def test_item1_dataframe(item_tmp_dir):
# As a pd.DataFrame
data = load_model_data(1, skip_cache=True)
assert len(data) == item1_size
# As a dict() of xr.DataArray
data = load_model_data(1, fmt=xr.DataArray)
size = sum([d.notnull().sum() for d in data.values()])
assert size == 875589 # Omits intensity_new
# As a xr.Dataset
data = load_model_data(1, fmt=xr.Dataset)
assert data.notnull().sum() == 875589 # Omits intensity_new
def plot_all_item1():
"""Produce all plots for the iTEM1 database."""
from plotnine import aes, facet_grid, geom_bar, labs
from item.model import load_model_data, squash_scenarios
from item.model.dimensions import ALL, PAX
class pass_energy_use_mode(Plot):
"""Passenger energy use by mode.
This reproduces a figure from the (private) item2-scripts respository.
"""
variable = "energy"
selectors = dict(region="Global",
mode=PAX,
tech=ALL,
fuel=ALL,
year=[2015, 2030, 2050])
terms = [
aes("year", "value / 1000", fill="mode"),
geom_bar(stat="identity"),
facet_grid(["scenario", "model"]),
labs(x="Year", y="EJ/year"),
]
df = load_model_data(1)
df = squash_scenarios(df, 1)
pass_energy_use_mode(df, 1).save()
def test_invalid_version():
# Load an invalid model database version
with pytest.raises(ValueError):
load_model_data(99)
def test_item2_xr(item_tmp_dir):
# As a dict() of xr.DataArray
data = load_model_data(2, fmt=xr.DataArray)
size = sum([d.notnull().sum() for d in data.values()])
assert size == item2_size
def test_item2(item_tmp_dir):
# As a pd.DataFrame
data = load_model_data(2, skip_cache=True)
assert len(data) == item2_size
def item1_data(item_tmp_dir):
yield load_model_data(1)