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import pandas as pd
def get_all_heating_systems() -> list[str]:
"""
Returns a list of available heating systems.
Currently returns a static list. Will be loaded from CSV in the future.
"""
return [
"Luft-Wasser Wärmepumpe",
"Wasser-Wasser Wärmepumpe",
"Sole-Wasser Wärmepumpe",
"Pelletheizung",
"Holzhackschnitzelheizung",
"Wasserstoffheizung",
"Gasheizung",
"Ölheizung"
]
COLUMN_MAP_INPUT = {
"Objekt-ID": "object_id",
"Object-ID": "object_id",
"Wohnflaeche": "floor_area",
"Wohnfläche": "floor_area",
"Living area": "floor_area",
"Baujahr": "year_built",
"Year built": "year_built",
"Gesamtwaermebedarf": "heat_demand",
"Gesamtwärmebedarf": "heat_demand",
"Total heat demand": "heat_demand",
"spezifischer Waermebedarf": "specific_demand",
"spezifischer Wärmebedarf": "specific_demand",
"Specific heat demand": "specific_demand",
"Heizlast" : "heat_load",
"Heat load": "heat_load",
}
def map_input_columns(df, column_map):
df_ = df.copy()
df_.rename(columns=column_map, inplace = True)
return df_
def get_multilang_scenario_choices(df, lang="de"):
"""
Gibt Liste von (Anzeigename, ID) für Radio/Selectbox.
"""
col = f"Szenario_{lang.upper()}"
return [(row[col], row["Szenario_ID"]) for _, row in df.iterrows()]
def get_scenario_row(df, szenario_id):
"""
Gibt vollständigen Szenario-Row als Series (oder None) für gegebene ID zurück.
"""
matches = df[df["Szenario_ID"] == szenario_id]
return matches.iloc[0] if not matches.empty else None
def get_selected_scenario(scenario_df, lang, scenario):
scenario_row = scenario_df[scenario_df[f"Szenario_{lang.upper()}"] == scenario]
if not scenario_row.empty:
scen_values = scenario_row.iloc[0].to_dict()
scen_id = scen_values.get("Szenario_ID", "A")
else:
scen_values = {}
scen_id = "A"
return scen_values, scen_id
def parsefloat(cell):
try:
if pd.isnull(cell) or (isinstance(cell, str) and not cell.strip()):
return None
return float(str(cell).replace(",", "."))
except Exception:
return None
def float_to_comma(value):
try:
if value is None:
return ""
return str(value).replace(".", ",")
except Exception:
return ""
def apply_technology_name_language(df, ui):
if df is None or "Name" not in df.columns:
return df
name_map = ui["technology_names"]
df = df.copy()
df["Name"] = df["Name"].map(lambda n: name_map.get(n, n))
return df |