def npv_weighted(x, first_year, last_year, w1, w2, w1_function, w2_function,
r):
if min(x.index) > first_year or max(x.index) < last_year:
return np.nan
x[first_year] = pyam.fill_series(x, first_year)
x[last_year] = pyam.fill_series(x, last_year)
years = [
i for i in x.index
if i >= first_year and i <= last_year and ~np.isnan(x[i])
]
years.sort()
# loop over years
if not np.isnan(x[first_year]) and not np.isnan(x[last_year]):
value = 0
for (i, yr) in enumerate(years[:-1]):
next_yr = years[i + 1]
dt = next_yr - yr
if dt not in w1.keys():
w1[dt] = w1_function(r, dt)
if dt not in w2.keys():
w2[dt] = w2_function(r, dt)
# the summation is shifted to include the first year fully in sum,
# otherwise, would return a weighted average of `yr` and `next_yr`
value += w1[dt] * x[yr] + w2[dt] * x[next_yr]
# the loop above does not include the last element in range `last_year`,
# therefore added explicitly
value += x[last_year]
return value / (last_year - first_year + 1)
def test_fill_series_datetime():
# note that the series is not order and the index is defined as float
y = pd.Series(
data=[3, 1],
index=[datetime.datetime(2001, 1, 1), datetime.datetime(2003, 1, 1)]
)
assert fill_series(y, datetime.datetime(2002, 1, 1)) == 2.
def test_fill_series_out_of_range():
y = pd.Series(data=[np.nan, 1, 3, 1],
index=[2002.0, 2005.0, 2007.0, 2013.0])
assert fill_series(y, 2001) is np.nan
def test_fill_series():
# note that the series is not order and the index is defined as float
y = pd.Series(data=[np.nan, 1, 4, 1],
index=[2002.0, 2008.0, 2005.0, 2013.0])
assert fill_series(y, 2006) == 3.0
