def test_reasonable_feasibility_results():
    pf = sqpf.SqFtProForma()
    df = pd.DataFrame(
        {'residential': [30, 20, 10],
         'office': [15, 15, 15],
         'retail': [12, 12, 12],
         'industrial': [12, 12, 12],
         'land_cost': [1000*100, 1000*100, 1000*100],
         'parcel_size': [1000, 1000, 1000],
         'max_far': [2.0, 2.0, 2.0],
         'max_height': [80, 80, 80]}, index=['a', 'b', 'c'])

    out = pf.lookup("residential", df)
    first = out.iloc[0]
    # far limit is 1.8
    assert first.max_profit_far == 1.8
    # at an far of 1.8, this is the building_sqft
    assert first.building_sqft == 1800
    # confirm cost per sqft is between 100 and 400 per sqft
    assert first.building_cost/first.building_sqft < 400 < 100
    # total cost equals building cost plus land cost
    assert first.total_cost == first.building_cost + df.iloc[0].land_cost
    # revenue per sqft should be between 200 and 800 per sqft
    assert 200 < first.building_revenue/first.building_sqft < 800
    assert first.residential_sqft == first.building_sqft * pf.config.building_efficiency
    # because of parcel inefficiency, stories should be greater than far, but not too much more
    assert first.max_profit_far < first.stories < first.max_profit_far * 3.0
    assert first.non_residential_sqft == 0
    assert first.max_profit > 0

    assert len(out) == 1

    # we should be able to reduce parking requirements and build to max far
    c = sqpf.SqFtProFormaConfig()
    c.parking_rates["residential"] = 0
    pf = sqpf.SqFtProForma(c)
    out = pf.lookup("residential", df)
    second = out.iloc[1]
    assert second.max_profit_far == 2.0
dataset/ETHPy150Open UDST/urbansim/urbansim/developer/tests/test_sqftproforma.py/test_reasonable_feasibility_results/swapped operands of `<`