def test_1(self):
expected = np.array([[0., 1., 0., 0., 0.],
[0.5, 0., 0.5, 0., 0.],
[0., 0.5, 0., 0.5, 0.],
[0., 0., 0.5, 0., 0.5],
[0., 0., 0., 1., 0.]])
assert np.all(expected == lu.comp_mat_gen(self.rank, 1))
def test_5(self):
expected = np.array([
[0., 0.25, 0.25, 0.25, 0.25],
[0.25, 0., 0.25, 0.25, 0.25],
[0.25, 0.25, 0., 0.25, 0.25],
[0.25, 0.25, 0.25, 0., 0.25],
[0.25, 0.25, 0.25, 0.25, 0.]])
assert np.all(expected == lu.comp_mat_gen(self.rank, 5))
def competition(self):
""" Generate averages in competition sets """
# Generate competition variables
reac_vars = lu.reaction_spec("full")[0]
comp_vars = ["OverallRank"]
comp_vars = [reac_vars, comp_vars]
comp_vars = [el for sublist in comp_vars for el in sublist]
comp_vars_comp = [el + "_comp" for el in comp_vars]
comp_add = pd.DataFrame(np.zeros((self.data.shape[0], len(comp_vars_comp))), columns=comp_vars_comp)
self.data = self.data.join(comp_add)
for year in self.data["year"].unique():
for cvar in comp_vars:
mask = (1 - np.isnan(self.data[cvar])).astype(bool)
mdata = deepcopy(self.data[mask])
comp_mat = lu.comp_mat_gen(mdata.loc[mdata["year"] == year, "OverallRank"])
mdata.loc[mdata["year"] == year, cvar + "_comp"] = np.dot(
comp_mat, mdata.loc[mdata["year"] == year, cvar]
)
self.data[mask] = mdata
