from statkraft.ltm.io.run_repository import RunRepository from statkraft.ltm.scripting import plot_ts, plot_percentiles import matplotlib.pyplot as plt rr = RunRepository() labels = ["operational", "daily", "samtap sk"] run_id = sorted(rr.search(labels=labels))[-1] # Pick newest run = rr.recreate(run_id=run_id) no1 = run.model.market.areas["NO1"] # shortcut variable st1 = no1.power_price mean = st1.mean() plot_percentiles(no1.power_price.percentiles()) plt.figure() plot_ts(no1.power_price.mean()) plt.show() m = st1.mean print(m)
from statkraft.ltm.io.run_repository import RunRepository
import shyft.api as sa
from statkraft.ltm.state import quantity
from statkraft.ltm.scripting import plot_ts
import matplotlib.pyplot as plt
from statkraft.ltm.io.converter import to_pandas
rr = RunRepository()
t0 = sa.utctime_now() - sa.Calendar.DAY * 2
res = rr.search(labels=["operational", "norway"], created_from=t0)
areas = ["NO1", "NO2", "NO5"]
tsv = quantity(sa.TsVector(), "GWh")
tot_cons_list = []
legend_list = []
for key in res.keys():
run_info = res[key]
run = rr.recreate(run_id=key)
legend_list.append(key)
time_axis = sa.TimeAxis(sa.utctime_now(), sa.Calendar.DAY, 365)
tot_cons = quantity(sa.TsVector(), "GWh")
for key1 in run.model.market.areas.keys():
this_area = run.model.market.areas[key1]
if key1 in areas:
cons = this_area.consumption.mean(unit="GWh", time_axis=time_axis)
tot_cons += cons
tot_cons_list.append(tot_cons)
diff_cons = tot_cons_list[0] - tot_cons_list[1]
tsv.extend(diff_cons.magnitude)