def retrofits(retrofit):
"""make retrofits."""
obj = CSV("./", "buildings_heat_retrofit{}.csv".format(retrofit))
buildings = pd.read_csv("buildings.csv", index_col=0)
buildings = buildings.dropna()
buildings = buildings[
[st in StatGebWilhemsburg for st in buildings["statistical area"]]]
buildings['heat'] = 0
# sample_size = int(buildings.shape[0] * 0.01)
sample_size = int(buildings.shape[0] * 0.1)
print("sample size = ", sample_size)
random.seed(12345)
rows = random.sample(buildings.index.tolist(), sample_size)
buildings = buildings.ix[rows]
b_dim = buildings.shape[0]
cols = buildings.columns.tolist()
cols.append("heat")
obj.writeLine(cols)
for i in range(b_dim):
heat = get_heat(buildings.iloc[i], verbose=False, retrofit=retrofit)
print("{:>4} / {:<4} --> {:0.2f}kWh/m2a for year {} - s {}".format(
i, b_dim, heat/buildings.iloc[i]["sqm"], buildings.iloc[i]["bja"],
retrofit))
val = buildings.iloc[i].tolist()
val.append(heat)
obj.writeLine(val)
def computeHeat(buildings,
file_name="buildings_heat.csv",
file_path="./"):
"""Estimate heat demand for all building in the data frame."""
obj = CSV(file_path, file_name)
buildings = buildings[
[st in StatGebWilhemsburg for st in buildings["statistical area"]]]
b_dim = buildings.shape[0]
cols = buildings.columns.tolist()
cols.append("heat")
obj.writeLine(cols)
for i in range(b_dim):
heat = get_heat(buildings.iloc[i], verbose=False)
print("{:>4} / {:<4} --> {:0.2f}kWh/m2a for year {}".format(
i, b_dim, heat/buildings.iloc[i]["sqm"], buildings.iloc[i]["bja"]))
val = buildings.iloc[i].tolist()
val.append(heat)
obj.writeLine(val)
