def import_with_deco():
df = pd.read_csv('data/declarative_funcs.csv')
engine = declarative.Engine('2021-04-26')
engine.init_df(df)
engine.process_module('declarative_funcs')
assert 'basic' in engine.func_dict
assert hasattr(engine.func_dict['basic'], 'ignoreme')
def test_highlynested():
engine = declarative.Engine(35 * 12)
for n in range(10):
engine.initialize(module='highlynested')
d = engine.calculate()
for xs in d.values():
for x in xs:
assert x is not pd.NA
def test_highlynested_timeseries():
print('HNTS')
timesteps = 35 * 12
engine = declarative.Engine(timesteps)
for n in range(10):
engine.initialize(module='highlynested_timeseries')
d = engine.calculate()
print(engine.get_calc(50, 'f49'))
for xs in d.values():
for x in xs:
assert x is not pd.NA
assert len(d['t']) == timesteps
def test_engine_init():
engine = declarative.Engine(5)
engine.initialize(module='highlynested')
df = engine.calculate()
for xs in df.values():
for x in xs:
assert x is not pd.NA
assert len(df['t']) == 5
engine.initialize(module='highlynested')
df = engine.calculate()
for xs in df.values():
for x in xs:
assert x is not pd.NA
assert len(df['t']) == 5
def test1():
df = pd.read_csv('data/declarative_funcs.csv')
d = {}
for col in df.columns:
d[col] = list(df[col])
print(d)
df = d
run = declarative.Engine(15, input=d, module='declarative_funcs')
df = run.results
print(df)
df = run.calculate(optimization=5)
print(df)
for xs in df.values():
for x in xs:
assert x is not pd.NA
print(df)
assert len(df['t']) == 15
def test_get_no_calculate():
engine = declarative.Engine()
engine.initialize(module='highlynested')
x = engine.get_calc(0, 'f49')
assert x is not pd.NA