def test_get_interpolated_spline_extrapolated_linear_function():
# If there is only one unique x, expect default_slope
f = get_interpolated_spline_extrapolated_linear_function(
[(100, 100)], default_slope=5)
assert np.isclose(f(102), 110)
# If x is inside the interpolated domain, y should match original
# If x is on the left, y should extend the trend from the leftmost point
# If x is on the right, y should extend the trend from the rightmost point
f = get_interpolated_spline_extrapolated_linear_function(
[(100, 1), (300, 2), (500, 5)])
assert np.isclose(f(300), 2)
assert np.isclose(f(99), 0.99)
assert np.isclose(f(501), 5.01)
assert np.allclose(f([300, 99, 501]), [2, 0.99, 5.01]) # Get ys from xs
def test_get_interpolated_spline_extrapolated_linear_function():
# If there is only one unique x, expect default_slope
f = get_interpolated_spline_extrapolated_linear_function([(100, 100)],
default_slope=5)
assert np.isclose(f(102), 110)
# If x is inside the interpolated domain, y should match original
# If x is on the left, y should extend the trend from the leftmost point
# If x is on the right, y should extend the trend from the rightmost point
f = get_interpolated_spline_extrapolated_linear_function([(100, 1),
(300, 2),
(500, 5)])
assert np.isclose(f(300), 2)
assert np.isclose(f(99), 0.99)
assert np.isclose(f(501), 5.01)
assert np.allclose(f([300, 99, 501]), [2, 0.99, 5.01]) # Get ys from xs
def estimate_population(target_year, country_name):
t = POPULATION_BY_YEAR_BY_COUNTRY_TABLE
country_t = _get_country_table(t, "Country or Area", country_name)
try:
earliest_estimated_year = min(country_t[country_t["Variant"] == "Low variant"]["Year(s)"])
except ValueError:
raise InvalidData("missing population")
# Get actual population for each year
year_packs = country_t[country_t["Year(s)"] < earliest_estimated_year][["Year(s)", "Value"]].values
# Estimate population for the given year
estimate_population = get_interpolated_spline_extrapolated_linear_function(year_packs)
return estimate_population(target_year)
def estimate_population(target_year, country_name):
t = POPULATION_BY_YEAR_BY_COUNTRY_TABLE
country_t = _get_country_table(t, 'Country or Area', country_name)
try:
earliest_estimated_year = min(
country_t[country_t['Variant'] == 'Low variant']['Year(s)'])
except ValueError:
raise EmptyDataset('Missing population')
# Get actual population for each year
year_packs = country_t[country_t['Year(s)'] < earliest_estimated_year][[
'Year(s)', 'Value'
]].values
# Estimate population for the given year
estimate_population = get_interpolated_spline_extrapolated_linear_function(
year_packs)
return estimate_population(target_year)
def estimate_electricity_consumption_per_capita(target_year, country_name):
t = ELECTRICITY_CONSUMPTION_PER_CAPITA_BY_YEAR_TABLE
country_t = _get_country_table(t, "Country Name", country_name)
if not len(country_t):
raise InvalidData("missing country_name")
year_packs = []
for column_name in country_t.columns:
try:
year = int(column_name)
except ValueError:
continue
value = country_t[column_name].values[0]
if isnull(value):
continue
year_packs.append((year, value))
if not year_packs:
raise InvalidData("missing year_value")
estimate_electricity_consumption_per_capita = get_interpolated_spline_extrapolated_linear_function(year_packs)
return estimate_electricity_consumption_per_capita(target_year)
def estimate_electricity_consumption_per_capita(target_year, country_name):
t = ELECTRICITY_CONSUMPTION_PER_CAPITA_BY_YEAR_TABLE
country_t = _get_country_table(t, 'Country Name', country_name)
if not len(country_t):
raise InvalidData('missing country_name')
year_packs = []
for column_name in country_t.columns:
try:
year = int(column_name)
except ValueError:
continue
value = country_t[column_name].values[0]
if isnull(value):
continue
year_packs.append((year, value))
if not year_packs:
raise InvalidData('missing year_value')
estimate_electricity_consumption_per_capita = \
get_interpolated_spline_extrapolated_linear_function(year_packs)
return estimate_electricity_consumption_per_capita(target_year)
