto_min_soc": 50,
-    # Electric car battery capacity (Wh)
-    "eauto_cap": 60000,
-    # Charging efficiency of the electric car
-    "eauto_charge_efficiency": 0.95,
-    # Charging power of the electric car (W)
-    "eauto_charge_power": 11040,
-    # Current SOC of the electric car (%)
-    "eauto_soc": 15,
-    # Current PV power generation (W)
-    "pvpowernow": 211.137503624,
-    # Initial solution for the optimization
-    "start_solution": start_solution,
-    # Household appliance consumption (Wh)
-    "haushaltsgeraet_wh": 5000,
-    # Duration of appliance usage (hours)
-    "haushaltsgeraet_dauer": 0,
-    # Minimum Soc PV Battery
-    "min_soc_prozent": 15,
-}
+parameters = OptimizationParameters(
+    **{
+        "ems": {
+            # Value of energy in battery (per Wh)
+            "preis_euro_pro_wh_akku": 0e-05,
+            # Feed-in tariff for exporting electricity (per Wh)
+            "einspeiseverguetung_euro_pro_wh": 7e-05,
+            # Overall load on the system
+            "gesamtlast": gesamtlast,
+            # PV generation forecast (48 hours)
+            "pv_prognose_wh": pv_forecast,
+            # Electricity price forecast (48 hours)
+            "strompreis_euro_pro_wh": strompreis_euro_pro_wh,
+        },
+        "pv_akku": {
+            # Battery capacity (in Wh)
+            "kapazitaet_wh": 26400,
+            # Initial state of charge (SOC) of PV battery (%)
+            "start_soc_prozent": 15,
+            # Minimum Soc PV Battery
+            "min_soc_prozent": 15,
+        },
+        "eauto": {
+            # Minimum SOC for electric car
+            "min_soc_prozent": 50,
+            # Electric car battery capacity (Wh)
+            "kapazitaet_wh": 60000,
+            # Charging efficiency of the electric car
+            "lade_effizienz": 0.95,
+            # Charging power of the electric car (W)
+            "max_ladeleistung_w": 11040,
+            # Current SOC of the electric car (%)
+            "start_soc_prozent": 5,
+        },
+        # "spuelmaschine": {
+        #     # Household appliance consumption (Wh)
+        #     "verbrauch_wh": 5000,
+        #     # Duration of appliance usage (hours)
+        #     "dauer_h": 0,
+        # },
+        # Temperature forecast (48 hours)
+        "temperature_forecast": temperature_forecast,
+        # Initial solution for the optimization
+        "start_solution": start_solution,
+    }
+)
 
 # Startzeit nehmen
 start_time = time.time()
@@ -281,7 +289,7 @@ config = load_config(working_dir)
 opt_class = optimization_problem(config, verbose=True, fixed_seed=42)
 
 # Perform the optimisation based on the provided parameters and start hour
-ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=start_hour)
+ergebnis = opt_class.optimierung_ems(parameters=parameters, start_hour=start_hour)
 
 # Endzeit nehmen
 end_time = time.time()
@@ -298,23 +306,23 @@ ac_charge, dc_charge, discharge = (
 )
 
 visualisiere_ergebnisse(
-    gesamtlast=gesamtlast,
-    pv_forecast=pv_forecast,
-    strompreise=strompreis_euro_pro_wh,
-    ergebnisse=ergebnis["result"],
-    ac=ac_charge,
-    dc=dc_charge,
-    discharge=discharge,
-    temperature=temperature_forecast,
-    start_hour=start_hour,
+    parameters.ems.gesamtlast,
+    parameters.ems.pv_prognose_wh,
+    parameters.ems.strompreis_euro_pro_wh,
+    ergebnis["result"],
+    ac_charge,
+    dc_charge,
+    discharge,
+    parameters.temperature_forecast,
+    start_hour,
     einspeiseverguetung_euro_pro_wh=np.full(
-        config.eos.feed_in_tariff_eur_per_wh, parameter["einspeiseverguetung_euro_pro_wh"]
+        config.eos.feed_in_tariff_eur_per_wh, parameters.ems.einspeiseverguetung_euro_pro_wh
     ),
     config=config,
-    filename="visualization_results.pdf",
-    extra_data=None,
 )
 
 
 json_data = NumpyEncoder.dumps(ergebnis)
 print(json_data)
+
+OptimizeResponse(**ergebnis)